Neural-Network-Based Collaborative Control for Continuous Unknown Nonlinear Systems

This paper proposes an NN-based cooperative control scheme for a type of continuous nonlinear system. The model studied in this paper is designed as an interconnection topology, and the main consideration is the connection mode of the undirected graph. In order to ensure the online sharing of learning knowledge, this paper proposes a novel weight update scheme. In the proposed update scheme, the weights of the neural network are discrete, and these discrete weights can gradually approach the optimal value through cooperative learning, thereby realizing the control of the unknown nonlinear system. Through the trained neural network, it is proved if the interconnection topology is undirected and connected, the state of the unknown nonlinear system can converge to the target trajectory after a finite time, and the error of the system can converge to a small neighbourhood around the origin. It is also guaranteed that all closed-loop signals in the system are bounded. A simulation example is provided to more intuitively prove the effectiveness of the proposed distributed cooperative learning control scheme at the end of the article.