Neural network-based safe optimal robust control for affine nonlinear systems with unmatched disturbances q
Neural network-based safe optimal robust control for affine nonlinear systems with unmatched disturbances q
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DOI：
10.1016/j.neucom.2022.07.072
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发表时间：
2022-07-30
期刊：
影响因子：
6
通讯作者：
Zhang, Dehua
中科院分区：
文献类型：
--
作者：
Qin, Chunbin;Wang, Jinguang;Zhang, Dehua
针对具有不匹配扰动的安全关键系统，提出了一种基于神经网络的安全最优鲁棒控制方法，以确保安全关键系统在其安全区域内运行，并学习最优控制策略。成本函数是设计者的目标，通过增加控制屏障函数(CBF)来实现安全性和最优化。该方法不直接将安全性视为对系统状态的约束，而是通过安全惩罚机制来影响代价函数。使用一个附加函数来逼近非匹配扰动对安全临界系统的影响。在满足安全性和鲁棒性的前提下，利用神经网络逼近法学习最优控制策略。基于Lyapunov稳定性理论，证明了基于神经网络的安全最优鲁棒控制器能够保证闭环系统的所有信号最终一致有界。最后，给出了两个仿真实例，验证了该方法的有效性。(C)2022爱思唯尔公司版权所有。
In this paper, for the safety-critical systems with unmatched disturbances, a safe optimal robust control method based on neural network is proposed to ensure that the safety-critical system operates within its safe region and learns the optimal control strategy. The cost function which is the goal of the designers is augmented by a control barrier function (CBF) to achieve both safety and optimality. This method does not directly regard security as a constraint on the system state, but influences the cost function through a security penalty mechanism. An additional function is used to approximate the effect of the unmatched disturbances on the safety-critical systems. On the premise of satisfying the security and robustness, the neural network approximation method is used to learn the optimal control strategy. Based on Lyapunov stability theory, it is shown that the neural network-based safe optimal robust controller can guarantee all the signals of the resulting closed-loop systems to be uniformly ultimately bounded. Finally, two simulation examples are given to demonstrate the effectiveness of the proposed method.(c) 2022 Elsevier B.V. All rights reserved.