Model Predictive Control - Dynamic Matrix Control for MIMO Systems

Model Predictive Control (MPC) is a multivariable control strategy employing dynamic matrix control methodology for multi-input multi-output (MIMO) systems. This advanced control technique leverages system models and optimization algorithms to predict and regulate multiple input and output variables, delivering superior system performance and enhanced stability. Implementation typically involves constructing a dynamic matrix using step response coefficients, formulating quadratic programming problems to minimize cost functions over a prediction horizon, and applying constraints handling through active-set methods or interior-point algorithms. Key computational components include state estimation using Kalman filters, real-time optimization solvers, and feedback correction mechanisms. MPC finds extensive applications in industrial process control, chemical engineering, energy management systems, and robotics, providing precise multivariable coordination and improved operational efficiency. The controller's receding horizon approach ensures robust performance against disturbances while maintaining system constraints.