a good example of flywheel controlled by model predictive controller

资 源 简 介

a good example of flywheel controlled by model predictive controller

详 情 说 明

Flywheels serve as an efficient energy storage solution in various applications, from stabilizing power grids to hybrid vehicles. Model Predictive Control (MPC) is particularly well-suited for flywheel control due to its ability to handle constraints and optimize performance dynamically.

In this context, MPC works by predicting the flywheel’s future behavior over a finite time horizon and computing optimal control inputs to maintain desired speed or torque while respecting physical limits such as maximum rotational speed or energy storage capacity. The controller continuously adjusts predictions based on real-time feedback, ensuring precise regulation even under varying load conditions.

A practical example could be a flywheel-based energy recovery system in motorsports. Here, MPC ensures rapid energy absorption during braking (regenerative deceleration) and smooth power delivery during acceleration—all while preventing overspeed conditions that could damage the flywheel assembly. The optimization aspect minimizes mechanical stress, extending component lifespan.

Key advantages include: Constraint Handling – Explicitly accounts for speed/force limits. Multi-Objective Optimization – Balances response time, efficiency, and wear. Adaptability – Compensates for disturbances like friction changes.

This approach demonstrates MPC’s strength in high-performance systems where traditional PID controllers might struggle with nonlinearities or tight operational bounds.