A Dynamic Neural Network-Based Control Method Using Reinforcement Learning for Nonlinear Parameter-Varying System With Application to Morphing Aircraft.

Abstract

This article proposes a dynamic neural network (DNN)-based control method to realize the optimal control of nonlinear parameter-varying (NPV) systems. Specifically, a DNN-based control policy (DNN-CP) composed of static shared layers and a parameter-related dynamic layer is constructed to improve the generalization and adaptability. An extreme learning machine (ELM)-based weight prediction model is established to fit the relationship between the dynamic weights and the system parameters. The shared layers are updated by solving the constrained multiobjective problem to reduce performance conflicts among different systems, and the weight prediction model is tuned by maximizing parameter-related objectives to achieve optimal control of each system. To improve data efficiency and adaptability, a supervised learning-based pretraining and reinforcement learning (RL)-based fine-tuning algorithm is developed. Finally, the control performance of the DNN-CP is verified on morphing aircraft. We demonstrate that the designed DNN-CP and training algorithm can achieve generalization capabilities, and DNN-CP can be immediately generalized to any system within the parameter space without sample collection or fine-tuning. Compared with other methods, DNN-CP has better control performance on the system with continuously varying parameters.