Cogging Compensating Piecewise Iterative Learning Control with application to a motion system

Abstract

Iterative Learning Control (ILC) is an effective control technique for motion systems that perform repetitively the same trajectory (setpoint). The result of the learning procedure is a feedforward signal that perfectly compensates all deterministic dynamics in the system for the learned setpoint performed at a specific start position. For other setpoints and start positions, the learned feedforward signal will not be perfect, because the learned deterministic dynamics are setpointand position-dependent. In this paper Cogging Compensating Piecewise ILC (CCPILC) is proposed that enables to use one learned feedforward signal for different setpoints and start positions without losing performance. The learned feedforward signal will therefore be decomposed into a setpoint-and a position-dependent part, such that both parts can be adapted individually according to the desired change in setpoint and/or start position.

Linear Parameter-Varying Iterative Learning Control With Application to a Linear Motor System

“…Proof: The recursive algorithm (19) has the same asymptotic properties as the batch result (18) so the consistency of (18) can be considered. (18) can be rewritten as:…”

“…The developed ILC algorithm uses the matrix G(σ(k)), as seen in (19). This matrix is formed from the system matrix G(σ(k)) and the functions λ i (σ(ȳ(k))).…”

“…As was seen in the simulation, the proposed algorithm is robust to a certain amount of system uncertainty. A single LTI model G(q) is therefore identified to be used in the place of the LPV model in (19).…”

“…However, if the movement starts from a different position, the disturbance will change and the learnt input will no longer produce optimal tracking. This problem has been investigated in [19]. The method proposed there is to learn the input that gives optimal tracking for a specific starting position.…”

Adaptive Iterative Learning Control for High Precision Motion Systems

“…In [8] and [9], we show that the proposed method does not lead to noise amplification. The final problem, position-dependent behavior, is addressed in [10] and [11].…”