FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See Section 10 and Rule 13)
Title of invention:
REAL-TIME MONITORING AND CONTROLLING OF INDUSTRIAL INSTRUMENTS USING ASSET ADMINISTRATION SHELL
Applicant
Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India
Preamble to the description:
The following specification particularly describes the invention and the manner in which it is to be performed.
2
TECHNICAL FIELD
[001]
The disclosure herein generally relates to industrial automation, and, more particularly, to real-time monitoring and controlling of industrial instruments using asset administration shell.
5
BACKGROUND
[002]
The Fourth Industrial Revolution, or Industry 4.0, marks a significant shift towards the digitization of manufacturing and industrial processes, emphasizing cyber-physical systems, the Internet of Things (IoT), and cloud computing. One key concept arising from this paradigm shift is the digital twin, a 10 dynamic digital replica of a physical system that serves as a bridge between the physical and digital worlds. In the midst of this transformative landscape, the Asset Administration Shell (AAS), an Industry 4.0 component, is gaining attention as a standardized, virtual representation of assets. By providing a unified digital model for assets in the industrial process, AAS enhances interoperability, promoting 15 seamless communication between heterogeneous systems and devices. A notable challenge within this context is the integration and real-time control of complex physical systems such as controllers within the AAS industry 4.0 framework.
SUMMARY 20
[003]
Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems.
[004]
For example, in one aspect, there is provided a processor implemented method for real-time monitoring and controlling of industrial 25 instruments using asset administration shell. The method comprises integrating an industrial instrument with an Asset Administration Shell (AAS), wherein the industrial instrument is positioned at a distance ‘d’ from an ultrasonic distance sensor; initializing an Open Platform Communications Unified Architecture (OPC UA) server based on one or more parameters associated with the industrial 30 instrument; controlling and measuring the one or more parameters of the industrial
3
instrument by using the OPC UA server, wherein the one or more parameters
measured serve as one or more OPC UA nodes; synchronizing the OPC UA server with the AAS using a data bridge; mapping the one or more OPC UA nodes to one or more sub models of the AAS; and performing tuning of the one or more parameters based on the mapping of the one or more OPC UA nodes to the one or 5 more sub models.
[005]
In an embodiment, an arrangement of the industrial instrument and the ultrasonic distance sensor represents a real-world physical asset under control.
[006]
In an embodiment, the step of synchronizing the OPC UA server with the AAS enables seamless control and monitoring of the industrial instrument. 10
[007]
In an embodiment, a feedback mechanism is enabled for a control system to monitor and measure a position of the industrial instrument in real-time.
[008]
In an embodiment, the real time monitoring and measuring of the position of the industrial instrument enables the control system to (i) adapt to one or more associated conditions and (ii) instantaneously respond to one or more actual 15 physical parameter changes.
[009]
In an embodiment, the one or more parameters comprise at least a setpoint, a proportional gain, an integral gain, and a derivative gain of the industrial instrument.
[010]
In another aspect, there is provided a processor implemented system 20 for real-time monitoring and controlling of industrial instruments using asset administration shell. The system comprises: a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to: integrate an industrial 25 instrument with an Asset Administration Shell (AAS), wherein the industrial instrument is positioned at a distance ‘d’ from an ultrasonic distance sensor; initialize an Open Platform Communications Unified Architecture (OPC UA) server based on one or more parameters associated with the industrial instrument; control and measure the one or more parameters of the industrial instrument by 30 using the OPC UA server, wherein the one or more parameters measured serve as
4
one or more OPC UA nodes; synchronize the OPC UA server with the AAS using
a data bridge; map the one or more OPC UA nodes to one or more sub models of the AAS; and perform tuning of the one or more parameters based on the mapping of the one or more OPC UA nodes to the one or more sub models.
[011]
In an embodiment, an arrangement of the industrial instrument and 5 the ultrasonic distance sensor represents a real-world physical asset under control.
[012]
In an embodiment, the OPC UA server is synchronized with the AAS to enable seamless control and monitoring of the industrial instrument.
[013]
In an embodiment, a feedback mechanism is enabled for a control system to monitor and measure a position of the industrial instrument in real-time. 10
[014]
In an embodiment, the real time monitoring and measuring of the position of the industrial instrument enables the control system to (i) adapt to one or more associated conditions and (ii) instantaneously respond to one or more actual physical parameter changes.
[015]
In an embodiment, the one or more parameters comprise at least a 15 setpoint, a proportional gain, an integral gain, and a derivative gain of the industrial instrument.
[016]
In yet another aspect, there are provided one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause a 20 method for real-time monitoring and controlling of industrial instruments using asset administration shell by integrating an industrial instrument with an Asset Administration Shell (AAS), wherein the industrial instrument is positioned at a distance ‘d’ from an ultrasonic distance sensor; initializing an Open Platform Communications Unified Architecture (OPC UA) server based on one or more 25 parameters associated with the industrial instrument; controlling and measuring the one or more parameters of the industrial instrument by using the OPC UA server, wherein the one or more parameters measured serve as one or more OPC UA nodes; synchronizing the OPC UA server with the AAS using a data bridge; mapping the one or more OPC UA nodes to one or more sub models of the AAS; and performing 30
5
tuning of the one or more parameters based on the mapping of the one or more OPC
UA nodes to the one or more sub models.
[017]
In an embodiment, an arrangement of the industrial instrument and the ultrasonic distance sensor represents a real-world physical asset under control.
