2022/03/21 by Jörg Fehr, Fehr, Jörg, Arnim Kargl +3
Engineering · #A priori and a posteriori #Advanced Control Systems Design #Advanced Control Systems Optimization #Artificial intelligence #Computer science #Control (management) #Control engineering #Control theory (sociology) #Data modeling #Engineering #FOS: Electrical engineering #Identification (biology) #Model predictive control #Nonlinear control #Nonlinear system #Real-time simulation and control systems #Robust control #Robustness (evolution) #System identification #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2203.10896
openalex publication_date 2022/03/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
For model-based control, an accurate and in its complexity suitable representation of the real system is a decisive prerequisite for high and robust control quality. In a structured step-by-step procedure, a model predictive control (MPC) scheme for a Schunk PowerCube robot is derived. Neweul-M2 provides the necessary nonlinear model in symbolical and numerical form. To handle the heavy online computational burden involved with the derived nonlinear model, a linear time-varying MPC scheme is developed based on linearizing the nonlinear system concerning the desired trajectory and the a priori known corresponding feed-forward controller. To improve the identification of the nonlinear friction models of the joints, a nonlinear regression method and the Sparse Identification of Nonlinear Dynamics (SINDy) are compared with each other concerning robustness, online adaptivity, and necessary preprocessing of the input data. Everything is implemented on a slim, low-cost control system with a standard laptop PC.