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:
- MPC Design: The toolbox provides a range of functions for designing MPC controllers, including model identification, controller tuning, and performance analysis.
- Modeling and Simulation: The toolbox includes tools for modeling and simulating MPC systems, including linear and nonlinear models, and simulation of system behavior.
- Optimization: The toolbox provides advanced optimization algorithms for optimizing MPC performance, minimizing costs, and maximizing efficiency.
- Real-Time Deployment: The toolbox supports real-time deployment of MPC systems, enabling users to deploy controllers in real-world applications.
Benefits:
- Improved Process Efficiency: The MPC Toolbox enables users to improve process efficiency, reduce waste, and optimize performance.
- Advanced Control Capabilities: The toolbox provides advanced control capabilities, enabling users to tackle complex control problems and optimize system behavior.
- Easy to Use: The MPC Toolbox is designed to be easy to use, even for those without extensive control systems expertise.
Supported MATLAB Functions:
- mpc: The toolbox provides a range of functions for designing and simulating MPC controllers, including the
mpc function for creating and simulating MPC controllers.
- 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.
- mpcquadprog: The
mpcquadprog function is used to solve quadratic programming problems, which are often used in MPC optimization
