Event-triggered optimal adaptive control algorithm for continuous-time nonlinear systems
Event-triggered optimal adaptive control algorithm for continuous-time nonlinear systems
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DOI：
10.1109/jas.2014.7004686
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
K. Vamvoudakis
中科院分区：
文献类型：
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作者：
K. Vamvoudakis
针对非线性连续时间系统，提出了一种新的最优自适应事件触发控制算法。其目标是通过仅在事件被触发时对状态进行采样来减少控制器更新，以保持稳定性和最佳性。在线算法是基于参与者/批评者神经网络结构实现的。用批评性神经网络逼近代价，用执行者神经网络逼近最优事件触发控制器。由于在所提出的算法中，存在由常微分方程组和瞬时跳跃或脉冲描述的连续演化的动力学，因此我们将使用脉冲系统方法。李雅普诺夫稳定性证明确保闭环系统渐近稳定。最后，通过与时间触发控制器的比较，说明了该方法的有效性。
This paper proposes a novel optimal adaptive event-triggered control algorithm for nonlinear continuous-time systems. The goal is to reduce the controller updates, by sampling the state only when an event is triggered to maintain stability and optimality. The online algorithm is implemented based on an actor/critic neural network structure. A critic neural network is used to approximate the cost and an actor neural network is used to approximate the optimal event-triggered controller. Since in the algorithm proposed there are dynamics that exhibit continuous evolutions described by ordinary differential equations and instantaneous jumps or impulses, we will use an impulsive system approach. A Lyapunov stability proof ensures that the closed-loop system is asymptotically stable. Finally, we illustrate the effectiveness of the proposed solution compared to a time-triggered controller.