Double-Layer Constraint Structure-Based Adaptive Neural Tracking Control for Nonlinear Strict-Feedback Systems
针对跟踪误差和全状态受时变约束且控制方向未知的非线性系统，提出一种结合非线性映射和双层约束结构的自适应神经控制策略，利用径向基神经网络辨识未知动态，通过动态面控制降低计算量，并采用Nussbaum增益技术处理未知控制方向，数值算例验证了方法的有效性。
For a category of nonlinear systems subject to time-varying constraints on tracking error and full states and unknown control directions, this article develops an adaptive neural control strategy using nonlinear mapping and double-layer constraint structure. Radial basis function neural network is applied to identify the unknown system dynamics. The dynamic surface control with less learning parameters is employed to eliminate “explosion of complexity” and reduce online computation burden. The Nussbaum gain technique is employed to deal with the unknown control direction. The nonlinear mapping is applied to ensure the satisfaction of the multiple constraints on state variables and remove feasibility conditions on virtual control signals. Double-layer constraint boundaries are incorporated into controller design process, the inside boundaries are utilized to cope with the multiple state constraints, and the outside boundaries are used into controller and adaptive law design. Hence, the singularity problem caused by the system state approaching the bound boundary is completely solved. The numerical example is used to deduce the availability of developed control approach.