The ball and plate system is a typical multivariable, nonlinear system, which is widely used in control engineering experiments and scientific research. As a two-dimensional extension of a ball and beam system, it not only stabilizes the ball at a desired position on the plate but also realizes random tracking control, which has involved a lot of research.
Model predictive control, which was mostly used for delay systems like chemical processes, has been used a lot in the control of mechanical systems recently. Prediction models are of different types, and recently, state space models have been used as a prediction model for many predictive controls. In this case, a state observer is necessary because it is impossible to measure all of the system states in practice. However, there are some problems with using a state observer. One of its drawbacks is robustness. And whether a state observer is included or not is also a problem for ball and plate systems.
Kim Hun Chol, a researcher at the Faculty of Automatics, presented a non-minimal state space (NMSS) model of ball and plate system, a typical multivariable, nonlinear system, and realized position control of the ball on the plate using NMSS model predictive control (MPC).
In conformity with the characteristics of MPC and the configuration of the proposed control system, he introduced the NMSS model without the need for a state observer to design MPC.
He verified the effectiveness and performance through MATLAB simulation and real experiments. The results validated that the proposed NMSS-MPC is a suitable control method for ball and plate systems.
You can find the details in his paper “Study on Stabilization and Tracking Control of Ball and Plate System using Non-Minimal State Space Model Predictive Control” in “Proceedings of KUTIC-2025”.