Full state constraints-based adaptive tracking control for uncertain nonlinear stochastic systems with input saturation

This paper focuses on the adaptive tracking control problem for uncertain nonlinear stochastic systems subject to input saturation under full state constraints. Different from existing works, we propose a novel predefined performance control algorithm to deal with the full state constraint problem. First, a new asymmetric tan-type barrier Lyapunov function is developed to pre-constrain the transformed state variables, which can render that those state variables are strictly constrained within asymmetric upper and lower boundaries. Second, a new full-order high gain compensation system is constructed to eliminate the influence caused by the input saturation characteristic. Subsequently, by the use of backstepping control method, a smooth predefined tracking controller with an adaptive law is designed for the stochastic system. Moreover, based on the Lyapunov stability theory, it is proved that all the signals of the resulting closed-loop system with the designed controller are bounded almost surely and all state variables can be constrained within asymmetric boundaries. Finally, a simulation example is presented to verify the effectiveness of proposed predefined performance control algorithm. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.