基于迭代学习的多智能体系统分布式跟踪控制 （英文）

Iterative learning-based consensus tracking control for conformable multi-agent systems

中文关键词  迭代学习控制  跟踪控制  具适导数  多智能体系统

英文关键词  Iterative learning control, consensus tracking control, conformable derivative, multi-agent systems

中文摘要

      文章考虑了具适多智能体系统的分布式跟踪控制问题。通过设计带有初始学习机制的$P$型和$PD^{\alpha}$ 型迭代学习控制策略求解跟踪问题。具适导数具有良好的性质且可以刻画不同步长的实际数据采样情况。初始学习机制放松了初始值条件且提高了算法实现趋同跟踪的性能。在可重复操作环境和有向通信拓扑的假设下，提出了一种分布式迭代学习方案，通过重复同一轨迹的控制尝试和用跟踪误差修正不满意的控制信号来实现有限时间趋同。严格证明了随着迭代次数增加，提出的$P$型和$PD^{\alpha}$ 型迭代学习控制策略使得所有智能体能渐近跟踪上参考轨迹。两个代表性数值仿真验证了算法的有效性。

英文摘要

      This paper considers the consensus tracking control problem for conformable multi-agent systems with linear and nonlinear dynamics by designing $P$-type and $PD^{\alpha}$-type iterative learning control law with initial learning mechanisms. Conformable derivative is well-behaved and can characterize a different step in real data sampling. The initial learning mechanism relaxes the initial value condition and improves the performance of the protocol to achieve consensus tracking. A distributed iterative learning scheme is proposed to realize the finite-time consensus by repeating the control attempt of the same trajectory and correcting the unsatisfactory control signal with the tracking error under the assumption of repeatable operation environments as well as a directed communication topology. The asymptotical convergence of the proposed $P$-type and $PD^{\alpha}$-type distributed iterative learning protocol for all agents is strictly proved as the iteration number increases. Two numerical examples are simulated to verify the effectiveness of the protocols.