Dahal Raju, Kar Indrani
Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India.
Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India.
Neural Netw. 2025 Nov;191:107752. doi: 10.1016/j.neunet.2025.107752. Epub 2025 Jun 21.
This paper addresses the robust tracking control problem for nonlinear systems with unmatched uncertainties and partially unknown dynamics while also taking into account the input and state constraints. An event-triggered ADP framework is utilized to tackle this issue. Initially, an identifier neural network (NN) is designed to estimate the unknown system dynamics. Next, an augmented system is constructed using the reference trajectory and tracking error. The uncertainty is then divided into matched and unmatched components, converting the tracking control problem into an optimal regulation problem for an auxiliary system. A novel event-triggered safe HJB equation is developed by integrating a control barrier function (CBF) and a nonquadratic term within the cost function to enforce the safety constraints. A critic NN is utilized to solve this safe HJB equation. The controller is updated based on a triggering rule formulated using the Lyapunov approach. Lyapunov stability theory is applied to demonstrate that the closed-loop system is stable and that the identifier network and the critic network parameters remain uniformly ultimately bounded (UUB) under constraints and disturbances. The effectiveness of the proposed theoretical approach is validated using a simulation example.
本文研究了具有不匹配不确定性和部分未知动态特性的非线性系统的鲁棒跟踪控制问题，同时考虑了输入和状态约束。利用事件触发自适应动态规划（ADP）框架来解决此问题。首先，设计一个辨识神经网络（NN）来估计未知的系统动态特性。接下来，使用参考轨迹和跟踪误差构建一个增广系统。然后将不确定性分为匹配和不匹配分量，将跟踪控制问题转化为辅助系统的最优调节问题。通过在代价函数中集成控制障碍函数（CBF）和非二次项来开发一个新颖的事件触发安全哈密顿-雅可比-贝尔曼（HJB）方程，以强制执行安全约束。利用一个批评神经网络来求解这个安全HJB方程。基于使用李雅普诺夫方法制定的触发规则更新控制器。应用李雅普诺夫稳定性理论证明闭环系统是稳定的，并且在约束和干扰下，辨识网络和批评网络的参数保持一致最终有界（UUB）。通过一个仿真例子验证了所提出理论方法的有效性。
