Examination Committee

Prof Nian LIN, PHYS/HKUST (Chairperson)
Prof Zexiang LI, ECE/HKUST (Thesis Supervisor)
Prof Youfu LI, Department of Mechanical and Biomedical Engineering, City University of Hong Kong (External Examiner)
Prof Roger S K CHENG, ECE/HKUST
Prof Frank Chongwoo PARK, ECE/HKUST
Prof Kai TANG, MAE/HKUST
 

Abstract

The research studies robot dynamics modelling, identification, control and application to comprehensively improve the capability of controller. Firstly, we employ geometric method to describe the dynamics in different forms. The recursive Newton-Euler equations are efficient for implementation. The matrix form is suitable for controller design. The dynamic parameters identification and adaptive control prefer to use regressive form. The accuracy of modelling  are validated by comparing with Simmechanics.
 
Next, we identify dynamic parameters through a systematic procedure. We firstly find the minimal identifiable parameters. Secondly, we optimize a identification trajectory in Fourier-Series form for increasing robustness to uncertainties. Next, the weighted least square error algorithm is adopted to estimate parameters. Finally, we do validation experiments for verification.
 
Thirdly, different control schemes are simulated in Matlab and implemented via dSpace. Traditional PID controllers only perform well locally due to varying motor inertias at different configurations. We firstly propose to simply add feed-forward torque to current loop. Furthermore, we select computed-torque type controllers designed by feedback linearisation or passivity-based techniques. Transitional PD, Modified PD and  Augment PD controllers are put forward. Finally, for counteracting effects of imperfect model, we apply adaptive control to on-line modifying model. We did thorough simulation to verify the stability, following performance and convergence. For real experiments, we introduce other two commercial controllers for comparison, i.e PDFF controller and HD controller. For testing adaptive control, we attach 8kg load to robot end. The results are desirable although not so amazing.
 
Finally, we fulfil some applications based on dynamics. One is robot teaching by dragging. The dynamics compensation provides assistance force for easy of dragging under a modified control scheme. Besides, we explore energy efficient trajectory planning for P2P movement by considering dynamics constraints. It can be simplified with B-spline parametrization of path which leads to only optimize control points.