How do you apply machine learning to flight control?

1 What is flight control?

Flight control is the process of regulating the motion and attitude of an aircraft or a spacecraft using sensors, actuators, and algorithms. Flight control systems (FCS) are the hardware and software components that implement the control logic and interface with the vehicle and the environment. FCS can be classified into two main types: conventional and intelligent. Conventional FCS use fixed or predefined control laws that are based on mathematical models and physical principles. Intelligent FCS use adaptive or learning-based control laws that can adjust to changing conditions and uncertainties.

2 Why use ML for flight control?

ML can offer several advantages for flight control, such as improving the performance and robustness of FCS by learning from data and feedback, rather than relying on inaccurate or incomplete models. It can also reduce the design and testing time and cost of FCS by automating the controller synthesis and tuning process, rather than using manual or heuristic methods. Moreover, ML enables the FCS to cope with complex and nonlinear dynamics, such as aerodynamic effects, structural deformations, and faults that are difficult to model or predict. In addition, it enables the FCS to handle multi-objective and multi-modal optimization problems, such as balancing fuel efficiency, safety, and comfort or switching between different flight modes and tasks.

3 How to apply ML to flight control?

Depending on the level of integration and the type of learning, there are various ways to apply ML to flight control. Supervised learning involves training a controller or a model using labeled data, such as input-output pairs or desired trajectories. For instance, a neural network can be trained to approximate the inverse dynamics of an aircraft and then used as a feedforward controller to compensate for nonlinearities and uncertainties. Reinforcement learning, on the other hand, involves learning a controller or a policy by interacting with the system and the environment, and receiving rewards or penalties based on performance. For example, through Q-learning, an optimal control policy for a quadrotor can be learned by exploring different actions and observing their effects on the state and the reward. Finally, unsupervised learning involves learning a controller or a model without using labeled data, but rather by finding patterns or structures in the data. A clustering algorithm can be used to identify different flight regimes or modes and then select the appropriate controller for each mode.

4 What are the challenges of ML for flight control?

ML for flight control presents certain challenges, such as data quality and availability, safety and reliability, and explainability and interpretability. For instance, the data used for training or testing the ML models or controllers must be reliable, representative, and sufficient, otherwise the results may be inaccurate, biased, or overfitted. Additionally, the ML models or controllers must be able to guarantee the safety and reliability of the system while also providing transparency, traceability, and accountability of their decisions. Moreover, data collection and processing can be costly and time-consuming for high-fidelity and real-world systems. Therefore, verification, validation, and testing methods are necessary to account for the complexity and variability of the ML methods.

5 What are the trends of ML for flight control?

ML for flight control is an active and evolving research field, with many opportunities and challenges for future development. Hybrid and hierarchical methods, which involve combining different types of ML methods or different levels of abstraction, are becoming increasingly popular in order to achieve better performance and robustness. Additionally, multi-agent and cooperative methods are being used to apply ML methods to multiple systems or agents that can communicate and coordinate with each other. Lastly, human-in-the-loop and human-centric methods are being utilized to apply ML methods to systems or agents that can interact and collaborate with human operators or users. All of these trends present exciting possibilities for the future of flight control.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?