Calibration-Based Iterative Learning Control for Path Tracking of Industrial Robots

Zhao, Yulin; Lin, You-Wei; Xi, Fengfeng; Guo, Shuai

doi:10.1109/tie.2014.2364800

Cited by 135 publications

(58 citation statements)

References 28 publications

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“…Iterative learning control (ILC for short) is put forward and introduced by Uchiyama [1] and Arimoto [2,3]. In the past two decades, ILC has been widely used in the field of industrial control as the representative of the robot [4][5][6][7][8][9][10]. Many engineers pay attention to study ILC for various distributed parameter systems, one can refer to [11,12] and reference therein.…”

Section: Introductionmentioning

confidence: 99%

On the iterative learning control of fractional impulsive evolution equations in Banach spaces

Debbouche

Wang

2015

Math Methods in App Sciences

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In this paper, we study P‐type, PIα‐type, and D‐type iterative learning control for fractional impulsive evolution equations in Banach spaces. We present triple convergence results for open‐loop iterative learning schemes in the sense of λ‐norm through rigorous analysis. The proposed iterative learning control schemes are effective to fractional hybrid infinite‐dimensional distributed parameter systems. Finally, an example is given to illustrate our theoretical results. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

On the iterative learning control of fractional impulsive evolution equations in Banach spaces

Debbouche

Wang

2015

Math Methods in App Sciences

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“…The PD control law can be written as τ = −K p z 1 − K dż1 . In the experiment, control gains are designed as K p = diag [8,6] and K p = diag[1.5, 1.1]. Saturation limits for the two motors are chosen as ϕ 1max = 2.15 Nm and ϕ 2max = 1.5 Nm.…”

Section: B Experiments Resultsmentioning

confidence: 99%

“…Since the control of exoskeletons presents a significantly increased complexity over the control of traditional robotic manipulators, some control approaches have been extended [8], [9]. In [10], the controller was obtained through emulating inertia compensation by adding the measurement of angular acceleration multiplying a proportional gain.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Disturbance Observer-Based Control Design for a Robotic Exoskeleton Incorporating Fuzzy Approximation

Wang

et al. 2015

IEEE Trans. Ind. Electron.

208

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To perform power augmentation tasks of a robotic exoskeleton, this paper utilizes fuzzy approximation and designed disturbance observers for compensating the disturbance torques caused by unknown input saturation, fuzzy approximation errors, viscous friction, gravity and payloads. The proposed adaptive fuzzy control with updated parameters mechanism and additional torque inputs by using the disturbance observers are exerted into the robotic exoskeleton via feed-forward loops to counteract to the disturbances. Through such an approach, the system does not need any requirement of built-in torque sensing units. In order to validate the proposed framework, the extensive experiments are conducted on the upper limb exoskeleton using the state feedback and output feedback control to illustrate the performance the proposed approaches.

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“…Traditional visual servoing relies on feature extraction. In most visual servoing literatures, several fiducial markers are used for features so that the tracking of them can be easily achieved [37].…”

Section: Control Strategy Based On Visual Feedbackmentioning

confidence: 99%

Brain–Machine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot

Qiu

et al. 2017

IEEE Trans. Fuzzy Syst.

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This paper presents a teleoperation control for an exoskeleton robotic system based on the brain-machine interface (BMI) and vision feedback. Vision compressive sensing, brainmachine reference commands, and adaptive fuzzy controllers in joint-space have been effectively integrated to enable robot performing manipulation tasks guided by human operator's mind. First, a visual-feedback link is implemented by video captured by a camera, allowing him/her to visualize the manipulator's workspace and the movements being executed. Then, compressed images are used as feedback errors in a nonvector space for producing SSVEP (Steady-State Visual Evoked Potentials) electroencephalography (EEG) signals, and it requires no prior information on features in contrast to the traditional visual servoing. The proposed EEG decoding algorithm generates control signals for the exoskeleton robot using features extracted from neural activity. Considering coupled dynamics and actuator input constraints during the robot manipulation, a local adaptive fuzzy controller has been designed following Lyapunov synthesis to drive the exoskeleton tracking the intended trajectories in human operator's mind and to provide a convenient way of dynamics compensation with minimal knowledge of the dynamics parameters of the exoskeleton robot. Extensive experiment studies employing three subjects have been performed to verify the validity of the proposed method.

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Calibration-Based Iterative Learning Control for Path Tracking of Industrial Robots

Cited by 135 publications

References 28 publications

On the iterative learning control of fractional impulsive evolution equations in Banach spaces

On the iterative learning control of fractional impulsive evolution equations in Banach spaces

Nonlinear Disturbance Observer-Based Control Design for a Robotic Exoskeleton Incorporating Fuzzy Approximation

Brain–Machine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot

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