2013 13th International Conference on Control, Automation and Systems (ICCAS 2013) 2013
DOI: 10.1109/iccas.2013.6703932
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Cited by 2 publications
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“…A more effective Q-ILC algorithm was proposed in [23], taking the adjacent input variations into account. In article [24], different types of stochastic disturbance are considered in the bias in a linear static model, and Q-ILC algorithms are derived. On the other hand, since uncertainty is widely present in robot system, the Kalman filter was typically considered to enhance the ILC performance [25]- [27].…”
Section: Introductionmentioning
confidence: 99%
“…A more effective Q-ILC algorithm was proposed in [23], taking the adjacent input variations into account. In article [24], different types of stochastic disturbance are considered in the bias in a linear static model, and Q-ILC algorithms are derived. On the other hand, since uncertainty is widely present in robot system, the Kalman filter was typically considered to enhance the ILC performance [25]- [27].…”
Section: Introductionmentioning
confidence: 99%
“…Iterative learning control (ILC), originally proposed to control robot arms by Arimoto, is an advanced control strategy which mimics the human learning mechanism and, therefore, has the capability of improving the control performance from trail to trail without much requirement for prior process knowledge. Due to good control performance and applicability, this control scheme has been studied extensively in recently decades with its application extending from mechanical systems to many batch or repetitive processes, such as batch chemical reactors, , biomaterial applications, , semiconductor manufacturing, , pharmaceutical engineering, , polymer processing, , etc.…”
Section: Introductionmentioning
confidence: 99%