Abstract
In order to solve the control problem of uncertain nonlinear systems with state constraints, a dynamic surface output feedback control technology based on Radial Basis Function (RBF) neural networks state observer is proposed. The state observer is designed to estimate the unknown state of the systems by using the approximation characteristics of RBF neural networks, and to constrain the system state by using the Barrier Lyapunov Function (BLF). Based on the backstepping control, a first-order low-pass filter is introduced to design a dynamic surface control (DCS), which solves the “differential explosion” phenomenon that can easily occur in backstepping control. Finally, the stability of the closed-loop system which is confirmed by the Lyapunov method guarantees the semi-globally uniformly ultimately boundedness (SGUUB) of all the signals. The effectiveness of the methods that the boundedness of the tracking errors, the observer states and the controllers can be guaranteed, and good control performance could be achieved is shown by simulation results.
Highlights
INTRODUCTIONThe characteristics and effects of nonlinear factors are inevitable
In practical engineering, the characteristics and effects of nonlinear factors are inevitable
Motivated by the above mentioned researches, in this paper, a dynamic surface output feedback control technology based on Radial Basis Function (RBF) neural networks state observer is studied for a class of nonlinear systems with unknown functions to solve the constraint control problem of this system
Summary
The characteristics and effects of nonlinear factors are inevitable. Swaroop et al proposed a dynamic surface control (DSC) method in [5] It introduces a firstorder low-pass filter to calculate the derivative of the virtual control and eliminates the ‘‘differential explosion’’ phenomenon which can be generated in the backstepping control, simplifying the controller and calculation amount, and achieving great results in many practical engineering problems. Many researches have made some achievements in nonlinear constrained systems, there are still some significant issues to be solved [16]–[19] Such as how to effectively solve the output feedback control problem of nonlinear systems with full state constraints and unknown functions. Motivated by the above mentioned researches, in this paper, a dynamic surface output feedback control technology based on RBF neural networks state observer is studied for a class of nonlinear systems with unknown functions to solve the constraint control problem of this system. Based on the Lyapunov stability theory, the semiglobally uniformly boundedness (SGUUB) of all the signals of the closed-loop system are proven, and the effectiveness of the method is demonstrated by a simulation example
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