Adaptive Constraints Control For Nonlinear Systems And Its Applications

It is well known that due to safety and performance factors,the actual system is inevitably limited,and some states of the system are required to operate within a certain boundary in both transient and steady state,that is,to satisfy certain constraints.When these constraint restrictions are ignored,it is very likely that the system state "out of bounds" will occur,and result in serious accidents.Therefore,research on system state constraints is necessary.Based on the above situation,this thesis studies the following three aspects:(1)An adaptive neural network(NN)tracking control method for a class of uncertain nonlinear strict-feedback systems with time-varying full-state constraints.As we all know,the actual systems are inevitably constrained because of the safety and performance factors.The proposed control method has the following characteristics: in order to ensure that the states do not violate the asymmetric time-varying constraint regions,the adaptive NN controller is constructed by introducing the asymmetric time-varying Barrier Lyapunov function(TVBLF);the amount of the minimum learning parameters is reduced by introducing a TVBLF at each step of the backstepping.Based on Lyapunov stability analysis,it can be proven that all the signals in the closed-loop system can be shown to be semi-global ultimately uniformly bounded(SGUUB)and the time-varying full-state constraints are never violated.Finally,a numerical simulation is given and the effectiveness of this adaptive control method is verified.(2)An adaptive NN constraint control method is studied for a class of uncertain nonlinear non-strict feedback systems with state constraints.The restrictive assumption that the unknown internal dynamics must possess the monotonically increasing characteristics in previous results is removed.The property of radial basis function(RBF)NNs are used to solve the algebraic loop problem based on the approximation function.In order to achieve full state constraint satisfactions,Barrier Lyapunov functions(BLFs)are employed in each design procedure.Based on the backstepping and fewer adjustable parameters techniques,the controllers and the adaptive laws are obtained.By using Lyapunov stability theory,the boundedness of all signals in the closed-loop system is proved.The proposed control scheme not only solves the stability problem of the non-strict feedback system,but also overcomes the influence of the state constraint on the control performance.Finally,the effectiveness of the control method is verified by a simulation example.(3)An adaptive NN constraint control method is designed for a class of DC motor nonlinear systems with time-varying output constraints.Based on the dynamics system of DC motor,a nonlinear model of the system with modeling uncertainty is established.Then,a Lyapunov function which satisfies the output constraint system is constructed.An adaptive neural network constraint control method is designed based on RBFNN.This method ensures that the output satisfies the constraints and ensures that all signals in the closed-loop system are bounded.At the same time,the design method is extended to a DC motor system with full state constraints.To guarantee state constraints always remain in the asymmetric time-varying constraint regions,the asymmetric time-varying BLFs are employed to structure an adaptive NN controller.Using Lyapunov analysis,all signals in the closed-loop system are proved to be bounded and the constraints are not violated.The effectiveness of the control method is demonstrated by simulation results.