The appropriate choice of sensing and how to obtain the desired state information from available sensing for feedback or learning process are essential for most control schemes, including iterative learning control (ILC), to achieve their performance objective. In the multi-joint robots with joint elasticity, the load side joint space measurements are usually not available, even though the load side (end-effector) performance is of ultimate interest. This is termed as mismatched sensing problem. Furthermore, the mismatched uncertainty and mismatched real-time feedback signals in the robots with joint elasticity set further difficulty in achieving high performance. In this paper, a hybrid two-stage model based iterative learning control (ILC) scheme is proposed to deal with the mismatched dynamics. Also, to tackle the mismatched sensing issue, a sensor fusion scheme is developed. An optimization based inverse differential kinematics algorithm and decoupled adaptive kinematic Kalman filter (KKF) are integrated to obtain load side joint space information from the insufficient end-effector measurements. The proposed ILC scheme together with the load side state estimation algorithm is validated through the experimental study on a 6-DOF industrial robot.
- Dynamic Systems and Control Division
Iterative Learning Control With Sensor Fusion for Robots With Mismatched Dynamics and Mismatched Sensing
Chen, W, & Tomizuka, M. "Iterative Learning Control With Sensor Fusion for Robots With Mismatched Dynamics and Mismatched Sensing." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control Over Networks; Control Systems Design; Cooperative and Decentralized Control; Dynamic System Modeling; Dynamical Modeling and Diagnostics in Biomedical Systems; Dynamics and Control in Medicine and Biology; Estimation and Fault Detection; Estimation and Fault Detection for Vehicle Applications; Fluid Power Systems; Human Assistive Systems and Wearable Robots; Human-in-the-Loop Systems; Intelligent Transportation Systems; Learning Control. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 907-915. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8721
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