Prescribed Performance Optimal Consensus of MASs With Connectivity Preservation

This article investigates a distributed finite-horizon optimal leader-follower consensus for discrete-time multi-agent systems with prescribed performance. Each agent has a limited communication range. First, to maintain the topology connectivity and satisfy performance requirements, a segmented error transformation function is designed. In this case, the terminal cost function is designed to transform an infinite horizon optimal problem into a finite horizon optimal problem. Furthermore, the algorithm via adaptive dynamic programming is developed to optimize the performance index function. The convergence analysis of the iterative algorithm is provided. Since it is almost impossible to directly solve the Hamilton-Jacobi-Bellman (HJB) equation, the reinforcement learning method with neural networks is introduced. Finally, simulation results demonstrate the effectiveness of the proposed optimal control method.