Neuro-Adaptive Control with Given Performance Specifications for Strict Feedback Systems under Full-State Constraints

In this paper, we investigate the tracking control problem for a class of strict feedback systems with pregiven performance specifications as well as full-state constraints. Our focus is on developing a feasible neural network (NN)-based control method that is able to, under full-state constraints, force the tracking error to converge into a prescribed region within preset finite time and further reduce the error to a smaller and adjustable residual set, while confining the overshoot within predefined small level. Based on two consecutive error transformations governed by two auxiliary functions, named with behavior-shaping function and asymmetric scaling function, respectively, a novel approach to achieve given performance specifications is developed under certain bound condition on the transformed error, such condition, along with the full-stated constraints, is guaranteed by imbedding barrier Lyapunov function (BLF) into the back-stepping design. Furthermore, asymmetric output constraints are maintained with a single symmetric BLF, simplifying the procedure of stability analysis. All internal signals including the stimulating inputs to the NN unit are ensured to be bounded. Both theoretical analysis and numerical simulation verify the effectiveness and the benefits of the design.