Model Predictive Control Toolbox vs. Solver SDK vs. System Composer vs. Wing Python IDE Comparison Chart

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 multi-stage 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. Design implicit, gain-scheduled, and adaptive MPC controllers that solve a quadratic programming (QP) problem. Generate an explicit MPC controller from an implicit design. Use discrete control set MPC for mixed-integer QP problems.

System Composer™ enables the specification and analysis of architectures for model-based systems engineering and software architecture modeling. With System Composer, you allocate requirements while refining an architecture model that can then be designed and simulated in Simulink®. Architecture models consisting of components and interfaces can be authored directly, imported from other tools, or populated from the architectural elements of Simulink designs. You can describe your system using multiple architecture models and establish direct relationships between them via model-to-model allocations. Behaviors can be captured in sequence diagrams, state charts, or Simulink models. You can define and simulate the execution order of component functions and generate code from your software architecture models (with Simulink and Embedded Coder®).