Adaptive iterative learning control for robot manipulators

Jia, Xiaogang; Yuan, Zhi-ye

doi:10.1109/icicisys.2010.5658818

Cited by 8 publications

(9 citation statements)

References 6 publications

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“…Based on these studies, this study intends to implement an adaptive control mechanism for ILC to minimize the tracking error so that the stability and robustness can be improved in the future. The proposed adaptive iterative learning control (AILC) can not only acquire the prior system knowledge based on multiple iterations but also reduce the nonlinear uncertainty of the controller [20,21]. In summary, this paper contributes to the body of knowledge by introducing a novel adaptive iterative learning control mechanism, which allows a more precise and flexible controller for the lower limb exoskeleton robots.…”

Section: Design Of the Ailc Controllermentioning

confidence: 99%

Single Leg Gait Tracking of Lower Limb Exoskeleton Based on Adaptive Iterative Learning Control

2019

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The lower limb exoskeleton is a wearable human–robot interactive equipment, which is tied to human legs and moves synchronously with the human gait. Gait tracking accuracy greatly affects the performance and safety of the lower limb exoskeletons. As the human–robot coupling systems are usually nonlinear and generate unpredictive errors, a conventional iterative controller is regarded as not suitable for safe implementation. Therefore, this study proposed an adaptive control mechanism based on the iterative learning model to track the single leg gait for lower limb exoskeleton control. To assess the performance of the proposed method, this study implemented the real lower limb gait trajectory that was acquired with an optical motion capturing system as the control inputs and assessment benchmark. Then the impact of the human–robot interaction torque on the tracking error was investigated. The results show that the interaction torque has an inevitable impact on the tracking error and the proposed adaptive iterative learning control (AILC) method can effectively reduce such error without sacrificing the iteration efficiency.

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Section: Design Of the Ailc Controllermentioning

confidence: 99%

Single Leg Gait Tracking of Lower Limb Exoskeleton Based on Adaptive Iterative Learning Control

2019

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“…One of the most attractive advantages of AILC scheme is the capability to deal with the issues of large initial resetting error, large input disturbance, and iteration-varying desired trajectory. In the past decade, the AILC schemes have been utilized for repeated tracking control of robotic systems [10][11][12][13][14], or a class of nonlinear systems [15,16]. In order to relax the restrict Lipschitz condition on the plant's nonlinearity, the Lyapunovlike approach instead of contraction mapping theory is applied in AILC to analyze the stability and convergence.…”

Section: Introductionmentioning

confidence: 99%

“…As noted above, ( ), ( ) ∈ ∞ [0, ], we havẽ ( ) ∈ ∞ [0, ] (by (19)). Moreover, because is a Hurwitz matrix and̃∈ ∞ [0, ], this implies that̂( ) ∈ ∞ [0, ] (by (12)). Finally, since are Hurwitz matrices and − ( ( )) ( ) + * ⊤ (3) (̂( )) + ( ) ∈ ∞ [0, ], we havẽ∈ ∞ [0, ] (by (14)).…”

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confidence: 99%

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An Observer-Based Adaptive Iterative Learning Control Using Filtered-FNN Design for Robotic Systems

Wang

Chien

2014

Advances in Mechanical Engineering

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An observer-based adaptive iterative learning control using a filtered fuzzy neural network is proposed for repetitive tracking control of robotic systems. A state tracking error observer is introduced to design the iterative learning controller using only the measurement of joint position. We first derive an observation error model based on the state tracking error observer. Then, by introducing some auxiliary signals, the iterative learning controller is proposed based on the use of an averaging filter. The main control force consists of a filtered fuzzy neural network used to approximate for unknown system nonlinearity, a robust learning term used to compensate for uncertainty, and a stabilization term used to guarantee the boundedness of internal signals. The adaptive laws combining time domain and iteration domain adaptation are presented to ensure the convergence of learning error. We show that all the adjustable parameters as well as internal signals remain bounded for all iterations. The norm of output tracking error will asymptotically converge to a tunable residual set as iteration goes to infinity.

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“…Paper [4] combines fuzzy control with ILC with error gain self-tuning by fuzzy control. Papers [5][6] adopt adaptive control based on ILC for the trajectory tracking in order to cope with the unknown model parameters and disturbances.…”

Section: Introductionmentioning

confidence: 99%

A New Fuzzy Iterative Learning Control Algorithm for Single Joint Manipulator

Wang

Bian

2016

Archives of Control Sciences

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This paper present a new fuzzy iterative learning control design to solve the trajectory tracking problem and performing repetitive tasks for rigid robot manipulators. Several times' iterations are needed to make the system tracking error converge, especially in the first iteration without experience. In order to solve that problem, fuzzy control and iterative learning control are combined, where fuzzy control is used to tracking trajectory at the first learning period, and the output of fuzzy control is recorded as the initial control inputs of ILC. The new algorithm also adopts gain self-tuning by fuzzy control, in order to improve the convergence rate. Simulations illustrate the effectiveness and convergence of the new algorithm and advantages compared to traditional method.

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Adaptive iterative learning control for robot manipulators

Cited by 8 publications

References 6 publications

Single Leg Gait Tracking of Lower Limb Exoskeleton Based on Adaptive Iterative Learning Control

Single Leg Gait Tracking of Lower Limb Exoskeleton Based on Adaptive Iterative Learning Control

An Observer-Based Adaptive Iterative Learning Control Using Filtered-FNN Design for Robotic Systems

A New Fuzzy Iterative Learning Control Algorithm for Single Joint Manipulator

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