In this work, we present a novel iterative learning control (ILC) scheme for a class of joint position constrained robot manipulator systems with both multiplicative and additive actuator faults. Unlike most ILC literature that requires identical reference trajectory from trail to trail, in this work the reference trajectory can be non-repetitive over the iteration domain without assuming the identical initial condition. A tan-type Barrier Lyapunov Function is proposed to deal with the constraint requirements which can be both time and iteration varying, with ILC update laws adopted to learn the iteration-invariant system uncertainties, and robust methods used to compensate the iteration and time varying actuator faults and disturbances. We show that under the proposed ILC scheme, uniform convergence of the full state tracking error beyond a small time interval in each iteration can be guaranteed over the iteration domain, while the constraint requirements on the joint position vector will not be violated during operation. An illustrative example on a two degree-of-freedom robotic manipulator is presented to demonstrate the effectiveness of the proposed control scheme.
|Number of pages||17|
|Journal||International Journal of Adaptive Control and Signal Processing|
|State||Published - Jun 2017|
Bibliographical notePublisher Copyright:
Copyright © 2016 John Wiley & Sons, Ltd.
- actuator faults
- iterative learning control
- joint position constraints
- non-repetitive reference trajectory
- robot manipulator
ASJC Scopus subject areas
- Control and Systems Engineering
- Signal Processing
- Electrical and Electronic Engineering