Neural networks in design of iterative learning control for nonlinear systems

Patan, Krzysztof; Patan, Maciej; Kowalów, Damian

doi:10.1016/j.ifacol.2017.08.2277

Cited by 23 publications

(2 citation statements)

References 16 publications

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“…The work of Nemec et al (2017) provides an adaptive ILC method to achieve smooth and safe manipulation of fragile items, with the adaptation supervised by reinforcement learning. Neural networks (Patan et al, 2017;Xu and Xu, 2018) or fuzzy neural network methods (Wang et al, 2008;Wang et al, 2014) are used within ILC to reduce the uncertainty of the model used for the design of the controller. The basis-motion torque composition approach (Sekimoto et al, 2009) has been proposed as a solution for the main disadvantage of the ILC, that the ILC requires a new learning process for achieving a different motion (Tanimoto et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Payload-adaptive iterative learning control for robotic manipulators

Yovchev

Miteva

2022

Mech. Sci.

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Abstract. The main purpose of the iterative learning control (ILC) method is to reduce the trajectory tracking error caused by an inaccurate model of the robot's dynamics. It estimates the tracking error and applies a learning operator to the output control signals to correct them. Today's ILC researchers are suggesting strategies for increasing the ILC's overall performance and minimizing the number of iterations required. When a payload (or a different end effector) is attached to a robotic manipulator, the dynamics of the robot change. When a new payload is added, even the most accurately approximated model of the dynamics will be altered. This will necessitate changes to the dynamics estimates, which may be avoided if the ILC process is used to control the system. When robotic manipulators are considered, this study analyses how the payload affects the dynamics and proposes ways to improve the ILC process. The study looks at the dynamics of a SCARA-type robot. Its inertia matrix is determined by the payload attached to it. The results show that the ILC method can correct for the estimated inertia matrix inaccuracy caused by the changing payload but at the cost of more iterations. Without any additional data of the payload's properties, the suggested technique may adjust and fine-tune the learning operator. On a preset reference trajectory, the payload adaptation process is empirically tested. When the same payload is mounted, the acquired adaptation improvements are then utilized for another desired trajectory. A computer simulation is also used to validate the suggested method. The suggested method increases the overall performance of ILC for industrial robotic manipulators with a set of similar trajectories but different types of end effectors or payloads.

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Section: Introductionmentioning

confidence: 99%

Payload-adaptive iterative learning control for robotic manipulators

Yovchev

Miteva

2022

Mech. Sci.

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“…It is worthy of noticing that the neural networks (NNs) and fuzzy system controllers have become very popular and efficient methods to deal with unknown system nonlinearities and approximate various uncertainties as well as disturbances. e researchers have devoted great efforts to improve the AILC schemes by combining neural networks with AILC schemes, and a variety of control algorithms have been proposed for nonlinear systems [11][12][13][14][15]. e authors employ the fuzzy system-based AILC schemes for nonlinear systems due to good approximation of fuzzy neural networks [16].…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Control for Uncertain Robot Manipulators with Repetitive Motions in Task Space

Liu

Dong

Yang

et al. 2021

Mathematical Problems in Engineering

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This paper aims at the trajectory tracking problem of robot manipulators performing repetitive tasks in task space. Two control schemes are presented to conduct trajectory tracking tasks under uncertain conditions including unmodeled dynamics of robot and additional disturbances. The first controller, pure adaptive iterative learning control (AILC), is based upon the use of a proportional-derivative-like (PD-like) feedback structure, and its design seems very simple in the sense that the only requirement on the learning gain and control parameters is the positive definiteness condition. The second controller is designed with a combination of AILC and neural networks (NNs) where the AILC is adopted to learn the periodic uncertainties that attribute to the repetitive motion of robot manipulators while the add-on NNs are used to approximate and compensate all nonperiodic ones. Moreover, a combined error factor (CEF), which is composed of the weighted sum of tracking error and its derivative, is designed for network updating law to improve the learning speed as well as tracking accuracy of the system. Stabilities of the controllers and convergence are proved rigorously by a Lyapunov-like composite energy function. The simulations performed on two-link manipulator are provided to verify the effectiveness of the proposed controllers. The results of compared simulations illustrate that our proposed control schemes can significantly conduct trajectory tracking tasks.

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