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

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.

An optimal open-loop strategy for handling a flexible beam with a robot manipulator

Fast and safe manipulation of flexible objects with a robot manipulator necessitates measures to cope with vibrations. Existing approaches either increase the task execution time or require complex models and/or additional instrumentation to measure vibrations. This paper develops a model-based method that overcomes these limitations. It relies on a simple pendulum-like model for modeling the beam, open-loop optimal control for suppressing vibrations, and does not require any exteroceptive sensors. We experimentally show that the proposed method drastically reduces residual vibrations -- at least 90% -- and outperforms the commonly used input shaping (IS) for the same execution time. Besides, our method can also execute the task faster than IS with a minor reduction in vibration suppression performance. The proposed method facilitates the development of new solutions to a wide range of tasks that involve dynamic manipulation of flexible objects.