Description:FIELD OF DISCLOSURE
[0001] The present disclosure generally relates to aircraft control system and specifically relates to an artificial intelligence (AI)-based adaptive flight control system
BACKGROUND OF THE DISCLOSURE
[0002] Embodiments of the present invention generally relate to an artificial intelligence (AI)-based adaptive flight control system (AFCS).
[0003] Autopilot technology has long been a critical component of aviation, assisting pilots by automating various flight functions such as maintaining altitude, heading, and speed. However, traditional autopilot systems operate on predefined rule-based programming, limiting their ability to respond dynamically to unforeseen situations. Such systems rely on pre-set parameters and programmed responses, which work effectively under normal conditions but may not adapt well to rapidly changing environments such as sudden weather disturbances, unexpected air traffic, or technical failures.
[0004] The limitations of current autopilot systems become evident in scenarios requiring real-time decision-making. For an instance, modern aircraft frequently encounter complex weather patterns, turbulence, or mechanical anomalies that require instant corrective action. Pilots often have to intervene manually because existing autopilot systems lack the ability to analyze data in real-time data and make informed adjustments. Such dependency on human intervention increases pilot workload and increases the risk of human error, especially during long-haul flights where fatigue is a major concern.
[0005] Furthermore, aviation safety remains a significant concern, with human error contributing to a significant portion of accidents. While autopilot technology has improved over the decades, it still remains a reactive solution. The absence of predictive analytics in conventional autopilot solutions limits forecasting of potential risks based on evolving flight conditions, leading to increased fuel consumption, inefficient flight route optimization, and increased safety risks.
[0006] Existing autopilot solutions primarily function as assistive technologies and are designed to follow specific commands, such as maintaining altitude, holding a steady course, or executing pre-programmed flight paths. While some modern aircraft incorporate AI-assisted decision-making in limited area such as optimizing fuel consumption or enhancing automated landing sequences. Another limitation of traditional autopilot systems is inability to analyze and interpret data in real time. As flight conditions changes rapidly, requiring immediate adjustments. For an instance, turbulence, wind shear, or sudden mechanical issues may necessitate quick course corrections, but current autopilot systems lack the capability to interpret such data dynamically and adapt accordingly.
[0007] Another drawback is the lack of predictive analytics based on lack of integration of historical flight data to predict failures and other operational risks. Additionally, traditional autopilot technology struggles with operational efficiency in fuel management and route optimization. Airlines constantly seek to minimize fuel consumption and reduce emissions, but current autopilot systems do not have capabilities to optimize flight paths dynamically based on real-time data.
[0008] As the aviation industry is moving toward greater autonomy, and the integration of advanced technology into autopilot systems is the need of the hour. Therefore, there is a need for an advanced solutions that has intelligent decision-making capabilities based on vast amounts of historical and real-time data.
[0009] Therefore, there is a need for an adaptive flight control system (AFCS) that has capability to provide dynamic operational parameter control.
SUMMARY
[0010] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0011] Embodiments in accordance with the present invention provide an adaptive flight control system (AFCS). Embodiments in accordance with the present invention further provide a dynamic autopilot control method for an aircraft.
[0012] Embodiments of the present invention may provide a number of advantages depending on its particular configuration. First, embodiments of the present application provide an adaptive flight control system (AFCS). Next, embodiments of the present application provide a dynamic autopilot control method for an aircraft.
[0013] The present disclosure solves all the major limitation of traditional system.
[0014] An objective of the present disclosure is to enhance the autonomy, safety, and efficiency of aircraft operations by leveraging artificial intelligence (AI)-driven real-time decision-making, predictive analytics, and adaptive learning.
[0015] Another objective of the present disclosure is to continuously monitor and adjust flight operational parameters to ensure optimal stability.
[0016] Another objective of the present disclosure is to utilize machine learning algorithms to predict and counteract environmental disturbances such as turbulence, wind shear, and other anomalies.
[0017] Another objective of the present disclosure is to minimize the need for manual intervention for providing real-time adjustments.
[0018] Another objective of the present disclosure is to predict potential failures before they occur, reducing maintenance costs and improving aircraft reliability.
[0019] Yet another objective of the present disclosure is to refine the decision-making processes based on historical flight data.
[0020] Yet another objective of the present disclosure is to reduce the need for manual input in standard and emergency conditions.
[0021] Yet another objective of the present disclosure is to detect early signs of mechanical failures and other faults.
[0022] Yet another objective of the present disclosure is to adjust routes in-flight based on real-time data to avoid delays and minimize fuel consumption.
