High order iterative learning control to solve the trajectory tracking problem for robot manipulators using Lyapunov theory

Bouakrif, Farah; Zasadzinski, Michel

doi:10.1177/0142331217741958

Cited by 33 publications

(31 citation statements)

References 39 publications

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“…In the ILC literature, Assumption 3 is widely used in many works. 14,40,43 Assumption 4. The external disturbances d k (t) are norm bounded by unknown function.…”

Section: F I G U R E 1 Dead-zone At the Input Of The Robotmentioning

confidence: 99%

“…2. Unlike many works based on ILC, 3,14,40 where the disturbances are supposed to be invariant, in our article, these disturbances are supposed to be nonrepetitive and also unknown. 3.…”

Section: Introductionmentioning

confidence: 96%

“…Over the last two decades, this technique has been the center of interest of many researchers. [2][3][4][5][6][7][8][9][10][11][12][13][14][15] dead-zone may severely degrade system performance. How to deal with these uncertainties becomes another direction in the development of ILC.…”

Section: Introductionmentioning

confidence: 99%

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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

Bensidhoum

Bouakrif

2020

Intl J Robust & Nonlinear

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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 synthesis is made here. In addition, the control design is very simple in the sense that we use, only, the proportional gain. Therefore, the considerable amount noise caused by the sensors for velocity measurements of robot manipulators is avoided. Another advantage of this work is that the unknown disturbances are assumed to be time varying and also varying from iteration to iteration. Thus, the RBF neural network is used to approximate these unknown nonlinear functions. Using the Lyapunov theory, the analysis of the stability of the closed-loop system is guaranteed when the iteration number tends to infinity. Finally, simulation results on the PUMA 560 arm are provided to illustrate the effectiveness of the proposed method. In order to evaluate the performance of our controller, a comparison of our results with other method is also given. K E Y W O R D SIterative learning control, Radial basis function, Dead-zone, Adaptive, Robot manipulators, Lyapunov theory 1 Indeed, input uncertainties such as dead-zone, saturation, backlash, and hysteresis are kind of nonsmooth and nonaffine in input factor widely existing in actuators and sensors. In fact, in many practical applications, the presence of Int J Robust Nonlinear Control. 2020;30:4075-4094.wileyonlinelibrary.com/journal/rnc

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“…In the ILC literature, Assumption 3 is widely used in many works. 14,40,43 Assumption 4. The external disturbances d k (t) are norm bounded by unknown function.…”

Section: F I G U R E 1 Dead-zone At the Input Of The Robotmentioning

confidence: 99%

“…2. Unlike many works based on ILC, 3,14,40 where the disturbances are supposed to be invariant, in our article, these disturbances are supposed to be nonrepetitive and also unknown. 3.…”

Section: Introductionmentioning

confidence: 96%

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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

Bensidhoum

Bouakrif

2020

Intl J Robust & Nonlinear

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“…This concept was first proposed in Uchiyama (1978). Nowadays, it has become an important branch of intelligence control and it is widely used in practical systems, such as ethanol fermentation processes (Lin et al, 2019), robot manipulator (Bouakrif and Zasadzinski, 2018), autonomous aerial refueling (Dai et al, 2018) and spacecraft attitude control (Hu et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A data-driven approach for trajectory-based aircraft operation with controlled time of arrival and along-track wind effects

Jiang

Hou

2020

Transactions of the Institute of Measurement and Control

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Trajectory-based aircraft operation and control is one of the hot issues in air traffic management. However, the accurate mechanism modeling of aircraft is tough work, and the operation data have not been effectively utilized in many studies. So, in this work, we apply the model-free adaptive iterative learning control method to address the time-of-arrival control problem in trajectory-based aircraft operation. This problem is first formulated into a trajectory tracking problem with along-track wind disturbance. Through rigorous analysis, it is shown that this method, combined with point-to-point iterative learning control (ILC) strategy, can effectively deal with the arrival time control problem with multiple time constraints. Then, the terminal ILC strategy is applied, aiming to resolve the same problem with a time constraint at the end point. Compared with the PID (Proportional Integral Derivative) type ILC, the proposed method improves control performance by 11.15% in root mean square of tracking error and 9.32% in integral time absolute error. The sensitivity and flexibility of the data-driven approach is further verified through numerical simulations.

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“…ILC differs from most existing control methods in the sense that it exploits every possibility of incorporating past control information, such as tracking errors and control input signals, into the construction of the present control action to enable the controlled system to perform progressively better from operation to operation. Since the ILC method was proposed by Uchiyama [8] and presented as a formal theory by Arimoto [9], this technique has been the center of interest of many researchers over the last decades [10][11][12][13][14][15][16][17][18][19].…”

mentioning

confidence: 99%

Iterative Learning and Fractional Order Control for Complex Systems

et al. 2019

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High order iterative learning control to solve the trajectory tracking problem for robot manipulators using Lyapunov theory

Cited by 33 publications

References 39 publications

Adaptive P‐type iterative learning radial basis function control for robot manipulators with unknown varying disturbances and unknown input dead zone

Adaptive P‐type iterative learning radial basis function control for robot manipulators with unknown varying disturbances and unknown input dead zone

A data-driven approach for trajectory-based aircraft operation with controlled time of arrival and along-track wind effects

Iterative Learning and Fractional Order Control for Complex Systems

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