Optimized Backstepping-Based Containment Control for Multiagent Systems With Deferred Constraints Using a Universal Nonlinear Transformation
提出一种通用非线性变换方法，处理多智能体系统中所有跟随者受延迟全状态约束的优化包含控制问题，利用神经网络强化学习算法优化性能，并证明系统有界且跟随者输出进入领导者凸包。
This article investigates an optimized containment control problem for multiagent systems (MASs), where all followers are subject to deferred full-state constraints. A universal nonlinear transformation is proposed for simultaneously handling the cases with and without constraints. Particularly, for the constrained case, initial values of states are flexibly managed to the midpoint between upper and lower boundaries by utilizing a state-shifting function, thus eliminating the initial restriction conditions. By deferred constraints, the state is forced to fall back into the restrictive boundaries within a preassigned time. A neural network (NN)-based reinforcement learning (RL) algorithm is executed under the identifier-critic-actor architecture, where the Hamilton-Jacobi-Bellman (HJB) equation is built in every subsystem to optimize control performance. For actor and critic NNs, updating laws are simplified, since the gradient descent method is performed based on a simple positive function rather than square of Bellman residual error. In view of the Lyapunov stability theorem and graph theory, it is proved that all signals are bounded and the outputs of followers can eventually enter into the convex hull constituted by leaders. Finally, simulations confirm the validity of the proposed approach.