[0023] In the light of above disclosure, in an aspect of the present disclosure an adaptive flight control system (AFCS) is disclosed herein. The system comprising a hybrid input unit further comprising a processing unit. The hybrid input unit further comprising a plurality of onboard sensor providing input to the processing unit and the onboard sensors configured to collect real-data related to aircraft’s altitude, speed, direction, navigation, and engine data. The hybrid input unit further comprising an external data capturing module providing input to the processing unit and the external data capturing module configured to capture data from a plurality of external source. The hybrid input unit further comprising an intelligent data aggregation module executed by the processing unit the intelligent data aggregation module configured to extract and aggregate data collected form the plurality of onboard sensor and the external data capturing module. The system also comprising a backend processing assembly operationally coupled to the processing unit and the backend processing assembly further comprising a predictive detection model configured to model and analyze real-time data obtained from the intelligent data aggregation module and optimize various operation parameters for dynamic flight path correction. The backend processing assembly further comprising an artificial intelligence (AI) engine configured to continuously learns from historical and real-time flight data to improve decision-making and flight safety. The artificial intelligence (AI) engine anticipates critical flight conditions and autonomously initiate safety procedures before manually intervention. The backend processing assembly further comprising a control execution module configured to directly communicate with the processing unit to optimize a plurality of operational parameters of an aircraft. The system also comprising a display and control interface operationally coupled to the backend processing assembly and the display and control interface configured to synchronize with the processing unit to generate reports and user-friendly visualizations. The display and control interface provide the pilots with real-time artificial intelligence-driven recommendations based on situational awareness, reducing cognitive overload.
[0024] In one embodiment, the external data capturing module captures data from historical flight data log, and a plurality of external database such as, weather forecasts, terrain data, and air traffic control data.
[0025] In one embodiment, the intelligent data aggregation module employs a plurality of intelligent data fusion algorithms to aggregate data collected from the plurality of onboard sensor and the external data capturing module.
[0026] In one embodiment, the system also includes a dedicated database for storing historical flight data and risk and threat mitigating strategies.
[0027] In one embodiment, the system also includes a training module operable to collect data from the hybrid input unit.
[0028] In one embodiment, the system also includes a training module operable to provide training data to the predictive detection model.
[0029] In one embodiment, the training module facilitates the continuously learns from every flight, adjusting algorithms dynamically to improve future decision-making.
[0030] In one embodiment, the artificial intelligence (AI) engine further comprises a predictive analysis module configured to predict the weather conditions and probable turbulence.
[0031] In one embodiment, the artificial intelligence (AI) engine further comprises a flight path optimization module configured to optimize speed and fuel consumption for the aircraft.
[0032] In one embodiment, the artificial intelligence (AI) engine further comprises a risk management module configured to detect faults and threats.
[0033] In one embodiment, the control execution module autonomously adjusts flight routes in response to optimization parameters generated by artificial intelligence (AI) engine based on the real-time environmental conditions, weather forecasts, and air traffic information obtained from the hybrid input unit.
[0034] In another aspect of the present invention, a dynamic autopilot control method for an aircraft is disclosed herein. The method comprising collecting real-time data from a plurality of onboard sensor and an external data capturing module linked to the external databases. The method also comprising aggregating the gathered data using an intelligent data aggregation module. The intelligent data aggregation module employs a plurality of intelligent data fusion algorithm. The method also comprising modelling and analyzing the real-time data collected from a hybrid input unit using a predictive detection model. The method also comprising executing operational parameter optimization via the artificial intelligence (AI) engine. The method also comprising continuously monitoring and analyzing to provide real-time adaptive decision-making support. The method also comprising predicting and detecting potential faults and risks using a control execution module. The processing unit is synchronized with a display and control interface.
[0035] In one embodiment, the method also includes dynamically adjusting the operational parameter for the aircraft.
[0036] In one embodiment, the method also includes refining and optimizing operational parameter for the aircraft based on continuous learning from accumulated flight data.
[0037] These and other advantages will be apparent from the present application of the embodiments and solves abovementioned limitations in the traditional system.
[0038] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0039] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0040] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0041] FIG. 1A illustrates a block diagram of an adaptive flight control system (AFCS), according to an embodiment of the present invention;
[0042] FIG. 1B illustrates a functional block diagram of an adaptive flight control system (AFCS), according to an embodiment of the present invention;
[0043] FIG. 2 illustrates a flowchart for a dynamic autopilot control method for an aircraft, according to an embodiment of the present invention;
[0044] FIG. 3A illustrates operational flow of the dynamic autopilot control for an aircraft, according to another embodiment of the present invention;
[0045] FIG. 3B illustrates operational flow of the adaptive learning for the dynamic autopilot control for an aircraft, according to another embodiment of the present invention; and
[0046] FIG. 3C illustrates operational flow of the sensor data integration for the dynamic autopilot control for an aircraft, according to another embodiment of the present invention.
