Model Predictive control

资 源 简 介

Model Predictive control

详 情 说 明

Model Predictive Control (MPC) is an advanced control strategy that utilizes mathematical models to predict the behavior of a system and determine the best control actions to achieve desired performance. This technique is particularly useful in systems that are subject to constraints, as it allows for optimization of the control actions in real-time, while ensuring that the constraints are satisfied.

MPC is widely used in various industries, including chemical, aerospace, automotive, and robotics. It has proven to be effective in controlling complex systems, such as chemical processes, aircraft flight, and vehicle dynamics, among others.

The basic principle of MPC involves creating a mathematical model of the system, which is then used to predict the behavior of the system over a specified horizon. Based on this prediction, an optimal control strategy is determined that minimizes a cost function, subject to constraints. This strategy is then implemented in real-time, and the process is repeated at each time step to continuously optimize the control actions.

MPC has several advantages over traditional control strategies, including its ability to handle constraints, its ability to handle nonlinear systems, and its ability to handle time-varying systems. However, it also has some limitations, such as the need for accurate models, which can be difficult to obtain in practice. Nevertheless, MPC is a powerful tool that has revolutionized control engineering, and its applications are only expected to grow in the future.