[018]
In an embodiment, the step of synchronizing the OPC UA server 5 with the AAS enables seamless control and monitoring of the industrial instrument.
[019]
In an embodiment, a feedback mechanism is enabled for a control system to monitor and measure a position of the industrial instrument in real-time.
[020]
In an embodiment, the real time monitoring and measuring of the position of the industrial instrument enables the control system to (i) adapt to one 10 or more associated conditions and (ii) instantaneously respond to one or more actual physical parameter changes.
[021]
In an embodiment, the one or more parameters comprise at least a setpoint, a proportional gain, an integral gain, and a derivative gain of the industrial instrument. 15
[022]
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS 20
[023]
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
[024]
FIG. 1 depicts an exemplary system for real-time monitoring and controlling of industrial instruments using asset administration shell (AAS), in 25 accordance with an embodiment of the present disclosure.
[025]
FIG. 2 depicts an exemplary high level block diagram of the system of FIG. 1 for real-time monitoring and controlling of industrial instruments using the AAS, in accordance with an embodiment of the present disclosure.
[026]
FIG. 3 depicts an exemplary flow chart illustrating a method for real-30 time monitoring and controlling of industrial instruments using the asset
6
administration shell
(AAS), and the systems of FIG. 1-2, in accordance with an embodiment of the present disclosure.
[027]
FIG. 4 depicts a block diagram illustrating synchronizing Open Platform Communications Unified Architecture (OPC UA) server with the AAS using a data bridge, in accordance with an embodiment of the present disclosure. 5
[028]
FIG. 5 depicts a block diagram illustrating a closed loop system for motion control, in accordance with an embodiment of the present disclosure.
[029]
FIG. 6 depicts a graphical representation illustrating a response of a Proportional – Integral – Derivative (PID)-controlled linear actuator to a step input, characterized by damped oscillations, in accordance with an embodiment of the 10 present disclosure.
[030]
FIG. 7 depicts a graphical representation illustrating performance graph of a PID-controlled linear actuator under a step input, with the system exhibiting continuous oscillations, in accordance with an embodiment of the present disclosure. 15
[031]
FIG. 8 depicts a graphical representation illustrating a response of a PID-controlled linear actuator to a step input that achieves a steady, and settled state, in accordance with an embodiment of the present disclosure.
[032]
FIG. 9 depicts a graphical representation illustrating a settled response of the PID-controlled linear actuator to a series of random step inputs, 20 with the control parameters retained from the previous steady-settled response (Kp=0.05, Ki=0.001, Kd=0.0), in accordance with an embodiment of the present disclosure.
[033]
FIG. 10 depicts a graphical representation illustrating the PID-controlled linear actuator responding to a sinusoidal input while maintaining the 25 previous steady-settled control parameters (Kp=0.05, Ki=0.001, Kd=0.0), in accordance with an embodiment of the present disclosure.
DETAILED DESCRIPTION OF EMBODIMENTS
[034]
Exemplary embodiments are described with reference to the 30 accompanying drawings. In the figures, the left-most digit(s) of a reference number
7
identifies the figure in which the reference number first appears.
Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments. 5
[035]
As mentioned earlier, Industry 4.0 marks a significant shift towards the digitization of manufacturing and industrial processes, emphasizing cyber-physical systems, the Internet of Things (IoT), and cloud computing. One key concept arising from this paradigm shift is the digital twin, a dynamic digital replica of a physical system that serves as a bridge between the physical and digital worlds. 10 In the midst of this transformative landscape, the Asset Administration Shell (AAS), an Industry 4.0 component, is gaining attention as a standardized, virtual representation of assets. By providing a unified digital model for assets in the industrial process, AAS enhances interoperability, promoting seamless communication between heterogeneous systems and devices. A notable challenge 15 within this context is the integration and real-time control of complex physical systems such as controllers within the AAS industry 4.0 framework. PID controllers are ubiquitous in industrial control systems, and their successful integration into AAS can set a precedent for other similar systems.
[036]
In the present disclosure, systems and methods are provided for 20 integrating and controlling industrial instruments (such as linear actuators) in real-time using the AAS. This involves the development/initializing of an OPC Unified Architecture (OPC UA) server implemented in Python, providing an interface for the Eclipse BaSyx data bridge module to interact with the control and measurement parameters of the actuator. The OPC UA, an open standard communication protocol 25 in industrial automation, facilitates interoperable and secure communication, making it an ideal choice for this application. The Eclipse BaSyx, on the other hand, is an open-source implementation of the AAS, providing a robust framework for realizing the AAS in industrial applications. The integration of these technologies allows for the mapping of the control parameters and measured distances as OPC 30 UA nodes in the AAS, thereby creating a comprehensive and real-time digital twin
8
of the controlled linear actuator. The resulting system demonstrates the potential of
AAS in handling real-time control data.
[037]
Referring now to the drawings, and more particularly to FIGS. 1 through 10, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments, and 5 these embodiments are described in the context of the following exemplary system and/or method.
[038]
FIG. 1 depicts an exemplary system 100 for real-time monitoring and controlling of industrial instruments using an asset administration shell (AAS), in accordance with an embodiment of the present disclosure. In an embodiment, the 10 system 100 includes one or more hardware processors 104, communication interface device(s) or input/output (I/O) interface(s) 106 (also referred as interface(s)), and one or more data storage devices or memory 102 operatively coupled to the one or more hardware processors 104. The one or more processors 104 may be one or more software processing components and/or hardware 15 processors. In an embodiment, the hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) is/are configured to fetch and execute computer-20 readable instructions stored in the memory. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices (e.g., smartphones, tablet phones, mobile communication devices, and the like), workstations, mainframe computers, servers, a network cloud, and the like. 25
[039]
The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an 30
9
embodiment, the I/O interface device(s) can include one or more ports for
connecting a number of devices to one another or to another server.
