Adaptive Neural Hierarchical Sliding Mode Control of Nonstrict-Feedback Nonlinear Systems and an Application to Electronic Circuits
提出一种自适应滑模控制方法，用于一类非严格反馈非线性系统，放松了系统结构的常见限制，并通过分层设计避免传统反步法的因果问题，仿真验证了有效性。
This paper proposes an adaptive sliding mode control method for a class of nonstrict-feedback nonlinear systems where some widely used restrictions on system structure are relaxed. Based on the calculus principle, the original system is first transformed into a new defined system. Then, by using sliding mode control technology and the concept of hierarchical design, a series of control signals are sequentially designed for the new defined system where radial basis function neural networks are used to approximate the unknown functions. Based on the Lyapunov stability theory, the closed-loop system together with the proposed sliding surfaces is proved to be uniformly ultimately bounded under our designed adaptive neural controller. The main contributions of this paper lie in that some strict restrictions on uncertain system functions are removed; a hierarchical control method is proposed for the considered systems, which can avoid the problem of “causes and consequences” that may be encountered by using traditional backstepping design method; and the proposed control method is also available for underactuated nonlinear systems. Finally, simulation results demonstrate the effectiveness of the proposed design techniques.