Adaptive neural network control of unknown nonlinear affine systems with input deadzone and output constraint. (September 2015) - He, Wei; Dong, Yiting; Sun, Changyin
Abstract: In this paper, we aim to solve the control problem of nonlinear affine systems, under the condition of the input deadzone and output constraint with the external unknown disturbance. To eliminate the effects of the input deadzone, a Radial Basis Function Neural Network (RBFNN) is introduced to compensate for the negative impact of input deadzone. Meanwhile, we design a barrier Lyapunov function to ensure that the output parameters are restricted. In support of the barrier Lyapunov method, we build an adaptive neural network controller based on state feedback and output feedback methods. The stability of the closed-loop system is proven via the Lyapunov method and the performance of the expected effects is verified in simulation. Abstract : Highlights: A practical controller is provided with the incorporation of Barrier Lyapunov Functions for a class of nonlinear affine systems with deadzone and constraint on the output parameter, which can guarantee desirable tracking performance based on the fact that the unknown deadzone parameters, constrained boundary, and external disturbance have been taken into account together. An approach for a class of uncertain nonlinear systems is proposed to tackle the control design problem under the condition that there exist both input deadzone and output constraint in the nonlinear systems. Meanwhile, the stability of closed-loop system can be ensured on the support of Lyapunov stability theorem. The bounds of NN approximation errors, NN weights and radial basis functions are not necessarily to be known for control design in the design procession according to construct adapting parameters to estimate these unknown bounds online.