Description:Complete Specification:
The following specification describes and ascertains the nature of this invention and the manner in which it is to be performed.

Field of the invention:
[0001] The present invention relates to controller and method to estimate a steering torque for a steering system.

Background of the invention:
[0002] In a steering system along with other sensors, torque sensor plays a vital role. The torque sensor value is one of the primary inputs for the automotive steering system. The steering system is greatly affected when there is some error in the torque sensor. Upon failure of the torque sensor vehicle comes to a static position suddenly which may lead to an emergency. Failure of other sensors like rotor position sensor, hall sensor, there is certain duration upto which the vehicle can move, and emergency can be avoided. For e.g., in the case of rotor position sensor failure there is a duration of 60s, in rotor position sensor path failure there is a duration of 60s, these additional duration helps to avoid emergency. However, when it comes to the failure of torque sensor, there is a duration of 0s. Hence, there is a strong motivation to come up with some solution which will provide some additional duration once the torque sensor fails.

[0003] According to a prior art KR20140139816, a virtual torque calculating method to control steering, an electronic control unit, and a steering apparatus in the case of failure of a torque sensor. The electronic control unit according to the present invention comprises: an information obtaining unit which obtains an angle of a motor shaft about the motor shaft of a motor supplying an auxiliary steering force through a motor location sensor and obtains a steering relative angle through the torque sensor; and a virtual torque calculating unit which calculates a virtual torque based on a calculated worm axis torsion angle.

[0004] Brief description of the accompanying drawings:
[0005] An embodiment of the disclosure is described with reference to the following accompanying drawings,
[0006] Fig. 1 illustrates a block diagram of a controller to estimate a steering torque for a steering system of a vehicle, according to an embodiment of the present invention;
[0007] Fig. 2 illustrates a flow diagram of a method for estimating a steering torque for a steering system of a vehicle, according to the present invention;
[0008] Fig .3 illustrates a graph illustrating a relation between a steering assist and the time for which there is a steering assist, according to the present invention.

Detailed description of the embodiments:
[0009] Fig. 1 illustrates a block diagram of the controller 100 to estimate the steering torque 140 for the steering system of the vehicle, according to an embodiment of the present invention. The conventional steering system of the vehicle comprises a steering column fixed with a steering wheel, coupled to a steering shaft which is coupled to a steering rack through a pinion. The steering rack is coupled to a set of wheels of the vehicle. The steering column receives the input of the driver from the steering wheel and subsequently transmitted to the wheel. The steering system is also possible to be a Steer-by-Wire (SBW) system as well without any physical connection between the steering column and the steering rack. A torque sensor 120 is positioned on the steering column of the vehicle to determine a steering torque 140 upon rotation of the steering wheel. The controller 100 detects an error/failure in the torque sensor 120 if there is a fault or malfunction. For estimating the steering torque 140 when there is error in the torque sensor 120, the controller 100 receives input signal from a steering system parameter 130 from a respective means 160 within the vehicle and processes the received steering system parameters 130 through a trained regression-based model 110 and then estimates the steering torque 140 based on the processing. The controller 100 is applicable any one of the front wheel steering system, rear wheel steering system and all-wheel drive vehicles. The complete structure of the steering system is not explained as it is state of the art and the present invention is specifically for the controller 100 of the steering system of the vehicle.

[0010] According to the embodiment of the present invention, the steering system parameter 130 are selected from a group comprising a steering angle, a steering angle speed, a rack force, a yaw rate, a longitudinal acceleration, a vehicle speed, a last applied motor toque, a rotor position, and the like. The respective means 160 from which the controller 100 receives input signals for the steering system parameters 130 are selected from a group comprising a steering angle sensor, a steering speed sensor, a rack force sensor, a yaw rate sensor, a longitudinal acceleration sensor, a lateral acceleration sensor, a vehicle speed sensor, a motor torque sensing means, a rotor position sensor, and the like. The motor torque is calculated as a amount of current that motor draws from a battery in the vehicle. These sensors are installed/ present in the vehicle through which the controller 100 receives input for processing to estimate the required torque.

[0011] According to the embodiment of the present invention, the trained regression-based model 110 processes the inputs received by the controller 100 from the group of sensors. The regression-based model 110 is a pretrained model, which is trained using parameters comprising the steering angle, the steering angle speed, the rack force, the yaw rate, the longitudinal acceleration, the vehicle speed, the last applied motor toque, the rotor position, and the like. The torque is estimated using the pretrained regression-based model 110 and the estimated steering torque 140 is substituted for a measured valued of the torque sensor 120 which is detected to be failed.

[0012] According to the embodiment of the present invention, while the torque sensor 120 is functioning, the controller 100 compares the estimated steering torque 140 with the torque from the torque sensor 120 and continuously updates the trained regression-based model 110.

