Motion design, control and implementation in robot manipulators
The dynamic performance of robots, specifically the tracking accuracy
and motion duration, is influenced by both the nominal motion profile
and the feedback control method employed.
Three schemes are developed and experimentally tested to tackle the
improvement of dynamic performance, in the absence of accurate dynamic
Model Referenced Adaptive Controller Schemes (MRACS) can be designed
to facilitate the characterisation of otherwise complex system
dynamics. In one scheme an MRACS is used to force the robot to behave
as if it were linear and decoupled, enabling simple model based
dYnamic tuning methods to be applied to the motion laws. Its promise
as a technique is demonstrated. but the controller performance is
found to be degraded by practical limitations. It is applied to both
joint and Cartesian based motion laws.
A computer controlled robot contains all the elements necessary for an
autonomous self experimentation system. This featUre is exploited in
the derivation and implementation of two further schemes which are
termed self learning. In these, the robot's trajectory is stored as a
set of discrete data. Algorithms are developed for tuning this data
subsequent to each run. Their use requires minimal knowledge of the
dynamics, no additional transducers and little computation.
The first of the self learning schemes is used to cyclically reduce
the tracking errors. Once complete, the updating process can be
curtailed. Errors on completion are close to the transducer
The second of these schemes involves an incremental reduction in the
duration of a given motion. Various. parameters for detecting
saturation are proposed and tested. A normalised ratio of peak to
average velocity is found promising.
Combining these two schemes, tuning for speed to near saturation then
tuning for accuracy, provides a method for obtaining a near minimum
time trajectory, with maximum possible tracking accuracy. at low cost.