2020
DOI: 10.1002/rnc.4988
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Adaptive P‐type iterative learning radial basis function control for robot manipulators with unknown varying disturbances and unknown input dead zone

Abstract: This article proposes an adaptive iterative learning radial basis function (RBF) scheme to solve the trajectory-tracking problem for perturbed robot manipulators with unknown iteration varying disturbances and unknown dead-zone input. It is well known that the presence of the dead zone in actuators and mechatronics devices gives rise to extra difficulty due to the presence of singularity in the input channels. Hence, it is interesting to take this problem into account when synthesizing a controller. This sy… Show more

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Cited by 17 publications
(10 citation statements)
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“…It was of great significance for nonlinear, complex, intricate modelling and perfect trajectory control problems. ILC was widely used in many fields such as robotics [21]- [23], rehabilitation medicine [24], chemical batch processing technology [25], and tracking control of piezoelectric systems [26]. In addition to practical applications, ILC has also been widely used in switching systems [27,28], time-delay systems [29], and stochastic systems [30].…”
Section: Introductionmentioning
confidence: 99%
“…It was of great significance for nonlinear, complex, intricate modelling and perfect trajectory control problems. ILC was widely used in many fields such as robotics [21]- [23], rehabilitation medicine [24], chemical batch processing technology [25], and tracking control of piezoelectric systems [26]. In addition to practical applications, ILC has also been widely used in switching systems [27,28], time-delay systems [29], and stochastic systems [30].…”
Section: Introductionmentioning
confidence: 99%
“…Recent years have witnessed a rapidly growing interest in control system design using neural networks (NNs) together with the adaptive technique. As one of the most popular intelligent computation approaches, NNs have the inherent learning ability and can approximate nonlinear continuous functions to arbitrary accuracy 14‐16 . For the uncertain systems, radial basis function neural networks (RBFNNs) have been efficiently employed to approximate the ideal controller 17‐19 .…”
Section: Introductionmentioning
confidence: 99%
“…However, none of the above studies considers such inherent characteristics of repeatability for control. In consideration of repeatability for control, it is reasonable to explore input and output information of previous iterations to gradually improve the control performance 12 . This idea motivates the proposed iterative learning control (ILC), which is a data‐driven intelligent control strategy 13‐15 .…”
Section: Introductionmentioning
confidence: 99%
“…In consideration of repeatability for control, it is reasonable to explore input and output information of previous iterations to gradually improve the control performance. 12 This idea motivates the proposed iterative learning control (ILC), which is a data-driven intelligent control strategy. [13][14][15] Different from other control methods, ILC uses input and output information from previous iterations to generate input for the current iteration.…”
Section: Introductionmentioning
confidence: 99%