[0013] According to an embodiment of the present invention, the controller 100 estimates the steering torque 140 for the steering system of the vehicle without using the torque sensor 120. The controller receives the input signal from the steering system parameter 130 from respective means 160 within the vehicle and processes the received input signals through the trained regression-based model 110 and the required steering torque 140 is estimated. The steering system parameters 130 are selected from the group comprising the steering angle, the steering angle speed, the rack force, the yaw rate, the longitudinal acceleration, the vehicle speed, the last applied motor toque, the rotor position, and the like. The respective means 160 from which the controller 100 receives input signals for the steering system parameters 130 are selected from the group comprising the steering angle sensor, the steering speed sensor, the rack force sensor, the yaw rate sensor, the longitudinal acceleration sensor, the lateral acceleration sensor, the vehicle speed sensor, the motor torque sensor, the rotor position sensor, and the like. These sensors are installed/ present in the vehicle through which the controller 100 receives input for processing to estimate the required torque. The regression-based model 110 is the pretrained model, which is trained using parameters comprising the steering angle, the steering angle speed, the rack force, the yaw rate, the longitudinal acceleration, the vehicle speed, the last applied motor toque, the rotor position, and the like. This regression-based model during run time updates itself continuously when the there is no fault in the torque sensor 120. The controller 100 as per the present invention also used as the virtual torque sensor.

[0014] According to the embodiment of the present invention, the controller 100 is at least one chosen from a group of devices comprising a smartphone, a computer and, a cloud. The controller 100 is the one which comprises input interface, output interfaces having pins or ports, the memory element such as Random Access Memory (RAM) and/or Read Only Memory (ROM), Analog-to-Digital Converter (ADC) and a Digital-to-Analog Convertor (DAC), clocks, timers, counters and at least one processor (capable of implementing machine learning) connected with each other and to other components through communication bus channels. The memory element (not shown) is pre-stored with logics or instructions or programs or applications or modules/models and/or threshold values/ranges, reference values, predefined/predetermined criteria/conditions, lists, knowledge sources which is/are accessed by the at least one processor as per the defined routines. The internal components of the controller 100 are not explained for being state of the art, and the same must not be understood in a limiting manner. The controller 100 may also comprise communication units such as transceivers to communicate through wireless or wired means such as Global System for Mobile Communications (GSM), 3G, 4G, 5G, Wi-Fi, Bluetooth, Ethernet, serial networks, and the like. The controller 100 is implementable in the form of System-in-Package (SiP) or System-on-Chip (SOC) or any other known types. Examples of controller 100 comprises but not limited to, microcontroller, microprocessor, microcomputer, Electronic Control Units (ECUs), etc.

[0015] According to the embodiment of the present invention, the working of the controller 100 is envisaged. The controller 100 estimates the steering torque 140 for the steering system using the trained regression-based model 110. The torque sensor 120 is positioned on the steering column of the vehicle. The controller 100 determines the steering torque 140 during any error in the torque sensor 120. There are groups of sensors present in the vehicle for e.g., the steering angle sensor, the steering speed sensor, the rack force sensor, the yaw rate sensor, the longitudinal acceleration sensor, the lateral acceleration sensor, the vehicle speed sensor, the motor torque sensing means, the rotor position sensor and the like. The controller 100 receives input signal from the steering system parameter 130 from respective means 160. The signals for the steering system parameter 130 are received and processed through the trained regression-based model 110 and the required steering torque 140 is estimated. The steering torque 140 is estimated during the failure of the torque sensor 120 which is substituted for the measured value of the torque sensor 120. With this substituted value of the steering torque 140, emergency occurred due to the sensor failure is taken care of.

[0016] Figure 2 illustrates a flow diagram of a method for estimating a steering torque 140 for the steering system of the vehicle, according to the present invention. The method comprises plurality of steps of which a step 202 comprises determining an error, by the controller 100, in the torque sensor 120. A step of 204 comprises receiving, by the controller 100, input signals from the steering system parameters 130 from respective means 160 within the vehicle. A step of 206 comprises processing, by the controller 100, the input parameters through the trained regression-based model 110 and then estimating the steering torque 140.

[0017] According to the method, in the step 206, the regression-based model 110 is pretrained using parameters comprising the steering angle, the steering angle speed, the rack force, the yaw rate, the longitudinal acceleration, the vehicle speed, the last applied motor torque and the rotor position and the like. The training of the model 110 using these parameters are continuous process. The steering torque 140 is estimated in the event of the failure of the torque sensor 120, and it is substituted for the measured value of the torque sensor 120.

[0018] Figure 3 illustrates a graph illustrating a relation between the steering assist and the time for which there is an assist due to the substitution of the estimated steering torque 140, according to the present invention. There are four points in the graph i.e. A, B, C and D. Usually in the failure of torque sensor 120, the graph falls from A to D instantaneously and there is no assistance to the vehicle. However, due to the substitution of the estimated steering torque 140, during the failure of the torque sensor 120, the graph falls from point A to B, then it is flat for few seconds from point B to C and finally from point C to D, which describes the temporary assistance or emergency assistance for parking or limp aside.