[040]
The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic-random access memory (DRAM), and/or 5 non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, a database 108 is comprised in the memory 102, wherein the database 108 comprises information pertaining to various industrial instruments that need monitoring and measuring of associated parameters. The database 108 further 10 comprises information to various nodes of sub models of an asset administration shell (AAS), and the like. The memory 102 further comprises (or may further comprise) information pertaining to input(s)/output(s) of each step performed by the systems and methods of the present disclosure. In other words, input(s) fed at each step and output(s) generated at each step are comprised in the memory 102 15 and can be utilized in further processing and analysis.
[041]
FIG. 2, with reference to FIG. 1, depicts an exemplary high level block diagram of the system 100 of FIG. 1 for real-time monitoring and controlling of industrial instruments using the AAS, in accordance with an embodiment of the present disclosure. 20
[042]
FIG. 3, with reference to FIGS. 1-2, depicts an exemplary flow chart illustrating a method for real-time monitoring and controlling of industrial instruments using the AAS, and the systems 100 of FIG. 1-2, in accordance with an embodiment of the present disclosure. In an embodiment, the system(s) 100 comprises one or more data storage devices or the memory 102 operatively coupled 25 to the one or more hardware processors 104 and is configured to store instructions for execution of steps of the method by the one or more processors 104. The steps of the method of the present disclosure will now be explained with reference to components of the system 100 of FIG. 1, the block diagram of the system 100 depicted in FIG. 2, and the flow diagram as depicted in FIG. 3. Although process 30 steps, method steps, techniques or the like may be described in a sequential order,
10
such processes, methods, and techniques may be configured to work in alternate
orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously. 5
[043]
At step 202 of the method of the present disclosure, the one or more hardware processors 104 integrate an industrial instrument with an Asset Administration Shell (AAS). The industrial instrument is positioned at a distance ‘d’ from an ultrasonic distance sensor. This arrangement of the industrial instrument and the ultrasonic distance sensor represents a real-world physical asset under 10 control. In an embodiment, the industrial instrument is a RPi4-based PID controller. The expression ‘industrial instrument’ refers to any device or system used in industrial applications that is subject to automated control to perform specific functions or tasks. This encompasses a wide range of machinery and equipment which may include actuators, sensors, motors, valves, pumps, other mechanical, 15 electrical, electronic devices, and the like. It is to be understood by a person having ordinary skill in the art or person skilled in the art that the above examples of industrial instruments shall not be construed as limiting the scope of the present disclosure. These instruments are typically controlled through the use of various control mechanisms, such as Proportional-Integral-Derivative (PID) controllers or 20 other advanced control algorithms, to achieve desired performance criteria such as precision, speed, force, temperature, pressure, other operational parameters, and the like. Such operation of controlling is often facilitated by integrating the industrial instrument with digital systems like the Asset Administration Shell (AAS) within Industry 4.0 frameworks, allowing for real-time data exchange, monitoring, and 25 adjustment. The term implies the capability of these instruments to be integrated into cyber-physical systems, enabling interaction with and adaptability to the dynamic industrial environment.
[044]
At step 204 of the method of the present disclosure, the one or more hardware processors 104 initialize an Open Platform Communications Unified 30 Architecture (OPC UA) server based on one or more parameters associated with
11
the industrial instrument. In the present disclosure, the integration of the RPi4
-based PID controller is with the AAS using the Eclipse BaSyx data bridge and the OPC UA server. The OPC UA server encapsulates essential control and measurement parameters such as setpoint, one or more gains, and the measured distance into OPC UA nodes. 5
[045]
Below is an illustrative pseudo code for initializing/configuring the OPC UA server, by way of example:
// Pseudocode for the given Python script
Import necessary modules
// Setup GPIO pins 10
Initialize GPIO mode and warnings
Configure GPIO pins for output
Set initial GPIO output states
// Initialize sensor pins
Define trigger_pin and echo_pin 15
Configure trigger_pin for output
Configure echo_pin for input
// Initialize PID parameters in shared memory
Create shared memory for Kp, Ki, Kd, and setpoint
Initialize a shared memory value for distance and a stop flag 20
Set a raw distance value for calibration
// Setup OPC UA server
Create an OPC UA server instance
Set server name and endpoint
Configure server security policies 25
Register a namespace for the server
// Create OPC UA variables for parameters
Add a parameter object to the server
Create and add variables for setpoint, Kp, Ki, Kd, and distance
Make the nodes writable by clients 30
// Function to measure distance
12
Function measure_distance():
Send a high pulse to trigger_pin
Wait briefly
Send a low pulse to trigger_pin
Record the start and end times of the echo signal 5
Calculate distance based on pulse duration and speed of sound
Store distance in shared memory
// Function to control the actuator
Function control_actuator(output):
Activate appropriate GPIO output based on the control signal 10
Sleep proportionally to the output signal's magnitude
Deactivate GPIO outputs
// Multiprocessing function for distance measurement
Function distance_process():
Loop until stop flag is set 15
Call measure_distance()
Sleep for a short interval
// Multiprocessing function for PID control
Function control_process():
Create a PID controller with shared memory parameters 20
Set PID controller output limits
Loop until stop flag is set
Update PID controller with current parameters
Compute control signal using current distance
Call control_actuator with the control signal 25
Sleep for a short interval
// Main execution block
If script is the main module:
Start the OPC UA server
Start distance and control processes 30
Try to loop indefinitely:
13
Update OPC UA server values with current shared memory values
Sleep for a short interval
If KeyboardInterrupt occurs:
Set stop flag to True
Join the processes 5
Finally, on script termination:
Stop the OPC UA server
Deactivate GPIO outputs and clean up
[046]
The above pseudo code is better understood by way of following description: 10
[047]
The provided Python (pseudo) code is designed to run on a Raspberry Pi and uses a controller (e.g., PID (Proportional-Integral-Derivative) controller) to manage the industrial instrument such as an actuator based on distance measurements. The distance is measured using an ultrasonic sensor connected to the Raspberry Pi’s GPIO pins. The code includes the setup of the Raspberry Pi 15 GPIO pins for controlling the industrial instrument and reading the sensor values.
