While PID and feedforward control remains the industry standard, advanced strategies like Model Predictive Control (MPC) are increasingly used in high-end applications and precision motion control systems. This project focuses on designing and optimizing a control system for a Ball-Balancing Tripod, developed as a demonstration platform to highlight the benefits of Model Predictive Control.

The tripod, a parallel manipulator with three linear actuators, stabilizes a ball with variable inertia by adjusting the platform’s tilt and height. A digital twin (DT) built in Simulink models the kinematics and dynamics and is used to compare Model Predictive Control (MPC) with a PID controller and feedforward controller. All controllers were tuned and validated on the digital twin before deployment to a Speedgoat target (Simulink Real-Time) or a Beckhoff IPC for real-time control, supporting robust mechatronic system design and real-time embedded control, an area covered in depth in our control systems competences.

The video highlights how Model Predictive Control enables faster settling times, reduce overshoot, and improve stability compared to classic control strategies. Explore related work in our linear motion stage project, where intelligent control technology drives similar gains in precision and system performance.