Adaptive critic learning for event‐triggered safe control of nonlinear safety‐critical systems
Adaptive critic learning for event‐triggered safe control of nonlinear safety‐critical systems
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DOI：
10.1002/asjc.3049
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发表时间：
2023-02
影响因子：
2.4
通讯作者：
C. Qin;Heyang Zhu;Jinguang Wang;Yandong Hou;Shaolin Hu;Dehua Zhang;Qiyang Xiao
中科院分区：
文献类型：
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作者：
C. Qin;Heyang Zhu;Jinguang Wang;Yandong Hou;Shaolin Hu;Dehua Zhang;Qiyang Xiao
针对一类非线性安全临界系统，提出了一种基于自适应评价学习（ACL）的事件触发安全控制方法.首先，在传统的二次型成本函数基础上增加控制障碍函数（CBF），构造安全成本函数，解决了经典ACL方法难以处理的安全约束优化问题。随后，引入事件触发方案以减少计算量。进一步，结合CBF的性质和基于ACL的事件触发机制，推导了事件触发安全的Hamilton-Jacobi-Bellman（HJB）方程，并构造了一个单批评神经网络（NN）框架来逼近事件触发安全的HJB方程的解.此外，并行学习方法被应用到神经网络的学习过程中，使持续激励（PE）的条件是不需要的。利用李雅普诺夫理论证明了神经网络的权值逼近误差和系统的状态在安全集中一致最终有界。最后，通过仿真验证了该方法的有效性.
In this paper, an event‐triggered safe control method based on adaptive critic learning (ACL) is proposed for a class of nonlinear safety‐critical systems. First, a safe cost function is constructed by adding a control barrier function (CBF) to the traditional quadratic cost function; the optimization problem with safety constraints that is difficult to deal with by classical ACL methods is solved. Subsequently, the event‐triggered scheme is introduced to reduce the amount of computation. Further, combining the properties of CBF with the ACL‐based event‐triggering mechanism, the event‐triggered safe Hamilton–Jacobi–Bellman (HJB) equation is derived, and a single critic neural network (NN) framework is constructed to approximate the solution of the event‐triggered safe HJB equation. In addition, the concurrent learning method is applied to the NN learning process, so that the persistence of excitation (PE) condition is not required. The weight approximation error of the NN and the states of the system are proven to be uniformly ultimately bounded (UUB) in the safe set with the Lyapunov theory. Finally, the availability of the presented method can be validated through the simulation.