[0019] According to the present invention, the controller 100 and method to estimate the steering torque 140 for the steering system of the vehicle. When failure occurs in the torque sensor 120, the controller 100 substitutes the estimated steering torque 140 and provide temporary assistance or emergency assistance for parking or limp aside. By substituting the estimated steering torque 140 during the failure of the torque sensor 120, issue of plausibility is taken care of i.e., during the failure of physical sensor the vehicle doesn’t come to halt instantaneously. The estimated steering torque 140 is used for plausibility check of the sensor value when there is no fault. During error in the torque sensor 120, the estimated steering torque 140 is used to provide the assistance. There is a possibility of using the estimated steering torque 140 for a brief period of torque sensor 120 unavailability. The availability of estimated steering torque 140 is an option as the secondary sensor in addition to the torque sensor 120. Due to the availability of estimated steering torque 140, there is no complete failure in the system. The controller 100 keeps calculating the torque values while the system is functioning and compares the predicted/estimated steering torque 140 with the torque sensor 120 value and reduces the error by continuous correction and learning. The continuous error correction will help in increasing the efficiency of the estimated data. The controller 100 as per the present invention also used as the virtual torque sensor. The availability of the estimated steering torque 140 helps the customers to driver the vehicle to a garage or side of the road during an occurrence of the torque sensor 120 failure.

[0020] It should be understood that the embodiments explained in the description above are only illustrative and do not limit the scope of this invention. Many such embodiments and other modifications and changes in the embodiment explained in the description are envisaged. The scope of the invention is only limited by the scope of the claims.
, Claims:We claim:
1. A controller (100) to estimate a steering torque (140) for a steering system of a vehicle, said steering system comprises a torque sensor (120) positioned on a steering column to determine said steering torque (140), said controller (100) comprises the steps of:
determining an error in said torque sensor (120), characterized by
receive an input signal from a steering system parameter (130) from respective means (160) within said vehicle; and
process said input signals through a pretrained regression-based model (110) and estimate said steering torque (140);

2. The controller (100) as claimed in claim 1, wherein said steering system parameters (130) are selected from a group comprising steering angle, a steering angle speed, a rack force, a yaw rate, a longitudinal acceleration, a vehicle speed, a last applied motor torque and a rotor position and said respective means (160) to receive said steering system parameters (130) are selected from a group comprising a steering angle sensor, a steering speed sensor, a rack force sensor, a yaw rate sensor, a longitudinal acceleration sensor, a lateral acceleration sensor, a vehicle speed sensor, a motor torque sensing means and a rotor position sensor, respectively.

3. The controller (100) as claimed in claim 1, wherein said trained regression-based model (110) is pretrained using parameters comprising a steering angle, a steering angle speed, a rack force, a yaw rate, a longitudinal acceleration, a vehicle speed, and a last applied motor torque and a rotor position.

4. The controller (100) as claimed in claim 1, wherein while said torque sensor (120) is functioning, said controller (100) compares said estimated steering torque (140) with the torque from the torque sensor (120), and continuously updates the trained regression-based model (110).
5. The controller (100) as claimed in claim 1, wherein said estimated steering torque (140) is substituted for a value of said torque sensor (120) which is determined to be failed.

6. A method for estimating a steering torque (140) for a steering system of a vehicle, said steering system comprises of a torque sensor (120) positioned on a steering column to determine said steering torque (140), said method comprising the steps of:
determining an error in said torque sensor (120), characterized by:
receiving input signals from a steering system parameters (130) from respective means (160) within said vehicle;
processing said input parameters through a trained regression-based model and estimating said steering torque (140).

7. The method as claimed in claim 6, wherein said steering system parameters (130) are selected from a group comprising steering angle, a steering angle speed, a rack force, a yaw rate, a longitudinal acceleration, a vehicle speed, a last applied motor torque and a rotor position and said respective means (160) to receive said steering system parameters (130) are selected from a group comprising a steering angle sensor, a steering speed sensor, a rack force sensor, a yaw rate sensor, a longitudinal acceleration sensor, a lateral acceleration sensor, a vehicle speed sensor, a motor torque sensing means and a rotor position sensor, respectively.

8. The controller (100) as claimed in claim 6, wherein said trained regression-based model (110) is pretrained using parameters comprising a steering angle, a steering angle speed, a rack force, a yaw rate, a longitudinal acceleration, a vehicle speed, and a last applied motor torque and a rotor position.

9. The controller (100) as claimed in claim 6, wherein while said torque sensor (120) is functioning, said controller (100) compares said estimated steering torque (140) with the torque from the torque sensor (120), and continuously updates the trained regression-based model (110).

10. The controller (100) as claimed in claim 6, wherein said estimated steering torque (140) is substituted for a measured value of said torque sensor (120) which is determined to be failed.