Barrier Lyapunov Function-Based Output Feedback Control for a Class of Nonlinear Systems with Constraints

Constraints limit system performance in real-world systems, especially nonlinear control, necessitating rigorous handling to prevent performance degradation and hazards.Aiming at the uncertain nonlinear systems with state constraints, an adaptive neural network output feedback control scheme is proposed based on barrier Lyapunov function (BLF) and backstepping design technique.The nonlinear state observer is designed to estimate the unmeasurable state of the system, and an adaptive neural network is used to approximate the continuous nonlinear unknown function.The BLF is employed to ensure that the system avoids the violation of state constraints.Meanwhile, combined with the dynamic surface control technique, the proposed control scheme can overcome the shortcomings inherent in the backstepping design method in the output feedback control design framework.Based on the Lyapunov theorem, it can be proved that the closed-loop system is stable in the sense that all the variables are guaranteed to be bounded, and the state constraints will not be violated.The performance of the observer and controller is verified by a simulation example of a single link manipulator.Experimental results show that the proposed state observer can estimate the state accurately, and the adaptive output feedback controller based on BLF can make the output track the desired reference trajectory under the condition of unknown system dynamics and unmeasurable system state, which verifies the feasibility and effectiveness of the proposed method.