Hybrid automaton-based disturbance-aware predictive control with receding horizon optimization for three-phase full-bridge inverters

Abstract

A data-driven disturbance-aware predictive control policy is proposed for DC–AC power inverters based on receding horizon optimization approach. First a discrete event-driven hybrid automaton model has been constructed for the nonlinear inverter system dynamics. A control problem of infinite discrete state-space transition sequence optimization is formulated. A receding-horizon-optimization-based hybrid controller is designed to solve the discrete optimization problem piece-wisely on-line. Accordingly, a disturbance-aware adaptive control is proposed, the external disturbance is sampled and estimated by an on-line Recursive Least Square (RLS) algorithm. It is elaborated that the conventional PWM control solution is a subset of solutions of the proposed control strategy and the code-transition between them is provided. By adding extra PWM constraints to the proposed control strategy, an Optimal PWM Control Mode (OPCM) is introduced as example. The essence of OPCM is still a data-driven optimal solution based on MPC following pre-designated PWM constraints which is a modified sub-mode of ODCM and greatly reduces computational requirement. The proposed controller can freely operate under the original Optimal Discrete Control Mode (ODCM) and the OPCM. Numerical simulation results have verified that the proposed discrete control strategy has realized disturbance-aware adaptive control of DC–AC inversion against load-shift, and ODCM has better control performance than OPCM. In addition, the proposed modeling and control frame has potential to support other forms of control modes.