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The license is restricted to a single country of operation. Global Individual licenses are available to serve users around the world. Run closed-loop simulations to evaluate controller performance. Interactively design MPC controllers by defining an internal plant model and adjusting horizons, weights, and constraints. Validate controller performance using simulation scenarios. Compare responses for multiple MPC controllers.
Use command-line functions to design MPC controllers. Define an internal plant model; adjust weights, constraints, and other controller parameters. Simulate closed-loop system response to evaluate controller performance. Use the reference examples to quickly design ADAS controllers. Generate code from the Simulink blocks for deploying MPC controllers in the vehicle.
Generate code from the prebuilt blocks for in-vehicle deployment. Use reference application examples to walk through a workflow for designing and deploying MPC controllers for automated driving systems.
Reference application examples also show you how different parts of your system can be modeled at various levels of fidelity. Design MPC controllers for systems with linear dynamics. Design adaptive and gain-scheduled MPC controllers for plants with dynamics that change with operating conditions. Update your plant model at run time and provide it as an input to the controller.
Use a built-in linear time-varying LTV Kalman filter with guaranteed asymptotic stability for state estimation in adaptive model predictive controllers. Control nonlinear plants over a wide range of operating conditions with the Multiple MPC Controllers block. Design an MPC controller for each operating point and switch between the controllers at run time. Iteratively improve your controller design by defining an internal plant model, adjusting controller parameters, and simulating closed-loop system response to evaluate controller performance.
Review your controller for potential design issues. After defining the internal plant model, complete the design of your MPC controller by specifying the sample time, prediction and control horizons, scale factors, input and output constraints, and weights. The toolbox also supports constraint softening and time-varying constraints and weights.
Estimate controller states from measured outputs using the built-in state estimator. Alternatively, use a custom algorithm for state estimation. Detect potential stability and robustness issues with your MPC controller using the built-in diagnostic function.
Use the diagnostic results to adjust controller weights and constraints during controller design to avoid run-time failures. Improve controller performance by tuning weights and constraints at run time. Analyze the run-time performance of your controllers. Adjust the weights and constraints of your MPC controller to optimize its performance at run time without redesigning or reimplementing it.
Access the optimization status signal to detect rare occasions when an optimization may fail to converge. Use this information to guide decisions on backup control strategies. Design, simulate, and deploy MPC controllers in applications with limited computational resources. Simplify a generated explicit MPC controller for a reduced memory footprint. Design, simulate, and deploy an MPC controller with guaranteed worst-case execution time using an approximate suboptimal solution.
Design nonlinear MPC controllers to control plants using nonlinear prediction models, cost functions, or constraints. Use nonlinear MPC controllers for optimal planning applications that require a nonlinear model with nonlinear costs or constraints. Simulate closed-loop control of nonlinear plants under nonlinear costs and constraints. You can also specify your own custom nonlinear solver. Design economic MPC controllers to optimize the controller for an arbitrary cost function under arbitrary nonlinear constraints.
You can use a linear or nonlinear prediction model, a custom nonlinear cost function, and custom nonlinear constraints. Generate code from provided active-set and interior-point quadratic programming QP solvers for efficient implementation on embedded processors.
For nonlinear problems, use the sequential quadratic programming SQP solver from Optimization Toolbox for simulation and code generation. Deploy the generated code to any number of processors. Contact the Model Predictive Control Toolbox technical team.
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