[048]
Additionally, the code sets up an OPC UA (Open Platform Communications Unified Architecture) server, which is used for industrial automation purposes. This server allows for monitoring and adjusting parameters of the industrial instrument (e.g., say PID parameters such as the setpoint, and PID 20 constants (Kp, Ki, Kd), and the like) remotely.
[049]
The code operates in two main processes: one for measuring distance using the ultrasonic sensor (distance_process) and another for controlling the industrial instrument (e.g., the actuator) based on the PID controller’s output (control_process). These processes are set to run in parallel using Python’s 25 multiprocessing capabilities.
The measure_distance function sends a pulse to the ultrasonic sensor and measures the time taken for the echo to return, calculating the distance from this time. The control_actuator function then activates the actuators for a duration based on the PID controller’s output to maintain the distance at the setpoint. 30
14
[050]
The main execution block of the code is responsible for starting the OPC UA server, initiating the parallel processes, and regularly updating the parameters based on the OPC UA server’s data. If the program is interrupted by the user (e.g., say through a keyboard interruption), it safely shuts down the processes, stops the OPC UA server, deactivates the GPIO outputs, and cleans up the GPIO 5 settings before exiting.
[051]
Referring to steps of FIG. 3, at step 206 of the method of the present disclosure, the one or more hardware processors 104 control and measure the one or more parameters of the industrial instrument by using the OPC UA server. The one or more parameters measured serve as one or more OPC UA nodes, in an 10 example embodiment of the present disclosure.
[052]
For instance, if the industrial instrument is PID Controller (Proportional-Integral-Derivative Controller), then the associated parameters that are controlled and measured include, but are not limited to, Setpoint, Proportional Gain (Kp), Integral Gain (Ki), Derivative Gain (Kd), and the like. Similarly, if the 15 industrial instrument is On-Off Control Logic, then the associated parameters that are controlled and measured include, but are not limited to, Setpoint, Threshold, and the like.
[053]
If the industrial instrument is Feedforward Control, then the associated parameters that are controlled and measured include, but are not limited 20 to, Setpoint, Disturbance Variables, and the like. If the industrial instrument is Fuzzy Logic Controller, then the associated parameters that are controlled and measured include, but are not limited to, Fuzzy Rules: Sets of if-then statements that describe how to handle different situations, Membership Functions: Functions that define how each point in the input space is mapped to a membership value 25 between 0 and 1, and the like.
[054]
If the industrial instrument is PLC (Programmable Logic Controller) Based Control, then the associated parameters that are controlled and measured include, but are not limited to, Input Variables, Logic Program, Output Signals, and the like. If the industrial instrument is State-Space Control, then the associated 30 parameters that are controlled and measured include, but are not limited to, State
15
Variables, Control Variables, State Equations,
and the like. If the industrial instrument is Model Predictive Control (MPC), then the associated parameters that are controlled and measured include, but are not limited to, Prediction Model, Control Horizon, Objective Function, and the like.
[055]
At step 208 of the method of the present disclosure, the one or more 5 hardware processors 104 synchronize the OPC UA server with the AAS using a data bridge. By synchronizing the OPC UA server with the AAS, the system 100 is enabled for seamless controlling and monitoring of the industrial instrument. Further, a feedback mechanism is enabled for a control system to monitor and measure a position of the industrial instrument in real-time. The real time 10 monitoring and measuring of the position of the industrial instrument enables the control system to (i) adapt to one or more associated conditions and (ii) instantaneously respond to one or more actual physical parameter changes. The physical parameter changes, include, but are not limited to, Temperature, Pressure, Flow Rate, Vibration, Humidity, Position, Velocity/Speed, Electrical Parameters, 15 Torque, Chemical Composition, Acoustic Signals, Light Intensity, Load/Force, pH Levels, Particle Count/Size, etc. Below is an illustrative pseudo code for synchronizing the OPC UA server with the AAS using the data bridge, by way of example:
1.
Procedure handle POST request: 20
2.
Read POST data from request
3.
Parse data into JSON object
4.
Define mapping of idShort values to OPC nodes
5.
Connect to OPC UA server at specific URL
6.
For each 'inputArgument' in the data: 25
a.
Extract 'idShort' and 'value'
b.
If 'idShort' is in the mapping:
i.
