| dc.description.abstract | Parallel robots have attracted more and more attention in recent years due to their
kinematical and mechanical advantages. However the complicated high nonlinear model
with unknown parameters and singularities make the control of a parallel robot much
more difficult than a serial robot. Nonlinear control has been made great progress since
backstepping technique was developed. Backstepping technique is a recursive design
procedure and feasible for lower triangular nonlinear systems. Moreover, the adaptive
backstepping is able to handle nonlinear systems with unknown parameters, which turns
out to be a suitable control design methodology for parallel robots.
The adaptive backstepping technique is applied to set point and tracking control of a
planar parallel robot in this thesis. The dynamic model of the robot is characterized by a
set of differential algebraic equations (DAEs) and further reduced to a set o f ordinary
differential equations (ODEs). The inverse kinematics is also under investigation. For set
point control, a model-based adaptive controller is designed based on backstepping
technique, and an adaptive PD controller is also constructed for comparison. For tracking
control, adaptive backstepping controller is designed based on the model with unknown
parameters. The adaptive PD controller is also implemented for comparison. The
performances o f the controllers are tested by experiments. Desired trajectories such as
circle, line, and square are tracked in experiments for two cases: with no load and with
load at the end effector.
It is shown that adaptive controllers can achieve less steady state errors in set point
control, and smaller tracking errors in tracking control than non-adaptive controllers,
especially when there is a load attached to the end effector. | |