[0047] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0048] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.
[0049] In any embodiment described herein, the open-ended terms "comprising," "comprises,” and the like (which are synonymous with "including," "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like.
[0050] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0051] FIG. 1A illustrates a block diagram of an adaptive flight control system (AFCS) 100, according to an embodiment of the present invention.
[0052] FIG. 1B illustrates a functional block diagram of an adaptive flight control system (AFCS) 100, according to an embodiment of the present invention.
[0053] The system 100 may be comprising a hybrid input unit 102 further comprising a processing unit 104, a plurality of onboard sensor 106, an external data capturing module 108, backend processing assembly 112 further comprising a predictive detection model 114, an artificial intelligence (AI) engine 116, and a control execution module 118, and a display and control interface 120.
[0054] The plurality of onboard sensor 106 may be providing input to the processing unit 104 and the onboard sensors 106 configured to collect real-data related to aircraft’s altitude, speed, direction, navigation, and engine data.
[0055] In an embodiment of the present disclosure, the plurality of onboard sensor 102 may be multiple types of sensors installed on the aircraft to collect various forms of real-time data critical to flight control, navigation, safety, and performance monitoring. The multiple types of sensors may include, but not limited to, accelerometers to measure linear acceleration in multiple axes (x, y, z), gyroscopes to measure angular velocity (rate of rotation) to understand orientation, inertial measurement units to provide attitude, velocity, and orientation data, pitot tubes to measure dynamic air pressure to calculate airspeed, static ports to measure static air pressure used for altitude determination, air temperature sensors to measure the air temperature to correct airspeed and altitude calculations, angle of attack sensors to measure the angle between the wing chord line and the oncoming airflow, a variety of global navigation satellite system (GNSS) receivers to provide precise positioning and timing data, radio altimeters to measure altitude above ground level using radar, barometric altimeters to measure atmospheric pressure to determine altitude, weather radars to detect precipitation, turbulence, and storm cells, wind shear detectors to identify dangerous wind gradients that can affect aircraft stability, icing sensors to detect atmospheric conditions conducive to ice formation on the aircraft., engine pressure ratio sensors to measure thrust performance, fuel flow meters to monitor the amount of fuel being consumed, engine temperature sensors to monitor critical engine temperatures (Exhaust Gas Temperature (EGT), Oil Temp, etc.), vibration sensors to detect abnormal vibrations in engines and rotating components, strain gauges to measure structural stress and deformation, load cells to measure forces acting on different parts of the aircraft, forward-looking infrared to provide night vision or poor-visibility imaging. LIDAR/RADAR sensors for terrain awareness or proximity detection.
[0056] The external data capturing module 108 may be providing input to the processing unit 104 and the external data capturing module 108 configured to capture data from a plurality of external source.
[0057] The external data capturing module 108 may capture data from historical flight data log, and a plurality of external database such as, weather forecasts, terrain data, and air traffic control data.
[0058] In an embodiment of the present disclosure, the external data capturing module 108 may interface with multiple external databases such as digital elevation models (DEMs), obstacle databases, NOTAMs, and airport-specific information, to optimize flight efficiency and safety. The external data capturing module 108 may employ artificial intelligence (AI)-driven filtering mechanisms to prioritize relevant data based on the aircraft’s current phase of flight and environmental conditions. The external data capturing module 108 may ensure reliability by cross-verifying weather, ATC, and terrain data from multiple sources, providing robust fail-safe mechanisms to maintain uninterrupted operation.
[0059] The intelligent data aggregation module 110 may be executed by the processing unit 104 and the intelligent data aggregation module 110 configured to extract and aggregate data collected form the plurality of onboard sensor 106 and the external data capturing module 108.
[0060] The intelligent data aggregation module 110 may employ a plurality of intelligent data fusion algorithms to aggregate data collected from the plurality of onboard sensor 106 and the external data capturing module 108.
[0061] In an embodiment of the present disclosure, the intelligent data aggregation module 110 may through continuous integration of real-time data from the onboard sensor 106 and the external data capturing module 108 and historical data that enables the system 100 to improve predictive modeling and adaptive learning, enhancing overall flight performance and safety.
[0062] In an embodiment of the present disclosure, the intelligent data fusion algorithms may include, but not limited to, Kalman Filtering for noise reduction and state estimation, Bayesian Networks for probabilistic reasoning and decision-making under uncertainty, and Neural Network-Based Fusion for complex pattern recognition and adaptive learning. In some embodiments, the intelligent data fusion algorithms may include sensor fusion algorithms such as Weighted Averaging, and Particle Filtering. In an embodiment of the present disclosure, the intelligent data aggregation module 110 may also perform various other data pre-processing techniques such as data filtering, noise reduction, and more.
