Offset-free 3DOF Ball and Plate Tracking Control Using Velocity-form Integral MPC

This paper proposes a disturbance-observer-assisted velocity-form integral model predictive control (MPC) structure for reference trajectory tracking on a physical three-degree-of-freedom (3DOF) Ball and Plate system. Under the small-angle assumption, the system dynamics are approximated as decoupled linear subsystems along the x- and y-axes, allowing identical MPC controllers to be applied independently to each axis. Conventional nominal-model-based MPC can exhibit steady-state tracking offsets because of model–plant mismatches, including plate-zero alignment uncertainty, actuator mismatch, sensor noise, velocity estimation errors, and residual cross-axis coupling. To mitigate these effects, a velocity-form integral MPC is formulated using state and input increments, with an augmented state containing an accumulated output term for offset-free tracking. However, direct use of state increments can make the controller sensitive to measurement noise and velocity estimation errors. Therefore, a Luenberger-observer-based disturbance observer reconstructs the system state using the innovation between the nominal model’s one-step prediction and the measured output. The reconstructed state is used to form the observer-based augmented state, improving the reliability of the initial condition for MPC prediction. Experimental results show that the proposed controller achieves stable trajectory tracking on the physical system and reduces steady-state tracking offsets under constant disturbances.

Authors

Publication Details

Journal
Journal of Institute of Control Robotics and Systems
Published
2026-11-06
DOI
https://doi.org/10.5302/j.icros.2026.26.0217
Primary Topic
Advanced Control Systems Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Offset-free 3DOF Ball and Plate Tracking Control Using Velocity-form Integral MPC

이재욱, Doyoon Ju, Young-sam Lee
Journal of Institute of Control Robotics and Systems
Advanced Control Systems Optimization
article

Offset-free 3DOF Ball and Plate Tracking Control Using Velocity-form Integral MPC

이재욱, Doyoon Ju, Young-sam Lee
article en

Abstract

This paper proposes a disturbance-observer-assisted velocity-form integral model predictive control (MPC) structure for reference trajectory tracking on a physical three-degree-of-freedom (3DOF) Ball and Plate system. Under the small-angle assumption, the system dynamics are approximated as decoupled linear subsystems along the x- and y-axes, allowing identical MPC controllers to be applied independently to each axis. Conventional nominal-model-based MPC can exhibit steady-state tracking offsets because of model–plant mismatches, including plate-zero alignment uncertainty, actuator mismatch, sensor noise, velocity estimation errors, and residual cross-axis coupling. To mitigate these effects, a velocity-form integral MPC is formulated using state and input increments, with an augmented state containing an accumulated output term for offset-free tracking. However, direct use of state increments can make the controller sensitive to measurement noise and velocity estimation errors. Therefore, a Luenberger-observer-based disturbance observer reconstructs the system state using the innovation between the nominal model’s one-step prediction and the measured output. The reconstructed state is used to form the observer-based augmented state, improving the reliability of the initial condition for MPC prediction. Experimental results show that the proposed controller achieves stable trajectory tracking on the physical system and reduces steady-state tracking offsets under constant disturbances.

Journal of Institute of Control Robotics and SystemsVol. 32(11)
Openalex Percentile: Top 23%
Advanced Control Systems Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon