Flywheel Control Using Model Predictive Controller: Implementation Example

This article presents a comprehensive implementation example of a flywheel system controlled by a Model Predictive Controller (MPC). The flywheel serves numerous applications including power systems, aerospace, marine vessels, and railway systems. Its primary function involves energy storage and release while providing stable power and kinetic energy output. Due to its high efficiency and reliability, flywheel technology is becoming an increasingly popular energy storage solution. The core implementation employs MPC as an advanced control technique that optimizes system performance by predicting future behavior through mathematical modeling. Typically, the control algorithm involves: - System identification to develop state-space models representing flywheel dynamics - Cost function formulation with constraints on speed, torque, and energy limits - Quadratic programming optimization solved at each control interval Key MATLAB functions commonly implemented include: mpc() for controller object creation setConstraints() for operational boundaries predict() for future state projections Applying MPC to flywheel control significantly enhances performance metrics through: - Predictive speed regulation preventing overshoot - Optimal torque management during charge/discharge cycles - Real-time adaptation to load variations The controller architecture typically samples system states (rotational speed, power flow) and computes optimal control inputs (motor torque) using receding horizon optimization, ensuring efficient energy management while maintaining mechanical constraints.