Get the OPC node corresponding to 'idShort'
ii.
Set the value of the node in the OPC UA server to 'value'
c.
Else: 30
i.
Print error message
16
7.
Disconnect from OPC UA server
8.
Send HTTP 200 response
[056]
FIG. 4, with reference to FIGS. 1 through 3, depicts a block diagram illustrating synchronizing the OPC UA server with the AAS using the data bridge, in accordance with an embodiment of the present disclosure. In FIG. 4, the block 5 depicted in broken and dotted line property refers to an asset under control (Industrial instruments). The OPC UA server is a Python server, in one example embodiment of the present disclosure. This Python server would be a separate entity that interacts with the Data Bridge and the OPC UA server independently, acting as a mediator or an endpoint for other external systems that want to communicate 10 using HTTP POST requests.
[057]
The bidirectional interaction between the Asset Administration Shell (AAS) submodel and the physical asset is a crucial aspect in Industry 4.0. One way to achieve this interaction is by implementing a server-client architecture, such as the OPC UA, a widely accepted industrial communication protocol as shown in 15 FIG. 4. This architecture is represented in the system 100 through a Python script which is shown in the pseudocode, which acts as the bridge between the AAS submodel and the physical asset.
[058]
The script employs a built-in HTTP server from Python's `http.server` library, which listens for POST requests from the invocation qualifier 20 of eclipse Basyx with JSON payloads. Each payload is expected to be an array of objects. Each object includes an array of 'inputArguments', which encapsulates the desired 'idShort' (the identifier for a variable in the AAS submodel) and its corresponding value. Four 'idShorts' are specifically catered for in the system: 'setpoint', 'kp', 'ki', and 'kd'. These variables are used as an example to showcase the 25 communication process between the AAS and the asset, where each 'idShort' is linked to a specific node in the OPC UA server.
[059]
The Python script begins by launching an HTTP server on port 8082. Upon receiving a POST request, the server retrieves the JSON payload and iterates through each 'inputArguments' object. It extracts the 'idShort' and the 'value' for 30 each argument and, using a pre-defined mapping dictionary, identifies the
17
corresponding OPC UA node for the 'idShort'. It then connects to the OPC UA
server at a specified endpoint URL and assigns the extracted 'value' to the corresponding OPC UA node. This essentially mirrors the value of the 'idShort' in the AAS submodel onto the physical asset represented in the OPC UA server. In case the 'idShort' does not match any of the pre-defined keys ('setpoint', 'kp', 'ki', 5 'kd'), the script notifies that no OPC node mapping was found for the 'idShort' and proceeds to the next 'inputArguments' object. After all values are set in the OPC UA server, the script disconnects from the server and sends a 200 HTTP response back to the client, signifying a successful transaction.
[060]
At step 210 of the method of the present disclosure, the one or more 10 hardware processors 104 map the one or more OPC UA nodes to one or more sub models of the AAS. AAS_PID of the AAS primarily consists of a single Submodel titled ‘Parameters’ (not shown in FIGS.). This Submodel is designed to hold the key parameters of the control system, capturing the real-time state of the linear actuator. The Submodel is further comprised of a collection of Submodel Elements, 15 each representing a specific parameter of the control system. The 'Distance' parameter is represented as a Property Submodel Element within the 'Parameters' Submodel. This parameter reflects the real-time measurement of the gap 'd' between the linear actuator and the ultrasonic distance sensor, acting as the process variable for the controller. The value of the 'Distance' parameter is continuously updated, 20 reflecting the dynamic operation of our physical asset.
[061]
Four Operation Submodel Elements are also included within the 'Parameters' Submodel, representing the 'Setpoint', 'Kp', 'Ki', and 'Kd' values of the controller. These parameters are pivotal in governing the behavior of the control system. The 'Setpoint' specifies the desired position for the linear actuator, while 25 'Kp', 'Ki', and 'Kd' determine the proportional, integral, and derivative gains of the controller, respectively. Through the AAS-PID Tuning GUI, these Operation Submodel Elements can be accessed and modified, allowing the operator to adjust the control parameters in real-time. This feature ensures the controller can adapt to varying operational demands, emphasizing the flexibility and adaptability offered 30 by the AAS.
18
[062]
In essence, the AAS_PID provides a real-time digital twin of the controlled linear actuator. It encapsulates the crucial control parameters into a standardized structure, facilitating seamless integration into the wider Industry 4.0 framework. By linking the physical asset to its digital twin, the system 100 ensures a constant stream of data exchange, thus enabling advanced control strategies and 5 performance optimization.
[063]
The above step 210 is better understood by way of following description.
[064]
The Python code provided sets up a Hypertext Transfer Protocol (HTTP) server that listens for POST requests and interacts with the OPC UA server 10 to facilitate two-way communication as part of an Asset Administration Shell (AAS) Data Bridge module. Here is a summary of its functionality:
1.
HTTP Server Initialization:
a.
It uses the http.server module (not shown FIGS.) to create an HTTP server. 15
b.
The server is configured to listen on all interfaces ('') at port 8082.
2.
Request Handler:
a.
The server has a request handler class RequestHandler that inherits from BaseHTTPRequestHandler.
b.
This handler only defines a do_POST method, meaning it only 20 responds to POST requests.
3.
Processing POST Requests:
a.
The handler reads the incoming POST data and expects it to be in JSON format.
b.
It parses the JSON data to work with it internally. 25
4.
