Performance Improvement of Consensus Tracking for Linear Multiagent Systems With Input Saturation: A Gain Scheduled Approach
针对输入饱和下领导者-跟随者多智能体系统，提出增益调度方法，通过时变增益控制器和参数调度器加速一致性跟踪，并设计基于最小值的分布式算法消除对全局信息的依赖。
For leader-following multiagent systems with input saturation, the existing protocols use a low gain feedback approach to achieve semi-global consensus. The main drawback of this approach is the ineffective utilization of the actuator potential, resulting in bad performance. To improve the transient performance of the consensus tracking, this paper proposes a gain scheduled approach for multiagent systems subject to the saturator saturations. A novel kind of scheduler-based protocols are proposed, which consists of state feedback controllers with time-varying gain and parameter schedulers. The role of the controllers is to achieve the consensus tracking, while the schedulers can accelerate this consensus progress by enlarging the gain parameter. To remove the dependence of the schedulers on global information, a minimum-value-based consensus algorithm is put forward, with idea of driving all values of agents throughout the network to their minimum value. Its implementation is guaranteed by the network-topology connectivity. Finally, our approach is further extended to the case where the leader's control input is nonzero, time-varying, and bounded. The discontinuous protocol and its continuous approximation counterpart are designed, yielding the exactand quasi-consensus tracking, respectively. Simulation results verify the theoretical analysis.