Model predictive control

Quantum mechanics

Robot

Robotic arm

Algorithm

Residual

Control (management)

Linearization

Engineering

Extended Kalman filter

Control engineering

Tracking (education)

Pedagogy

Computer science

Classical mechanics

Inverse dynamics

Tracking error

Physics

Nonlinear system

Offset (computer science)

Trajectory

Psychology

Astronomy

Artificial intelligence

Kalman filter

Kinematics

Medicine

Programming language

Physical medicine and rehabilitation

Control theory (sociology)

IEEE Robotics and Automation Letters

High-precision trajectory tracking is fundamental in robotic manipulation. While industrial robots address this through stiffness and high-performance hardware, compliant and cost-effective robots require advanced control to achieve accurate position tracking. In this letter, we present a model-based control approach, which makes use of data gathered during operation to improve the model of the robotic arm and thereby the tracking performance. The proposed scheme is based on an inverse dynamics feedback linearization and a data-driven error model, which are integrated into a model predictive control formulation. In particular, we show how offset-free tracking can be achieved by augmenting a nominal model with both a Gaussian process, which makes use of offline data, and an additive disturbance model suitable for efficient online estimation of the residual disturbance via an extended Kalman filter. The performance of the proposed offset-free GPMPC scheme is demonstrated on a compliant 6 degrees of freedom robotic arm, showing significant performance improvements compared to other robot control algorithms.

Data-Driven Model Predictive Control for Trajectory Tracking With a Robotic Arm