Identifier-based adaptive neural dynamic surface control for uncertain DC–DC buck converter system with input constraint

In this paper, an identifier-based adaptive neural dynamic surface control (IANDSC) is proposed for the uncertain DC–DC buck converter system with input constraint. Based on the analysis of the effect of input constraint in the buck converter, the neural network compensator is employed to ensure the controller output within the permissible range. Subsequently, the constrained adaptive control scheme combined with the neural network compensator is developed for the buck converter with uncertain load current. In this scheme, a newly presented finite-time identifier is utilized to accelerate the parameter tuning process and to heighten the accuracy of parameter estimation. By utilizing the adaptive dynamic surface control (ADSC) technique, the problem of “explosion of complexity” inherently in the traditional adaptive backstepping design can be overcome. The proposed control law can guarantee the uniformly ultimate boundedness of all signals in the closed-loop system via Lyapunov synthesis. Numerical simulations are provided to illustrate the effectiveness of the proposed control method.

► The state-space averaging model is built by load current and ESR of the capacitor.
► The model can describe the system response where load resistance varies largely.
► New finite-time identifier is employed in the adaptive dynamic surface control.
► Neural network compensator is designed to constrain the control input within [0, 1].
► Control response speed is enhanced when control parameter is set inappropriately.