Receding Horizon Optimization for Disturbance-aware Predictive Control of Power Electronic Inverters

TL;DR

This paper introduces a disturbance-aware predictive control method for power inverters using receding horizon optimization, hybrid automaton modeling, and online disturbance estimation, enhancing adaptive control performance.

Contribution

It presents a novel receding horizon control framework for inverters that incorporates disturbance estimation and hybrid automaton modeling, enabling adaptive and flexible control modes.

Findings

The proposed control strategy effectively manages load-shift disturbances.

ODCM outperforms OPCM in control performance.

The framework supports multiple control modes with potential for broader applications.

Abstract

A disturbance-aware predictive control policy is proposed for DC-AC power inverters with the 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 approach is applied to solve the discrete optimization problem piece-wisely on-line. Accordingly, disturbance-aware adaptive control is proposed, the external disturbance is sampled and estimated by an on-line Recursive Least Square (RLS) algorithm. Then 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 an example. The proposed controller can freely operate under the original Optimal Discrete Control Mode (ODCM) and the OPCM. The 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 the potential to support other forms of control modes.