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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Near Optimal Event-Triggered Control of Nonlinear Discrete-Time Systems Using Neurodynamic Programming
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Near Optimal Event-Triggered Control of Nonlinear Discrete-Time Systems Using Neurodynamic Programming

机译：基于神经动力程序的非线性离散系统的接近最优事件触发控制

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摘要

This paper presents an event-triggered near optimal control of uncertain nonlinear discrete-time systems. Event-driven neurodynamic programming (NDP) is utilized to design the control policy. A neural network (NN)-based identifier, with event-based state and input vectors, is utilized to learn the system dynamics. An actor-critic framework is used to learn the cost function and the optimal control input. The NN weights of the identifier, the critic, and the actor NNs are tuned aperiodically once every triggered instant. An adaptive event-trigger condition to decide the trigger instants is derived. Thus, a suitable number of events are generated to ensure a desired accuracy of approximation. A near optimal performance is achieved without using value and/or policy iterations. A detailed analysis of nontrivial inter-event times with an explicit formula to show the reduction in computation is also derived. The Lyapunov technique is used in conjunction with the event-trigger condition to guarantee the ultimate boundedness of the closed-loop system. The simulation results are included to verify the performance of the controller. The net result is the development of event-driven NDP.
机译：本文提出了不确定的非线性离散时间系统的事件触发的接近最优控制。事件驱动的神经动力学程序设计（NDP）用于设计控制策略。利用基于神经网络（NN）的标识符以及基于事件的状态和输入矢量来学习系统动力学。使用行为者批判框架来学习成本函数和最佳控制输入。标识符，评论者和演员NN的NN权重在每个触发的瞬间都会进行非周期性地调整。得出决定触发时刻的自适应事件触发条件。因此，生成适当数量的事件以确保期望的近似精度。在不使用值和/或策略迭代的情况下，可获得接近最佳的性能。还对非平凡的事件间时间进行了详细分析，并使用显式公式来显示计算量的减少。 Lyapunov技术与事件触发条件结合使用，以确保闭环系统的最终有界性。包含仿真结果以验证控制器的性能。最终结果是开发了事件驱动的NDP。

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