[0063] The backend processing assembly 112 may be operationally coupled to the processing unit 104 and the backend processing assembly 112 further comprising a predictive detection model 114 may be configured to model and analyze real-time data obtained from the intelligent data aggregation module 110 and optimize various operation parameters for dynamic flight path correction.
[0064] The system 100 may also include a training module 124 operable to collect data from the hybrid input unit 102, and provide training data to the predictive detection model 114.
[0065] The training module 124 may facilitate the continuously learns from every flight, adjusting algorithms dynamically to improve future decision-making.
[0066] In an embodiment of the present disclosure, the backend processing assembly 112 may be any remote processing unit such as, but not limited to, cloud server. In an embodiment of the present disclosure, the backend processing assembly 112 may be linked wirelessly to the hybrid input unit 102. In an embodiment of the present disclosure, the backend processing assembly 112 may be built upon a hybrid processing assembly comprising Field-Programmable Gate Arrays (FPGAs), Graphics Processing Units (GPUs), and Tensor Processing Units (TPUs), optimized for parallelized deep learning workloads and low-latency computations.
[0067] In an embodiment of the present disclosure, the predictive detection model 114 may utilize Reinforcement Learning (RL) algorithms to continuously learn optimal flight operational parameters through reward-based training. The predictive detection model 114 may dynamically adjust operation parameters such as speed, altitude, and heading to achieve goals related to safety, efficiency, and fuel optimization. The predictive detection model 114 may be guided by evaluating outcomes from previous flights and applying those lessons to improve future performance through the training module 124.
[0068] In an embodiment of the present disclosure, the predictive detection model 114 may employ supervised learning algorithms to model and optimize various flight operation parameters. The predictive detection model 114 may be trained using labeled datasets obtained from the training module (124), which collects data from the Hybrid Input Unit (102), including flight logs, sensor inputs, and environmental conditions. During the training process, the predictive detection model 114 may learn to recognize patterns associated with optimal flight performance under different scenarios. By continuously augmenting the training dataset with newly collected flight data, the predictive detection model 114 may progressively improves accuracy and reliability in predicting appropriate flight path corrections and safety measures.
[0069] In some embodiments of the present disclosure, the predictive detection model 114 may employ unsupervised learning algorithms to identify patterns and anomalies within the aggregated data without predefined labels and enables emerging threats or inefficiencies detection. In some embodiments of the present disclosure, the predictive detection model 114 may integrate reinforcement learning for real-time decision-making, supervised learning for fine-tuning predefined tasks, and unsupervised learning for anomaly detection and pattern discovery. By leveraging all three learning paradigms, the predictive detection model 114 may continuously adapts to evolving operational conditions, enhancing the robustness and flexibility of the flight control system. The training module 124 may facilitates hybrid approach by providing a comprehensive dataset that includes historical logs, real-time inputs, and labelled training data.
[0070] In some embodiments of the present disclosure, the predictive detection model 114 may establish a continuous learning pipeline to refine employed algorithms dynamically with the aid of the training module 124 to fine-tune the operational parameters and improving future decision-making.
[0071] The artificial intelligence (AI) engine 116 may be configured to continuously learns from historical and real-time flight data to improve decision-making and flight safety. The artificial intelligence (AI) engine 116 may anticipate critical flight conditions and autonomously initiate safety procedures before manually intervention.
[0072] In an embodiment of the present disclosure, the artificial intelligence (AI) engine 116 may employ a Service-Oriented Architecture (SOA) with microservices responsible for specialized tasks, including, but not limited to, predictive analytics, anomaly detection, and decision support. The artificial intelligence (AI) engine 116 may be Deep Neural Networks (DNNs) deployed using frameworks such as PyTorch, TensorFlow, and ONNX Runtime, with architectures including Convolutional Neural Networks (CNNs) for spatial pattern recognition, Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) for temporal sequence analysis, and such.
[0073] The artificial intelligence (AI) engine 116 may further comprise a predictive analysis module 126 configured to predict the weather conditions and probable turbulence.
[0074] In an embodiment of the present disclosure, the predictive analysis module 126 may employ supervised learning algorithms, including Support Vector Machines (SVM), Random Forests, and Neural Networks, trained on historical weather data, flight logs, and turbulence reports. By analysing large datasets containing labelled weather patterns and corresponding turbulence events, the predictive analysis module 126 may learn to identify correlations between environmental conditions (e.g., pressure, temperature, humidity, wind speed) and turbulence severity. Predictions may be continuously updated, enabling the system 100 to suggest course adjustments or altitude changes to avoid turbulence or severe weather.
