IMPACT: A Toolchain for Nonlinear Model Predictive Control Specification, Prototyping, and Deployment

TL;DR

IMPACT is a versatile Python-based toolchain that simplifies the specification, prototyping, and deployment of nonlinear model predictive control solutions across multiple platforms and hardware, enhancing usability and flexibility.

Contribution

It introduces a flexible, multi-language compatible toolchain with automatic code generation for NMPC, streamlining the implementation process and reducing engineering complexity.

Findings

Effective in embedded hardware deployment

Supports multiple programming environments

Facilitates rapid NMPC prototyping

Abstract

We present IMPACT, a flexible toolchain for nonlinear model predictive control (NMPC) specification with automatic code generation capabilities. The toolchain reduces the engineering complexity of NMPC implementations by providing the user with an easy-to-use application programming interface, and with the flexibility of using multiple state-of-the-art tools and numerical optimization solvers for rapid prototyping of NMPC solutions. IMPACT is written in Python, users can call it from Python and MATLAB, and the generated NMPC solvers can be directly executed from C, Python, MATLAB and Simulink. An application example is presented involving problem specification and deployment on embedded hardware using Simulink, showing the effectiveness and applicability of IMPACT for NMPC-based solutions.