Study On The Method Of Adaptive Control For Nonlinear Systems With State Constraints

State constraints problem is an important research direction in the nonlinear control theory, as the state constraints are widespread in the actual system. To this end, this thesis studies the adaptive control method for the nonlinear systems with state constraints. The main works are as follows:(1) The research background and theory of knowledge are introduced, including the prior knowledge about adaptive control design and the approximation ability of neural network in nonlinear functions. The neural network approximation method, is used to solve the problem with the characteristics of multivariable, nonlinear and large amount of data.(2) In order to stabilize a class of uncertain nonlinear strict-feedback systems with full state constraints, an adaptive neural network control method is investigated. By introducing Barrier Lyapunov Function(BLF) to every step in backstepping procedure, a novel adaptive backstepping design is well developed to ensure that the full state constraints are not violated. The main advantage is less adaptive parameters under the state constraints. By making use of Lyapunov analysis, the tracking error, the adaptation law and the control input can be proved bounded, at the same time the state constraints are not violated. Finally, a simulation example is given to verify the effectiveness of the method.(3) For a strict feedback nonlinear system with full state constrains. An Integral Barrier Lyapunov Functionals(iBLF) is employed to study the adaptive backstepping technique. Under the proposed iBLF-based control, overcomes the conservatism compared with existing methods. Finally, all the signals of the closed-loop system are proved to be bounded and the tracking error can be ensured to a small neighborhood around zero. A simulation example is used to demonstrate the effectiveness of the proposed control scheme.