OPC UA Client Setup:
a.
The code creates a client for connecting to an OPC UA server at the specified endpoint URL (opc.tcp://192.168.1.38:4840(Raspberry Pi IP)).
b.
Once a POST request is received, it connects to the OPC UA server. 30
5.
Data Mapping and Updating:
19
a.
A mapping is defined (id_short_value_mapping) that correlates identifiers from the AAS (like “setpoint”, “kp”, “ki”, “kd”) to corresponding OPC UA node identifiers.
b.
The server expects the JSON data to contain either a single ‘value’ entry or a set of ‘inputArguments’. 5
c.
For a single ‘value’, it interprets this as a ‘setpoint’ and updates the corresponding node on the OPC UA server.
d.
For ‘inputArguments’, it iterates over the arguments, maps the ‘idShort’ to the correct OPC UA node, and updates the node with the new ‘value’. 10
6.
Response:
a.
After processing the POST data, the server sends an HTTP 200 OK response to the client.
7.
Server Execution:
a.
After defining the server and request handler, the code starts the 15 server and runs indefinitely, waiting for incoming POST requests.
[065]
At step 212 of the method of the present disclosure, the one or more hardware processors 104 perform tuning of the one or more parameters based on the mapping of the one or more OPC UA nodes to the one or more sub models. The one or more parameters comprise at least a setpoint, a proportional gain, an integral 20 gain, and a derivative gain of the industrial instrument, in one example embodiment. In the present disclosure, the system 100 utilizes a graphical user interface (GUI) of the AAS for performing parameter tuning. The above step of 212 is better understood by way of following description:
[066]
The AAS setup enables the interaction and management of digital 25 representations of physical assets (i.e., the “digital twins”) using various microservices. Here’s a summary of each service:
1.
registrydb:
a.
This service runs an AAS Registry which is based on the Eclipse BaSyx project. 30
b.
It exposes port 4099 for communication.
20
2.
AASserverdb:
a.
This service runs the AAS Server, also based on Eclipse BaSyx, which hosts the digital twin models.
b.
It binds port 4500 to the host.
c.
An AASX package file for the controller’s digital representation is 5 specified as a data source.
3.
databridge:
a.
This service uses the Eclipse BaSyx Data Bridge image to enable data exchange between different systems and the AAS layer.
4.
GUI: 10
a.
This service runs a web-based GUI for the AAS environment.
b.
It makes the GUI available on port 4088 of the host machine
[067]
The Eclipse BaSyx data bridge facilitates the transformation of these nodes into sub models within the AAS, effectively generating a comprehensive, real-time digital twin of the physical asset in the AAS server. The AAS server 15 interfaces with an AAS-parameter tuning GUI, enabling the system 100 to adjust parameters in real-time while monitoring the system's response. The GUI provides a user-friendly environment for operators to tune the control system and ensure optimal performance of the linear actuator based on the real-time data.
[068]
To further capitalize on the real-time data generated by the setup, a 20 data pipeline from the AAS server to Node-RED, InfluxDB, and finally Grafana is established by the method and system 100 of the present disclosure. Node-RED serves as the data router, taking data from the AAS server and routing it into the InfluxDB, a time-series database designed for handling high write and query loads. This stored data can then be retrieved and visualized using Grafana, an open-source 25 platform for visualization and analytics.
[069]
FIG. 5, with reference to FIGS. 1 through 4, depicts a block diagram illustrating a closed loop system for motion control, in accordance with an embodiment of the present disclosure. More specifically, FIG. 5 provides a visual representation of the operation of the PID control system in correlation with the 30 physical components involved. The desired displacement for the linear actuator,
21
also known as the 'setpoint,' serves as the input to the system. This setpoint is fed
into the summer where it is compared with the feedback from the ultrasonic distance sensor (e.g., HC-SR04). The HC-SR04 sensor measures the actual displacement (also referred as actual motion and may be interchangeably used herein) of the linear actuator in real-time, providing crucial feedback to the control system. 5
[070]
The output of the summer is then processed by the PID controller, a key component in the control strategy of the method of the present disclosure. The PID controller calculates an 'error' value based on the difference between the setpoint and the feedback, then utilizes a PID algorithm to compute a control signal. This algorithm considers the present error (proportional term), the integral of past 10 errors (integral term), and the prediction of future errors (derivative term) to create an optimized control signal.
[071]
This control signal is subsequently sent to the DC motor driver, a 3A Dual Channel Motor Driver (MDD3A). This motor driver is responsible for driving the 6V linear actuator, translating the control signal into actual motion. The 15 motor driver is powered by an external 6V DC desktop power supply, ensuring stable operation of the linear actuator.
[072]
In the present disclosure, the PID parameters ('Kp', 'Ki', and 'Kd') were tuned using the trial and error method. Initially, random values were chosen, resulting in a damped oscillation graph as FIG. 6. FIG. 6, with reference to FIGS. 20 1 through 5, depicts a graphical representation illustrating a response of a Proportional – Integral – Derivative (PID)-controlled linear actuator to a step input, characterized by damped oscillations, in accordance with an embodiment of the present disclosure. The graphical representation of FIG. 6 depicts three lines representing the desired motion, the actual motion of the linear actuator, and a 25 filtered signal, which is a representation of the actuator’s response after applying a smoothing algorithm to reduce noise and improve clarity of the performance data.
