MPC Model Predictive Control Algorithm

In this article, we will explore the MPC (Model Predictive Control) algorithm, an advanced control strategy widely used in control systems that combines model prediction and optimization techniques. By implementing MPC, control systems can better respond to system requirements, thereby improving both performance and reliability. The core algorithm typically involves three key steps: predicting future system behavior using a dynamic model, optimizing control actions over a prediction horizon through cost function minimization, and applying the first control input while recalculating at each time step. If you're unfamiliar with model predictive control concepts, don't worry - we'll introduce its fundamental principles and application scenarios in this article. Code implementations often utilize optimization solvers like quadratic programming (QP) for the optimization step and require careful tuning of prediction horizons and constraint handling. We hope this article helps you better understand the applications and advantages of MPC predictive control algorithms, including practical implementation considerations for real-world systems.