Observer-Based Adaptive Neural Output Feedback Constraint Controller Design for Switched Systems Under Average Dwell Time

Aiming at a class of switched uncertain nonlinear strict-feedback systems under the action of average dwell time switching signal, this paper proposes a novel adaptive neural network output feedback tracking control based on the consideration of the full state constraints. The controller is proposed based on neural networks. One of the key characteristics of the system discussed is that the state variables cannot be measured and the system states need to be kept within the constraint ranges. For the sake of estimating the unmeasured states, the observer is constructed. In order to ensure all states which are within the time-varying boundary, the tangent barrier Lyapunov function (BLF-Tan) is selected in the design process. The boundedness of the closed-loop signals with average dwell time is guaranteed by the designed controllers and all the states limit in their constrained sets. It has been proved that the output tracking error converge to a small neighborhood of zero. In addition, the significance of the presented control strategy is verified and tested by a simulation example.

Automated summary

This paper proposes a novel adaptive neural network output feedback tracking control method based on full state constraints, where an observer is constructed to estimate unmeasured states and a tangent barrier Lyapunov function (BLF-Tan) is selected to ensure all states within the time-varying boundary. The designed controllers guarantee the boundedness of closed-loop signals with average dwell time and ensure all states remain within their constraints, with output tracking error converging to a small neighborhood of zero.