Control And Performances Analysis For Several Classes Of Nonlinear Systems With State Constraints

Nonlinear systems control theory has been one of the hot topics in the field of automatic control in recent decades.Especially,adaptive backstepping method based on neural network and fuzzy approximation for uncertain nonlinear systems have been made great progress.However,there are still a lot of problems to be further studied and explored.Based on the backstepping method,this paper will focuses on the problem of adaptive control for pure feedback systems and non-triangular form systems with state constraints.The state feedback and output feedback of several nonlinear systems are studied by combining the adaptive control theory with neural network and fuzzy logic systems approximation theory,the decentralized control theory of interconnected large-scale systems,the stability theory of stochastic differential equations and the control theory with constraints.The state feedback and output feedback of several constrained systems are studied in detail.The main works of this paper are listed as follow:1.The fixed time control problem for a class of pure feedback system with full state constraints is studied.Firstly,the mean value theorem is used to transform the pure feedback system into a strict feedback system.Secondly,the BLF is used to deal with the full state constraint.The fuzzy adaptive control method is proposed by combining FLS with backstepping technology.The fuzzy adaptive controller guarantees that all closed-loop signals are bounded at a fixed time and all state constraint are satisfied.2.An adaptive output feedback control strategy for a class of uncertain time-varying delay non-triangular form systems with full state constraints is developed.For unmeasurable states of the systems,the state observer is designed,the separating variable method is used to overcome the non-triangular form structure difficult,the dynamic surface control(DSC)is used to eliminate the problem of "computational expansion" in design procedure,and the neural networks is adopted to approximate the uncertain nonlinear function in the control design process.A new adaptive neural controller is proposed and proof that all the closed loop signals are ultimately bounded and the full state constraint are not violated.3.For a class of uncertain stochastic non-triangular form systems with dead zero input and unknown control directions,the problem of full state constraints control is studied.In this chapter,dominant method based on the fuzzy basis function is adopted to overcome the nontriangular structure difficult,in this way,the nonlinear functions in some literatures need to satisfy the requirement of monotonically increasing bounded function hypothesis be removed and have a smaller conservative.The Nussbaum-type function is employed to detect the control direction in the control design process.The adaptive fuzzy controller is designed,it can guarantee that all the closed loop signals are ultimately bounded in probability and the full state constraint are not violated.4.Two kinds of adaptive control problems for uncertain non-triangular form interconnect systems with full state constraints are studied.The dominant method is employed to overcome the non-triangular form structure of controlled systems,the BLF and time varying BLF are adopted to deal with the full state constraint problems.Decentralized controllers are designed and they can guarantee that all closed-loop signals are bounded and the full state constraint are satisfied of subsystems.5.For a class of uncertain non-triangular systems,the asymmetric time-varying full state constraints control problem is investigated.The dominant method is adopted to overcome the problem of non-triangular structure difficult,the ABLF is used to deal with the timevarying full-state constraint problem.The adaptive fuzzy controller is presented and it can guarantee that all closed-loop signals are ultimately bounded and full state constraint are met.