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Model Predictive Control Toolbox


The Model Predictive Control Toolbox is a MATLAB toolbox that provides a comprehensive set of tools and functions for designing, simulating, and deploying model predictive control (MPC) systems. Key Features:

  1. MPC Design: The toolbox provides a range of functions for designing MPC controllers, including model identification, controller tuning, and performance analysis.
  2. Modeling and Simulation: The toolbox includes tools for modeling and simulating MPC systems, including linear and nonlinear models, and simulation of system behavior.
  3. Optimization: The toolbox provides advanced optimization algorithms for optimizing MPC performance, minimizing costs, and maximizing efficiency.
  4. Real-Time Deployment: The toolbox supports real-time deployment of MPC systems, enabling users to deploy controllers in real-world applications.


  1. Improved Process Efficiency: The MPC Toolbox enables users to improve process efficiency, reduce waste, and optimize performance.
  2. Advanced Control Capabilities: The toolbox provides advanced control capabilities, enabling users to tackle complex control problems and optimize system behavior.
  3. Easy to Use: The MPC Toolbox is designed to be easy to use, even for those without extensive control systems expertise.

Supported MATLAB Functions:

  1. mpc: The toolbox provides a range of functions for designing and simulating MPC controllers, including the mpc function for creating and simulating MPC controllers.
  2. mpcmove: The mpcmove function is used to move the MPC controller to a new operating point, and to simulate the response of the system to changes in the operating point.
  3. mpcquadprog: The mpcquadprog function is used to solve quadratic programming problems, which are often used in MPC optimization