Division of Engineering Technology, Oklahoma State University, Stillwater, OK, USA.
Department of Electrical and Systems Engineering, Washington University, St. Louis, MO, USA.
Neural Netw. 2019 Jun;114:78-90. doi: 10.1016/j.neunet.2019.02.008. Epub 2019 Mar 8.
This paper presents a near optimal adaptive event-based sampling scheme for tracking control of an affine nonlinear continuous-time system. A zero-sum game approach is proposed by introducing a novel performance index. The optimal value function, i.e., the solution to the associatedHamilton-Jacobi-Issac (HJI) equation is approximated using a functional link neural network (FLNN) with event-based aperiodic state feedback information as inputs. The saddle point approximated optimal solution is employed to design the near optimal event-based control policy and the sampling condition. An impulsive weight update scheme is designed to guarantee local ultimate boundedness of the closed-loop parameters, which is analyzed via extension of Lyapunov stability theory for the impulsive hybrid dynamical systems. Zeno-freeness of the event-sampling scheme is enforced and its effect on stability is analyzed. Finally, numerical simulation results are included to corroborate the analytical design, which shows a 48.82% reduction of feedback communication and computational load.
本文提出了一种用于仿射非线性连续时间系统跟踪控制的近最优自适应基于事件的采样方案。通过引入一种新的性能指标，提出了一种零和博弈方法。最优值函数，即关联的哈密顿-雅可比-伊萨（HJI）方程的解，使用具有基于事件的非周期状态反馈信息作为输入的函数链接神经网络（FLNN）进行近似。鞍点近似最优解用于设计近最优基于事件的控制策略和采样条件。设计了一个脉冲权重更新方案，以保证闭环参数的局部有界性，这是通过扩展脉冲混合动态系统的李雅普诺夫稳定性理论进行分析的。强制基于事件的采样方案无零活动，并分析其对稳定性的影响。最后，包括数值模拟结果以验证分析设计，结果表明反馈通信和计算负载减少了 48.82%。