[0075] In an embodiment of the present disclosure, the predictive analysis module 126 may leverage Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), including Long Short-Term Memory (LSTM) networks for spatial analysis of meteorological maps and radar images, identifying weather fronts, storm cells, and other hazardous conditions. By integrating spatial and temporal information, the predictive analysis module 126 may provide highly accurate short-term and long-term weather predictions, enabling dynamic flight path adjustments to optimize safety and efficiency.
[0076] In an embodiment of the present disclosure, the predictive analysis module 126 may use data-driven models such as Neural Networks and Decision Trees, trained on past flight data and meteorological information to recognize turbulence patterns to provide additional insight into probable turbulence areas. In an embodiment of the present disclosure, the predictive analysis module 126 may use predictive ensemble learning techniques, including bagging, boosting, and stacking, to aggregate predictions from multiple machine learning models.
[0077] The artificial intelligence (AI) engine 116 may further comprise a flight path optimization module 128 configured to optimize speed and fuel consumption for the aircraft.
[0078] In an embodiment of the present disclosure, the flight path optimization module 128 may receive actionable insights from the predictive analysis module 126 and employ Reinforcement Learning (RL) algorithms such as, but not limited to Proximal Policy Optimization (PPO), Deep Q-Networks (DQN), and Twin Delayed Deep Deterministic Policy Gradient (TD3) to optimize flight paths. The flight path optimization module 128 may continuously interacts with the predictive analysis module 126 for adjusting control variables (e.g., throttle, altitude, heading) to minimize fuel consumption and maximize speed efficiency.
[0079] In an embodiment of the present disclosure, the flight path optimization module 128 may specifically use Convolutional Neural Networks (CNNs) for spatial pattern recognition and Long Short-Term Memory (LSTM) networks for temporal sequence prediction and employs a Multi-Objective Optimization Framework that minimizes fuel consumption and optimizes speed based on predefined constraints and operational requirements.
[0080] In an embodiment of the present disclosure, the flight path optimization module 128 may use Model-Based Predictive Control (MPC) along with historical flight logs and environmental conditions for solving nonlinear programming (NLP) and quadratic programming (QP) problems to determine the most efficient flight path. This hybrid approach enables the system 100 to adapt to sudden changes in external conditions such as weather disturbances or unexpected traffic congestion.
[0081] In an embodiment of the present disclosure, the flight path optimization module 128 may generate multiple probable flight paths and iteratively improves them using genetic operations such as selection, crossover, and mutation. In an embodiment of the present disclosure, the flight path optimization module 128 may combine multiple models to enhance accuracy, reduces variance, and improves generalization across various flight conditions.
[0082] The artificial intelligence (AI) engine 116 may further comprise a risk management module 130 configured to detect faults and threats.
[0083] In an embodiment of the present disclosure, the risk management module 130 may employ Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer Models, to detect anomalies and faults in aircraft by identifying patterns indicative of potential failures (e.g., engine issues, hydraulic malfunctions) before they manifest into critical problems. In an embodiment of the present disclosure, the risk management module 130 may employ Hybrid Deep Learning Architectures that combine LSTM Networks for temporal sequence analysis with Transformer Models for spatial attention-based processing for identifying potential threats such as turbulence, mid-air collisions, runway incursions, and adverse weather conditions with high precision.
[0084] In some embodiment of the present disclosure, the risk management module 130 may employ autoencoders, Principal Component Analysis (PCA), Isolation Forests, and Gaussian Mixture Models (GMMs) to detect unusual patterns that may signify faults or security threats. The module may continuously monitor data streams from the onboard sensors 102 and communication channels, comparing real-time data against established normal operational baselines. Deviations trigger alerts, prompting further analysis or automated mitigation measures.
[0085] The control execution module 118 may be configured to directly communicate with the processing unit 104 to optimize a plurality of operational parameters of an aircraft.
[0086] The control execution module 118 may autonomously adjust flight routes in response to optimization parameters generated by artificial intelligence (AI) engine 116 based on the real-time environmental conditions, weather forecasts, and air traffic information obtained from the hybrid input unit 102.
[0087] In an embodiment of the present disclosure, the control execution module 118 may include several subcomponents such as, but not limited to, Flight Control Interface Layer, Feedback Loop Mechanism, and Command Execution Layer. In an embodiment of the present disclosure, the control execution module 118 may receive optimized operational parameters from the artificial intelligence (AI) engine (116) including, flight path optimization parameters, risk management and safety protocols, predictive weather and turbulence data, and such.
