Double-Layer Constraint Structure-Based Adaptive Neural Tracking Control for Nonlinear Strict-Feedback Systems

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.