[073]
The control parameters are indicated on FIG. 6 as Kp=0.01, Ki=0.01, and Kd=-0.001. These parameters are tuned for the PID controller to manage the behavior of the linear actuator. The presence of damped oscillations 30 suggests that the system is stable, but the controller might be slightly underdamped
22
due to the visible oscillations around the setpoint before settling down. From FIG.
6, it is observed that the actual motion initially overshoots the desired motion significantly before stabilizing and tracking the desired motion closely.
[074]
The values were adjusted iteratively to produce a steady oscillation graph as shown in FIG. 7 and finally fine-tuned to achieve a steady-settled graph as 5 shown in FIG. 8 for the step input. This indicates that the system has reached a state where the actual displacement closely follows the setpoint, highlighting the effectiveness of our PID tuning.
[075]
FIG. 7, with reference to FIGS. 1 through 6, depicts a graphical representation illustrating performance graph of a PID-controlled linear actuator 10 under a step input, with the system exhibiting continuous oscillations, in accordance with an embodiment of the present disclosure. The graph of FIG. 7 plots three lines: the ‘Desired Motion’ with PID control parameters of Kp=0.05 and Ki=0.5 (with Kd set to 0.0), the ‘Actual Motion’ of the actuator which displays the real-time response, and the ‘Filtered’ signal which is a processed version of the actual motion 15 intended to reduce noise and allow for a clearer analysis of the trend. The substantial Ki (integral gain) in comparison to the Kp (proportional gain) and the absence of a Kd (derivative gain) contribute to the continuous oscillation observed in the actual motion. The high integral gain may be causing the actuator to accumulate a significant error over time, which results in the overshoots and the oscillatory 20 behavior as the system attempts to correct itself. This behavior indicates that while the actuator is trying to follow the setpoint, the lack of a derivative component and possibly an excessively high integral component prevents the system from settling to a steady state. The continuous oscillation can be problematic for control systems that require precision and stability, by the adjustments to the PID parameters, such 25 as reducing the integral gain or introducing a derivative component, might be needed to dampen the oscillations and achieve a more stable response.
[076]
FIG. 8, with reference to FIGS. 1 through 7, depicts a graphical representation illustrating a response of a PID-controlled linear actuator to a step input that achieves a steady, and settled state, in accordance with an embodiment 30 of the present disclosure. The graphical representation of FIG. 8 includes three
23
traces: the ‘Desired Motion’ with PID parameters set to Kp=0.05, Ki=0.001, and
Kd=0.0; the ‘Actual Motion,’ which represents the actuator’s performance under these parameters; and a ‘Filtered’ signal, which is a smoothed version of the actual motion, providing a cleaner representation of the system’s behavior over time.
[077]
This set of PID parameters indicates a moderate proportional gain 5 (Kp), a very low integral gain (Ki), and no derivative gain (Kd). The proportional gain is responsible for a proportional response to the error between the desired and actual positions, while the minimal integral gain implies that the error over time is not significantly contributing to the actuator’s correctional response.
[078]
The actual motion quickly rises to match the step change in the 10 desired motion and demonstrates minimal overshoot and undershoot, indicating that the chosen PID parameters provide a good balance between responsiveness and stability for this system (e.g., the industrial instrument/linear actuator). After the initial response, the system (e.g., the industrial instrument/linear actuator) closely follows the desired motion. 15
[079]
FIG. 9, with reference to FIGS. 1 through 8, depicts a graphical representation illustrating a settled response of the PID-controlled linear actuator to a series of random step inputs, with the control parameters retained from the previous steady-settled response (Kp=0.05, Ki=0.001, Kd=0.0), in accordance with an embodiment of the present disclosure. The graphical representation plots 20 ‘Desired Motion’, ‘Actual Motion’, and a ‘Filtered’ signal, which is a smoothed version of the actual motion.
[080]
Despite the randomness of the step inputs, the actual motion of the actuator demonstrates a settled response, closely following the desired motion with minimal delay and overshoot, indicating that the PID controller is effectively 25 compensating for the disturbances introduced by the random step changes. The low integral gain (Ki) is preventing integral wind-up, and the absence of derivative gain (Kd) suggests that the system is not overly sensitive to the rate of change of the error, which could be beneficial in preventing the amplification of noise.
24
[081]
Moreover, the actual motion is well-aligned with the desired motion, only showing slight deviations after each step change before returning to the desired path.
[082]
FIG. 10, with reference to FIGS. 1 through 9, depicts a graphical representation illustrating the PID-controlled linear actuator responding to a 5 sinusoidal input while maintaining the previous steady-settled control parameters (Kp=0.05, Ki=0.001, Kd=0.0), in accordance with an embodiment of the present disclosure. The graphical representation of FIG. 10 features the ‘Desired Motion’, which is sinusoidal, the ‘Actual Motion’ of the actuator, and a ‘Filtered’ line.
[083]
The actual motion follows the sinusoidal desired motion with a 10 degree of fidelity, exhibiting a settled response despite the varying nature of the input. The moderate proportional gain (Kp) enables the actuator to respond proportionally to the error, while the very low integral gain (Ki) minimizes the risk of integral wind-up, which could lead to overshoot or instability in response to the continually changing sinusoidal input. 15
[084]
With no derivative gain (Kd), the controller does not directly address the rate of change of the error, which could be beneficial in this scenario, as it might otherwise react to the sinusoidal input’s frequent changes, potentially causing instability. The filtered signal provides a smoothed representation of the actual motion, showing that while there are minor deviations and a slight phase lag 20 between the actual and desired motions, the overall system performance indicates a well-tuned controller that can handle dynamic inputs with a consistent and reliable output.