[0088] In an embodiment of the present disclosure, the control execution module 118 may employ Reinforcement Learning (RL) algorithms like, but not limited to, Deep Q-Networks (DQN), Soft Actor-Critic (SAC), and Proximal Policy Optimization (PPO) to dynamically respond to identified threats. The Reinforcement Learning (RL) algorithms may be continuously trained and fine-tuned using simulated environments that mimic real-world scenarios. In an embodiment of the present disclosure, the control execution module 118 may autonomously selects the optimal response strategy based on the severity and nature of the detected fault or threat by the risk management module 130. For instance, if an imminent collision is detected, the control execution module 118 may adjust flight parameters to perform an evasive maneuver.
[0089] In an embodiment of the present disclosure, the control execution module 118 may also evaluates multiple factors including, but not limited to, Flight efficiency (fuel consumption, speed, and altitude), Safety considerations (collision avoidance, severe weather avoidance) and Operational constraints (regulatory compliance, airspace restrictions). In an embodiment of the present disclosure, the control execution module 118 may optimization recommendations by using dynamic adjustment algorithms such as, but not limited to, Markov Decision Processes (MDP), Dynamic Programming, Nonlinear Model Predictive Control (NMPC), and Fuzzy Logic Controllers (for handling uncertain or imprecise inputs).
[0090] The display and control interface 120 may be operationally coupled to the backend processing assembly 112 and the display and control interface 120 configured to synchronize with the processing unit 104 to generate reports and user-friendly visualizations. The display and control interface 120 may provide the pilots with real-time artificial intelligence-driven recommendations based on situational awareness, reducing cognitive overload.
[0091] In an embodiment of the present disclosure, the display and control interface 120 may provide AI-driven recommendations generated by the backend processing assembly 112 and user-friendly visualizations to the pilots. The display and control interface 120 may be implemented using a combination of software and hardware components that ensure robust, real-time feedback and visualization of critical flight parameters, including, but not limited to, High-Resolution Touchscreen Displays, Heads-Up Displays (HUDs), Multifunctional Control Panels, Voice Interaction Modules, User Interface (UI) Frameworks, Visualization Libraries, AI Integration Layer, and Data Syncing Mechanism.
[0092] In an embodiment of the present disclosure, the display and control interface 120 may provide comprehensive control over flight settings, recommendations, and alerts. In some embodiments of the present disclosure, the display and control interface 120 may be integrated cockpit display, AI-assisted monitoring console, and portable interface module. In an embodiment of the present disclosure, the display and control interface 120 may display AI-driven suggestions related to route adjustments, weather avoidance, speed optimization, and fuel management and continuously updates recommendations based on real-time data fusion and predictive analytics. In an embodiment of the present disclosure, the display and control interface 120 may provide visualization for dynamic flight path alterations, potential threats, and alternative routes in a graphical and easily interpretable format. In some embodiments of the present disclosure, the display and control interface 120 may offer audio-visual cues to alert pilots about critical situations or suggested actions. In some embodiments of the present disclosure, the display and control interface 120 may provide concise, actionable insights to reduce decision-making time and mental effort.
[0093] The system 100 may also include a dedicated database 122 for storing historical flight data and risk and threat mitigating strategies.
[0094] In an embodiment of the present disclosure, the dedicated database 122 may be a Relational Database Management System (RDBMS) such as MySQL, PostgreSQL, or Oracle, a NoSQL database like MongoDB, Cassandra, or Elasticsearch, a Time-Series Database such as InfluxDB or TimescaleDB, Distributed Databases (e.g., Apache Cassandra, CockroachDB) or Cloud-Based Storage Solutions (e.g., AWS S3, Google Cloud Storage), and a Hybrid Database Architecture.
[0095] FIG. 2 illustrates a flowchart for a dynamic autopilot control method 200 for an aircraft, according to an embodiment of the present invention.
[0096] The method 200 may be comprising the following steps.
[0097] At 202, collecting real-time data from a plurality of onboard sensor 106 and an external data capturing module 108 linked to the external databases.
[0098] At 204, aggregating the gathered data using an intelligent data aggregation module 110. The intelligent data aggregation module 110 may employ a plurality of intelligent data fusion algorithm.
[0099] In an embodiment of the present disclosure, the of intelligent data fusion algorithm may provide real-time data on aircraft position and orientation, speed and altitude, environmental hazards, aircraft’s health, and proximity to other aircraft or terrain.
[0100] At 206, modelling and analyzing the real-time data collected from a hybrid input unit 102 using a predictive detection model 114.
[0101] At 208, executing operational parameter optimization via the artificial intelligence (AI) engine 116.
[0102] At 210, continuously monitoring and analyzing to provide real-time adaptive decision-making support, and predict and detect potential faults and risks using a control execution module 118. The processing unit 104 may be synchronized with a display and control interface 120.
