What Is Model Predictive Control Toolbox?
Model Predictive Control Toolbox™ provides functions, an app, Simulink® blocks, and reference examples for developing model predictive control (MPC). For linear problems, the toolbox supports the design of implicit, explicit, adaptive, and gain-scheduled MPC. For nonlinear problems, you can implement single- and multistage nonlinear MPC. The toolbox provides deployable optimization solvers and also enables you to use a custom solver.
You can evaluate controller performance in MATLAB® and Simulink by running closed-loop simulations. For automated driving, you can also use the provided MISRA C®– and ISO® 26262–compliant blocks and examples to quickly get started with lane keep assist, path planning, path following, and adaptive cruise control applications.
The toolbox supports C and CUDA® code and IEC 61131-3 Structured Text generation.
Published: 16 Sep 2020
Model Predictive Control Toolbox lets you design and simulate model predictive controllers to control multi-input, multi-output systems subject to input/output constraints for application such as advanced driver assistance systems, process control, and robotics.
For linear implicit MPC controllers, you can use the MPC Designer app to define a prediction model and specify parameters such as prediction and control horizons, constraints, and controller weight. You can interactively tune your PC controller, simulate it against the linear plant model, and verify its performance by running it against a nonlinear Simulink model. For nonlinear plants with a wide operating range, you can implement adaptive MPC controllers that let you update the internal prediction model at each computation step.
Alternatively, for highly nonlinear plants, the Toolbox supports single and multi-stage nonlinear MPC design using nonlinear prediction models, cost functions, and constraints. If the prediction model cannot be specified analytically, you can use models identified through measured data with System Identification Toolbox and Deep Learning Toolbox.
For applications with fast sample times, explicit MPC controllers require fewer runtime computations than traditional MPC controllers by using optimal solutions pre-computed offline. You can accelerate the development of MPC-based automated driving systems such as path planning, path following, and adaptive cruise control by using pre-built Simulink blocks that satisfy ISO 26262 and MISRA C standards.
The Toolbox provides built-in quadratic programming solvers for linear MPC problems. For nonlinear MPC problems, you can use fmincon from Optimization Toolbox. Additionally, the Toolbox lets you use custom solvers, such as FORCES PRO solvers developed by Embotech. The Toolbox supports C, C++, and CUDA code generation as well as Structured Text generation for targeting embedded microprocessors, GPUs, and PLCs.
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