Adaptive Control For Nonlinear Systems With Output And State Constraints And Its Applications

Practical engineering control systems are often subject to various forms of constraints because of the environment limitations of the controlled objects,the influence of physical limitation of control system components or consider the security factors,which cause destruction of system equipment and even resulting in serious casualties when these constraints are not considered.At the same time,due to the existences of the constraint characteristics will make the controller design of the nonlinear systems to be more difficult and complex,so the constraint control of nonlinear systems also gradually become the hot and difficult problem in the field of control.This paper mainly studies the following three aspects:(1)An adaptive neural network approximation method is proposed for nonlinear uncertain robot systems with time-varying output constraints.The tracking performance and stable performance of the system are guaranteed by constructing the new time-varying Barrier Lyapunov Function and designing an adaptive controller.The unknown function of the controlled object is approximated by neural network.At the same time,the theory is extended to the robot system with time-varying state constraints.In both cases,constraint conditions are not violated based on the Lyapunov function analysis of the system stability.The simulation of nonlinear robot system is studied to verify the effectiveness of the proposed control method.(2)An adaptive control method is proposed for nonlinear stochastic systems with full-state constraints.This method does not only overcomers the difficulty of the stochastic system in achieving stability,but also simplifies the controller design.By constructing the symmetric and asymmetri forms of Barrier Lyapunov Functions,it can be obtained that the all state boundaries of the stochastic systems are not violated,and the effect of state constraint on system performance is overcome.A simulation example is performed to demonstrate the effectiveness of this method.An control method is proposed for a class of uncertain nonlinear MIMO coupled systems based on fuzzy adaptive method.The fuzzy logic system is used to approximate the unknown function.Based on the decoupling Backstepping method,the controller and adaptive law are designed to stabilize the system.It makes sure that all states are constrained in a corresponding compact set and the nonlinear control problem which difficult to achieve stability due to coupling structure is solved.The simulation of Continuous Stirred Tank Reactors system and numerical simulation experiment is researched to verify the feasibility of the proposed adaptive control method.(3)An adaptive control method is first proposed for a class of nonlinear uncertain parameterized strict-feedback system with time-varying state constraints.In each step of Backstepping design,by constructing time-varying asymmetrical Barrier Lyapunov function,all state variables are bounded within the time-varying constraints.The tracking performance of system output variables is guaranteed by the Backstepping design controller.The simulation example verifies the effectiveness of the adaptive control method.In addition,the proposed time-varying state constraint adaptive control theory is used to solve the neural network control problem of nonlinear hydraulic servo system.According to previous studies,the constraint of the system can improve the tracking precision of the nonlinear system.The Backstepping algorithm is used to design an adaptive controller,and the uncertainty function is estimated by the neural network.By analyzing the stability of the system,it is concluded that all signals of the closed-loop system are bounded and the time-varying state constraints are not violated.The simulation results of the hydraulic servo system show that the proposed method can improve the tracking performance of the controller and the robustness of the system.