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Neural-Network-Based Control for Discrete-Time Nonlinear Systems with Input Saturation Under Stochastic Communication Protocol
IEEE/CAA Journal of Automatica Sinica ( IF 15.3 ) Pub Date : 2021-02-19 , DOI: 10.1109/jas.2021.1003922
Xueli Wang , Derui Ding , Hongli Dong , Xian-Ming Zhang

In this paper, an adaptive dynamic programming (ADP) strategy is investigated for discrete-time nonlinear systems with unknown nonlinear dynamics subject to input saturation. To save the communication resources between the controller and the actuators, stochastic communication protocols (SCPs) are adopted to schedule the control signal, and therefore the closed-loop system is essentially a protocol-induced switching system. A neural network (NN)-based identifier with a robust term is exploited for approximating the unknown nonlinear system, and a set of switch-based updating rules with an additional tunable parameter of NN weights are developed with the help of the gradient descent. By virtue of a novel Lyapunov function, a sufficient condition is proposed to achieve the stability of both system identification errors and the update dynamics of NN weights. Then, a value iterative ADP algorithm in an offline way is proposed to solve the optimal control of protocol-induced switching systems with saturation constraints, and the convergence is profoundly discussed in light of mathematical induction. Furthermore, an actor-critic NN scheme is developed to approximate the control law and the proposed performance index function in the framework of ADP, and the stability of the closed-loop system is analyzed in view of the Lyapunov theory. Finally, the numerical simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

中文翻译:

随机通信协议下输入饱和的离散时间非线性系统的神经网络控制

在本文中,针对具有未知非线性动力学且受输入饱和影响的离散时间非线性系统,研究了一种自适应动态规划(ADP)策略。为了节省控制器和执行器之间的通信资源,采用了随机通信协议(SCP)来调度控制信号,因此,闭环系统本质上是协议引发的交换系统。利用具有鲁棒性项的基于神经网络(NN)的标识符来逼近未知非线性系统,并借助梯度下降来开发一组具有附加NN权重可调参数的基于开关的更新规则。凭借新颖的Lyapunov函数,提出了一个充分的条件来实现系统识别误差和神经网络权重更新动态的稳定性。然后,提出了一种离线方式的值迭代ADP算法,以解决具有饱和约束的协议诱导交换系统的最优控制,并结合数学归纳法深入讨论了收敛性。此外,在ADP框架下,开发了一种基于行为准则的神经网络算法,以近似控制律和拟议的性能指标函数,并根据李雅普诺夫理论对闭环系统的稳定性进行了分析。最后,数值仿真结果表明了所提出的控制方案的有效性。提出了一种离线方式的值迭代ADP算法,以解决具有饱和约束的协议交换系统的最优控制,并结合数学归纳法对收敛进行了深入的讨论。此外,在ADP框架下,开发了一种基于行为准则的神经网络算法,以近似控制律和拟议的性能指标函数,并根据李雅普诺夫理论对闭环系统的稳定性进行了分析。最后,数值仿真结果表明了所提出的控制方案的有效性。提出了一种离线方式的值迭代ADP算法,以解决具有饱和约束的协议交换系统的最优控制问题,并结合数学归纳法深入讨论了收敛性。此外,在ADP框架下,开发了一种基于行为准则的神经网络算法,以近似控制律和拟议的性能指标函数,并根据李雅普诺夫理论对闭环系统的稳定性进行了分析。最后,数值仿真结果表明了所提出的控制方案的有效性。在ADP框架下,提出了一种基于行为准则的神经网络算法,以逼近控制律和拟议的性能指标函数,并根据李雅普诺夫理论对闭环系统的稳定性进行了分析。最后,数值仿真结果表明了所提出的控制方案的有效性。在ADP框架下,提出了一种基于行为准则的神经网络算法,以逼近控制律和拟议的性能指标函数,并根据李雅普诺夫理论对闭环系统的稳定性进行了分析。最后,数值仿真结果表明了所提出的控制方案的有效性。
更新日期:2021-03-12
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