[0103] The method 200 may also include dynamically adjusting the operational parameter for the aircraft, and refining and optimizing operational parameter for the aircraft based on continuous learning from accumulated flight data.
[0104] In an embodiment of the present disclosure, the method 200 may continuously learn from every flight via a training module 124, adjusting the employed algorithms dynamically to improve future decision-making. In an embodiment of the present disclosure, the method 200 may use the flight path optimization module 128 to anticipate critical flight conditions and autonomously initiate safety procedures before pilots manually intervene. In an embodiment of the present disclosure, the method 200 may use real real-time external inputs and onboard sensors 102 inputs for a fully context-aware control of flight’s operational parameters. In an embodiment of the present disclosure, the method 200 may provide the pilots with real-time AI-driven recommendations based on situational awareness, reducing cognitive overload via the display and control interface 120.
[0105] FIG. 3A illustrates operational flow 300 of the dynamic autopilot control for an aircraft, according to another embodiment of the present invention.
[0106] FIG. 3B illustrates operational flow 300 of the adaptive learning for the dynamic autopilot control for an aircraft, according to another embodiment of the present invention.
[0107] FIG. 3C illustrates operational flow 300 of the sensor data integration for the dynamic autopilot control for an aircraft, according to another embodiment of the present invention.
[0108] The operational flow 300, as depicted in FIG. 3A-C may include the following steps.
[0109] At 302, collecting data from the plurality of onboard sensor 106.
[0110] At 304, using multi-sensor data integration algorithm.
[0111] At 306, employing artificial intelligence (AI) unit and neural network model for analysis of collected real-time data.
[0112] At 308, processing incoming data to make decision on flight path and safety.
[0113] At 310, adaptively controlling adaptive flight operational parameters.
[0114] In an embodiment of the present disclosure, the adaptive control may include the following steps.
[0115] At 312, analyzing the incoming sensor data.
[0116] At 314, performing AI processing to recognize patterns in the incoming data.
[0117] At 316, predicting fuel optimization and flight path.
[0118] In an embodiment of the present disclosure, the sensor data integration may include the following steps.
[0119] At 318, collecting the weather prediction data and the air traffic control data.
[0120] At 320, performing data fusion of the weather prediction data and the air traffic control data with the incoming sensor data.
[0121] At 322, using for AI for autopilot decision making.
[0122] At 324, adaptive flight control implementation.
[0123] The comparison of the disclosed invention with the prior/existing solutions is presented in the table 1.
Table 1: Comparison with prior solutions
Feature Traditional Autopilot Systems AI-Enhanced Autopilot System
Data Processing Predefined rule-based Real-time adaptive learning
Flight Path Optimization Manual or rule-based AI dynamically adjusts routes
Pilot Assistance Limited AI-driven real-time suggestions
Emergency Handling Pilot-dependent AI-assisted autonomous responses
Predictive Maintenance Reactive AI-driven failure detection
Multi-Sensor Integration Basic sensor inputs Comprehensive multi-source fusion
[0124] The disclosed invention offers several advantages including, improved operational efficiency, reduce costs of operations, and improved real-time autonomous decision making. The disclosed invention represents a major advancement in aviation technology, which can revolutionize the way modern aircraft operate. Unlike traditional autopilot and flight control systems that rely on predefined rules and manual inputs, the disclosed invention enhances safety, efficiency, and autonomy through real-time decision-making, predictive analytics, and adaptive learning.
[0125] The disclosed invention has capability to enhance flight safety and stability. Traditional autopilot systems operate within fixed parameters and struggle to adapt to sudden disturbances such as turbulence, wind shear, or system failures. In contrast, the disclosed invention continuously analyzes real-time sensor data to detect and respond to potential hazards and faults before escalation of the situation. The system 100 can optimize various operational parameters micro-adjustments to flight controls to maintain stability, reducing the risk of accidents caused by sudden atmospheric changes or unexpected mechanical issues.
[0126] The disclosed invention leverages real-time data processing and adaptive learning. The system 100 continuously collects and analyzes flight data, weather conditions, and aircraft performance metrics to make intelligent, data-driven decisions. Unlike conventional systems that follow predetermined protocols, the disclosed invention can adjust dynamically to changing conditions. The system 100 can modify altitude, speed, and more, instantly in an autonomous manner to optimize flight performance, which improves overall flight efficiency and ensures improved safety.
[0127] The disclosed invention reduced the work of the pilot and minimizes the possibility of human error. Often pilots face high cognitive loads, especially during long-haul flights, adverse weather conditions, or emergency situations. Human fatigue and stress are significant contributors to aviation accidents. The disclosed invention reduces pilot workload by automating complex flight control tasks, allowing pilots to focus on strategic decision-making and supervision rather than manual adjustments. The system 100 can potentially serve as a virtual co-pilot, offering real-time suggestions and taking control during adverse situations.
