Model-free event-triggered control algorithm for continuous-time linear systems with optimal performance
Model-free event-triggered control algorithm for continuous-time linear systems with optimal performance
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DOI：
10.1016/j.automatica.2017.03.013
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
K. Vamvoudakis;Henrique Ferraz
中科院分区：
文献类型：
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作者：
K. Vamvoudakis;Henrique Ferraz
针对连续时间线性系统，提出了一种新的无模型事件触发最优控制算法。该问题被表述为一个无限视界最优自适应学习问题，我们能够通过设计同时解决保证最优性能的控制和触发机制的设计问题。为了提供一个无模型的解决方案，我们采用了一个带有批评家网络的q学习框架来近似最优成本和一个零阶保持参与者网络来近似最优控制。由于我们有连续和离散时间演化的动力学，我们将闭环系统写成脉冲模型，并证明了其平衡点的渐近稳定性。最后对一个未知不稳定系统进行了数值仿真，验证了该方法的有效性。
This paper proposes a new model-free event-triggered optimal control algorithm for continuous-time linear systems. The problem is formulated as an infinite-horizon optimal adaptive learning problem, and we are able to simultaneously address the issue of designing a control and a triggering mechanism with guaranteed optimal performance by design. In order to provide a model-free solution, we adopt a Q-learning framework with a critic network to approximate the optimal cost and a zero-order hold actor network to approximate the optimal control. Since we have dynamics that evolve in continuous and discrete-time, we write the closed-loop system as an impulsive model and prove asymptotic stability of its equilibrium. Numerical simulation of an unknown unstable system is presented to show the efficacy of the proposed approach.