IEEE Trans Neural Netw Learn Syst. 2023 Jun;34(6):2732-2741. doi: 10.1109/TNNLS.2021.3107600. Epub 2023 Jun 1.
In this article, the problem of tracking control for a class of nonlinear time-varying full state constrained systems is investigated. By constructing the time-varying asymmetric barrier Lyapunov function (BLF) and combining it with the backstepping algorithm, the intelligent controller and adaptive law are developed. Neural networks (NNs) are utilized to approximate the uncertain function. It is well known that in the past research of nonlinear systems with state constraints, the state constraint boundary is either a constant or a time-varying function. In this article, the constraint boundaries both related to state and time are investigated, which makes the design of control algorithm more complex and difficult. Furthermore, by employing the Lyapunov stability analysis, it is proven that all signals in the closed-loop system are bounded and the time-varying full state constraints are not violated. In the end, the effectiveness of the control algorithm is verified by numerical simulation.
本文研究了一类非线性时变全状态约束系统的跟踪控制问题。通过构造时变非对称障碍李雅普诺夫函数（BLF），并结合反推算法，设计了智能控制器和自适应律。利用神经网络（NNs）来逼近不确定函数。众所周知，在过去的具有状态约束的非线性系统研究中，状态约束边界要么是常数，要么是时变函数。在本文中，研究了同时与状态和时间相关的约束边界，这使得控制算法的设计更加复杂和困难。此外，通过李雅普诺夫稳定性分析证明，闭环系统中的所有信号都是有界的，并且不会违反时变全状态约束。最后，通过数值模拟验证了控制算法的有效性。