[0128] The disclosed invention also provides improved fuel efficiency and related cost savings and performs intelligent route optimization and traffic management. As the system 100 incorporates real-time data acquired from the satellite weather data, air traffic control (ATC) updates, and the onboard sensor 194. This allows the system 100 to anticipate congestion, adverse weather conditions, or restricted airspace and make real-time route adjustments.
[0129] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
[0130] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims.
, Claims:I/We Claim:
1. An adaptive flight control system (AFCS) (100), the system (100) comprising:
a hybrid input unit (102) further comprising:
a processing unit (104);
a plurality of onboard sensor (106) providing input to the processing unit (104), the onboard sensors (106) configured to collect real-data related to aircraft’s altitude, speed, direction, navigation, and engine data;
an external data capturing module (108) providing input to the processing unit (104), the external data capturing module (108) configured to capture data from a plurality of external source;
an intelligent data aggregation module (110) executed by the processing unit (104), the intelligent data aggregation module (110) configured to extract and aggregate data collected form the plurality of onboard sensor (106) and the external data capturing module (108);
a backend processing assembly (112) operationally coupled to the processing unit (104), the backend processing assembly (112) further comprising:
a predictive detection model (114) configured to model and analyze real-time data obtained from the intelligent data aggregation module (110) and optimize various operation parameters for dynamic flight path correction;
an artificial intelligence (AI) engine (116) configured to continuously learns from historical and real-time flight data to improve decision-making and flight safety,
wherein the artificial intelligence (AI) engine (116) anticipates critical flight conditions and autonomously initiate safety procedures before manually intervention;
a control execution module (118) configured to directly communicate with the processing unit (104) to optimize a plurality of operational parameters of an aircraft; and
a display and control interface (120) operationally coupled to the backend processing assembly (112), the display and control interface (120) configured to synchronize with the processing unit (104) to generate reports and user-friendly visualizations;
wherein the display and control interface (120) provide the pilots with real-time artificial intelligence-driven recommendations based on situational awareness, reducing cognitive overload.
2. The system (100) as claimed in claim 1, wherein the external data capturing module (108) captures data from historical flight data log, and a plurality of external database such as, weather forecasts, terrain data, and air traffic control data.
3. The system (100) as claimed in claim 2, wherein the intelligent data aggregation module (110) employs a plurality of intelligent data fusion algorithms to aggregate data collected from the plurality of onboard sensor (106) and the external data capturing module (108).
4. The system (100) as claimed in claim 1, wherein the system (100) also includes a dedicated database (122) for storing historical flight data and risk and threat mitigating strategies.
5. The system (100) as claimed in claim 1, wherein the system (100) also includes a training module (124) operable to:
collecting data from the hybrid input unit (102); and
providing training data to the predictive detection model (114).
6. The system (100) as claimed in claim 5, wherein the training module (124) facilitates the continuously learns from every flight, adjusting algorithms dynamically to improve future decision-making.
7. The system (100) as claimed in claim 1, wherein the artificial intelligence (AI) engine (116) further comprises:
a predictive analysis module (126) configured to predict the weather conditions and probable turbulence;
module (128) configured to optimize speed and fuel consumption for the aircraft; and
a risk management module (130) configured to detect faults and threats.
8. The system (100) as claimed in claim 1, wherein the control execution module (118) autonomously adjusts flight routes in response to optimization parameters generated by artificial intelligence (AI) engine (116) based on the real-time environmental conditions, weather forecasts, and air traffic information obtained from the hybrid input unit (102).
9. A dynamic autopilot control method (200) for an aircraft, the method (200) comprising:
collecting real-time data from a plurality of onboard sensor (106) and an external data capturing module (108) linked to the external databases;
aggregating the gathered data using an intelligent data aggregation module (110),
wherein the intelligent data aggregation module (110) employs a plurality of intelligent data fusion algorithm;
modelling and analyzing the real-time data collected from a hybrid input unit (102) using a predictive detection model (114);
executing operational parameter optimization via the artificial intelligence (AI) engine (116);
continuously monitoring and analyzing to provide real-time adaptive decision-making support, and predict and detect potential faults and risks using a control execution module (118),
wherein the processing unit (104) is synchronized with a display and control interface (120).
10. The method (200) as claimed in claim 9, wherein the method (200) also includes:
dynamically adjusting the operational parameter for the aircraft; and
refining and optimizing operational parameter for the aircraft based on continuous learning from accumulated flight data.