AI 中文总结
针对含随机不确定性的多智能体系统，提出基于强化学习的最优分布式控制算法，采用 actor-critic-identifier 结构，用低通滤波器和混合事件触发控制策略，经稳定性证明，在仿真中验证正确性并与非最优算法比较突出优势。
AI 中文摘要
我们针对具有随机不确定性的多智能体系统提出了一种基于强化学习的最优分布式控制算法。与现有方法不同，在优化反步设计过程中，采用了 actor-critic-identifier 结构。actor 神经网络反映控制行为，critic 神经网络评估控制性能，标识符神经网络处理未知随机不确定性。此外，低通滤波器有效抑制非仿射非线性故障问题，提出混合事件触发控制策略降低控制频率。通过李雅普诺夫稳定性证明保证所有误差有界，在单轴机器人仿真中验证算法正确性，并与非最优控制算法比较突出优势。
英文摘要
We propose a reinforcement learning (RL) based optimal distributed control algorithm for the multi-agent systems (MASs) with stochastic uncertainties. Unlike existing methods, during the optimized backstepping design process, we use the actor-critic-identifier structure. The actor neural network is used to reflect control behavior, the critic neural network works to evaluate control performance and the unknown stochastic uncertainties are handled by identifier neural network. Furthermore, a low-pass filter effectively suppresses problems stemming from non-affine nonlinear faults and a hybrid event-triggered control (ETC) strategy is proposed to reduce control frequency. We analyze our algorithm's operation, and we provide a Lyapunov-based stability proof that guarantees all errors are bounded, ensuring precise tracking between the leader and followers. We validate its correctness in a single-axis robotic manipulator simulation and finally, we compare against the non-optimal control algorithm highlighting our optimal control algorithm's operational advantages.
