IMMPC: An Internal Model Based MPC for Rejecting Unknown Disturbances

TL;DR

This paper introduces an internal model based MPC that effectively rejects unknown disturbances by leveraging known disturbance dynamics, ensuring constraint satisfaction and stability in control systems.

Contribution

The paper proposes a novel MPC scheme based on the internal model principle that handles unknown disturbances by incorporating disturbance dynamics into the control design.

Findings

Successfully rejects unknown disturbances in a four-tank system

Ensures constraint satisfaction and stability under disturbance conditions

Reformulates output regulation as a stability problem

Abstract

Model predictive control (MPC) is a powerful control method that allows to directly include state and input constraints into the controller design. However, errors in the model, e.g., caused by unknown disturbances, can lead to constraint violation, loss of feasibility and deteriorate closed-loop performance. In this paper, we propose a new MPC scheme based on the internal model principle. This enables the MPC to reject unknown disturbances provided that the dynamics of the linear signal generator are known. We reformulate the output regulation problem as a stability problem, to ensure feasibility, constraint satisfaction, and convergence to the optimal reachable setpoint. The controller is validated on a real fourtank system.