Adaptive Control Of Uncertain Nonlinear Systems With State Constraints And Its Application

As the main factor of limiting the system performance,constraints often appear in the real industrial process unavoidably.Hence,the research of state or output constraints is particularly important in nonlinear control fields.Combined with obstacle Lyapunov function and backstepping method,the following four aspects are studied for nonlinear system:(1)For a class of strict feedback nonlinear systems with all state constraints,the adaptive dead time compensation control method is studied.Firstly,in order to deal with the unknown function in the nonlinear system,the neural network adaptive control method is introduced.Secondly,the barrier Lyapunov functions and backstepping process are introduced to ensure that the full state constraint is implemented.Then,delay constraints are introduced to solve the problem of initial state uncertainty.Simulation results show that the developed method is effective.(2)This article investigates an adaptive finite-time neural control for a class of strict feedback nonlinear systems with multiple objective constraints.Firstly,in order to solve the main challenges brought by the state constraints and the emergence of finite-time stability,a new barrier Lyapunov function is proposed,not only can it solve multi-objective constraints effectively but also ensure that all states are always within the constraint intervals.Secondly,by combining the command filter method and backstepping control,the adaptive controller is designed.What is more,the proposed controller has the ability to avoid the “singularity” problem.The compensation mechanism is introduced to neutralize the error appearing in the filtering process.Furthermore,error compensation mechanism is introduced to eliminate the errors in the filtering process.Finally,a simulation example of electro-mechanical dynamic system is given to prove the effectiveness of the proposed control strategy.(3)A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints.Firstly,the use of an integral barrier Lyapunov function not only ensures that all states are within the bounds of the constraint,but also mixes the states and errors to directly constrain the state,reducing the conservativeness of the constraint satisfaction condition.Secondly,the fuzzy logic system is used to design the approximator,which deals with uncertain and continuous functions in the process of backstepping design.Meanwhile,according to Lyapunov stability theory,it is proved that all the signals in the closed-loop system are bounded and all the states do not violate the constraint bounds.The simulation results prove the effectiveness of the proposed control scheme.(4)This article investigates an adaptive neural network control algorithm for marine surface vessels with time-varying output constraints and unknown external disturbances.Firstly,the barrier Lyapunov function is used to achieve time-varying output constraints.Secondly,the nonlinear state-dependent transformation is introduced to eliminate the feasibility conditions of virtual controller.Subsequently,the disturbance observer is structured to observe time-varying constraints and unknown external disturbances,Lyapunov stability theorem is used to verify that all signals in the closed-loop system are bounded.Finally,the simulation results verify the benefit of the proposed method.