Subspace-based Model Predictive Control of time-varying systems

This paper presents an approach to constrained Subspace-based Model Predictive Control (SMPC) of time-varying systems. The central ideas are to find the predictive control law recursively using a subspace identification technology, and to update the control law once a plant-model mismatch is detected. Within this framework, the proposed control law ensures that enough excitation is applied to the system when mismatch occurs, without violating the control constraints. Additionally, an implementation of a variable forgetting factor is used to facilitate faster convergence when plant changes. A simulation example is used to demonstrate the efficacy of the proposed approach.