Event-based distributed cooperative neural learning control for nonlinear multiagent systems with time-varying output constraints

Abstract

In practical engineering, many systems are required to operate under different constraint conditions due to considerations of system security. Violating these constraints conditions during operation may lead to performance degradation. Additionally, communication among agents is highly dependent on the network, which inevitably imposes a network burden on the control systems. To address these issues, this paper investigates the switching event-triggered distributed cooperative learning control issue for nonlinear multiagent systems with time-vary output constraints. An improved output-dependent universal barrier function with adjustable constraint boundaries is proposed, which can uniformly handle symmetric or asymmetric output constraints without changing the controller structure. Meanwhile, an improved switching event-triggered condition is designed based on neural networks (NNs) weight, which can allow the system to adaptively adjust the NNs weight update frequency according to the performance of the system, thereby saving communication resources. Furthermore, the Padé approximation technique is employed to address the input delay issue and simplify the controller design process. Using Lyapunov stability theory, it is proved that the outputs of all followers converge to a neighborhood around the leader output without violating output constraints, and all signals in the closed-loop system remain ultimately bounded. At last, the availability of the presented approach can be verified through some simulation results.