Nonlinear MPC for Feedback-Interconnected Systems: a Suboptimal and Reduced-Order Model Approach
This work addresses control design for complex systems with unmodeled dynamics or low-level compensators, offering a computationally efficient solution, though it is incremental as it builds on existing MPC frameworks.
The paper tackles the challenge of applying Model Predictive Control (MPC) to feedback-interconnected systems by proposing a suboptimal and reduced-order MPC architecture, proving global exponential stability for the closed-loop system and validating it with simulations on a mechatronic pendulum system.
In this paper, we propose a suboptimal and reduced-order Model Predictive Control (MPC) architecture for discrete-time feedback-interconnected systems. The numerical MPC solver: (i) acts suboptimally, performing only a finite number of optimization iterations at each sampling instant, and (ii) relies only on a reduced-order model that neglects part of the system dynamics, either due to unmodeled effects or the presence of a low-level compensator. We prove that the closed-loop system resulting from the interconnection of the suboptimal and reduced-order MPC optimizer with the full-order plant has a globally exponentially stable equilibrium point. Specifically, we employ timescale separation arguments to characterize the interaction between the components of the feedback-interconnected system. The analysis relies on an appropriately tuned timescale parameter accounting for how fast the system dynamics are sampled. The theoretical results are validated through numerical simulations on a mechatronic system consisting of a pendulum actuated by a DC motor.