MPC (Model-Predictive Control)

Model-Predictive Control (MPC) is an advanced method of process control that has become essential in many areas of automation and robotics. It involves the use of a mathematical model of the process to predict the future states of a system over a specified horizon and then optimizes the control inputs to achieve desired outcomes while respecting constraints. MPC is known for its ability to handle multivariable systems with constraints and has applications ranging from chemical process control to automotive engineering and power systems. Its predictive nature allows for anticipatory control actions, making it highly effective in managing complex dynamics and disturbances.

MPC gained prominence in the 1970s and 1980s as computational capabilities increased, allowing for the real-time solution of optimization problems. This period marked the transition from theoretical development to practical implementation in industrial settings.

Key contributors to the development of MPC include Eduardo F. Camacho and Carlos Bordons, among others, who have provided extensive research and publications that have furthered the understanding and application of this control strategy. Their work, along with advancements in computational power, has significantly contributed to the widespread adoption and continuous development of MPC in various industries.

MPC
Model-Predictive Control

Key Contributors

Newsletter

Academic Papers

Learning-based model predictive control: Toward safe learning in control

Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization

Comparison of deep reinforcement learning and model predictive control for adaptive cruise control

Reinforced model predictive control (RL-MPC) for building energy management

Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control

MPCModel-Predictive Control