[085]
The integration of real-time control systems into Industry 4.0 frameworks represents a crucial step forward in the evolution of automated and 25 smart industries. The present disclosure demonstrated the method of FIG. 3 and the systems 100 of FIGS. 1 and 2 to integrate a real-time controlled linear actuator into the Asset Administration Shell (AAS) using OPC UA server and the Eclipse BaSyx Data Bridge. The method of the present disclosure was implemented on a Raspberry Pi and validated with a real-world experimental setup. The results showed (e.g., 30 refer graphical representation of FIGS. 6 through 10) that this approach provides
25
real
-time control of the system with efficient performance and reliable communication.
[086]
The integration of the method allows for seamless and efficient control of a PID-controlled system, thereby enhancing the realization of the AAS concept in Industry 4.0. The use of OPC UA server ensures interoperability and 5 standardized communication protocols, which are essential attributes in the Industry 4.0 paradigm. The successful implementation and demonstration of this method provide a strong foundation for future applications that aim to integrate real-time control systems into the AAS.
[087]
The written description describes the subject matter herein to enable 10 any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent 15 elements with insubstantial differences from the literal language of the claims.
[088]
It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a 20 server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g., any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g., hardware means like e.g., an application-specific integrated circuit (ASIC), a field-programmable 25 gate array (FPGA), or a combination of hardware and software means, e.g., an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include 30
26
software means. Alternatively, the embodiments may be implemented on different
hardware devices, e.g., using a plurality of CPUs.
[089]
The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by 5 various components described herein may be implemented in other components or combinations of other components. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. 10
[090]
The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily 15 defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such 20 alternatives fall within the scope of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be 25 noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
[091]
Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which 30 information or data readable by a processor may be stored. Thus, a computer-
27
readable storage medium may store instructions for execution by one or more
processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include 5 random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
[092]
It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the 10 following claims.
We Claim:
1. A processor implemented method, comprising:
integrating, via one or more hardware processors, an industrial instrument with an Asset Administration Shell (AAS) (202), wherein the industrial instrument is positioned at a distance 'd' from an ultrasonic distance sensor;
initializing, via the one or more hardware processors, an Open Platform Communications Unified Architecture (OPC UA) server based on one or more parameters associated with the industrial instrument (204);
controlling and measuring, via the one or more hardware processors, the one or more parameters of the industrial instrument by using the OPC UA server (206), wherein the one or more parameters measured serve as one or more OPC UA nodes;
synchronizing, via the one or more hardware processors, the OPC UA server with the AAS using a data bridge (208);
mapping, via the one or more hardware processors, the one or more OPC UA nodes to one or more sub models of the AAS (210); and
performing tuning, via the one or more hardware processors, of the one or more parameters based on the mapping of the one or more OPC UA nodes to the one or more sub models (212).
2. The processor implemented method as claimed in claim 1, wherein an arrangement of the industrial instrument and the ultrasonic distance sensor represents a real-world physical asset under control.
3. The processor implemented method as claimed in claim 1, wherein the step of synchronizing the OPC UA server with the AAS enables seamless control and monitoring of the industrial instrument.
4. The processor implemented method as claimed in claim 1, wherein a feedback mechanism is enabled for a control system to monitor and measure a position of the industrial instrument in real-time.

5. The processor implemented method as claimed in claim 4, wherein the real time monitoring and measuring of the position of the industrial instrument enables the control system to (i) adapt to one or more associated conditions and (ii) instantaneously respond to one or more actual physical changes.
6. The processor implemented method as claimed in claim 1, wherein the one or more parameters comprise at least a setpoint, a proportional gain, an integral gain, and a derivative gain of the industrial instrument.
7. A system (100), comprising:
a memory (102) storing instructions;
one or more communication interfaces (106); and
one or more hardware processors (104) coupled to the memory (102) via the one or more communication interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to:
integrate an industrial instrument with an Asset Administration Shell (AAS), wherein the industrial instrument is positioned at a distance 'd' from an ultrasonic distance sensor;
initialize an Open Platform Communications Unified Architecture (OPC UA) server based on one or more parameters associated with the industrial instrument;
control and measure the one or more parameters of the industrial instrument by using the OPC UA server, wherein the one or more parameters measured serve as one or more OPC UA nodes;
synchronize the OPC UA server with the AAS using a data bridge;
map the one or more OPC UA nodes to one or more sub models of the AAS; and
perform tuning of the one or more parameters based on the mapping of the one or more OPC UA nodes to the one or more sub models.

8. The system as claimed in claim 7, wherein an arrangement of the industrial instrument and the ultrasonic distance sensor represents a real-world physical asset under control.
9. The system as claimed in claim 7, wherein the step of synchronizing the OPC UA server with the AAS enables seamless control and monitoring of the industrial instrument.
10. The system as claimed in claim 7, wherein a feedback mechanism is enabled for a control system to monitor and measure a position of the industrial instrument in real-time.
11. The system as claimed in claim 10, wherein the real time monitoring and measuring of the position of the industrial instrument enables the control system to (i) adapt to one or more associated conditions and (ii) instantaneously respond to one or more actual physical changes.
12. The system as claimed in claim 7, wherein the one or more parameters comprise at least a setpoint, a proportional gain, an integral gain, and a derivative gain of the industrial instrument.