A new fast nonlinear model predictive control of parallel manipulators: Design and experiments

High-speed parallel manipulators are characterized by fast sampling rates and challenges owing to the presence of constraints, high nonlinearities, uncertainties, dynamics. The development a reliable nonlinear model predictive control (NMPC) approach for this type robotic systems is challenging because complex dynamics that involves computational burden. To address problem, paper proposes new NMPC strategy applied high-speed robots. Based on (i) parameterization technique, (ii) gradient solver, (iii) proportional integral derivative (PID) term, (iv) dynamic model, (v) an artificial neural network (ANN) two frameworks were developed reduce cost classical online implementation. In study, we focus improving performance standard outside range its applicability developing controller kinematic (PKMs). Numerical simulations real-time experimental results presented discussed validate relevance proposed controllers. Experiments conducted four-degree-of-freedom (4-DOF) PKM called VELOCE in our laboratory. show solution outperforms original scheme terms tracking performance.