D‐type iterative learning control for one‐sided Lipschitz nonlinear systems

Gu, Panpan; Tian, Senping

doi:10.1002/rnc.4511

Cited by 25 publications

(25 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…1. Differently to References [38][39][40], only the proportional action has been used in our control scheme, which makes the control design very simple and practical. Therefore, the problem of amplification of the measurement noise will disappear.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

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

show abstract

“…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%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

show abstract

“…where Ψ 1 ∈ R m×m and Ψ 2 ∈ R m×m denote two learning gain matrices to be designed. Remark 1 It has been known from Zhang [24,26,39] that impulse terms exist in the response of the singular fractionalorder system. The impulse terms may result in control saturation or even deteriorate the system performance, and thereby it is expected to eliminate.…”

Section: A Convergence Analysis Of Linear Sfomasmentioning

confidence: 99%

“…Furthermore, with the developed closed-loop D α -type ILC law, the singular fractional-order system can be transformed into a normal system, where the impulsive effects can be removed. For more details, please refer to [26,39].…”

Section: A Convergence Analysis Of Linear Sfomasmentioning

confidence: 99%

Consensus Tracking Via Iterative Learning Control for Singular Fractional-Order Multi-Agent Systems Under Iteration-Varying Topologies and Initial State Errors

Wang

Zhou

et al. 2020

IEEE Access

View full text Add to dashboard Cite

This paper investigates the leader-following consensus tracking problems via iterative learning control for singular fraction-order multi-agent systems in the presence of iteration-varying switching topologies and initial state errors. First, in order to eliminate the impulsive effect of singular systems and handle iteration-varying topologies, the closed-loop D α-type iterative learning control protocol is proposed. To deal with initial state errors, the initial state learning laws are introduced in light of the initial output errors of each follower agent. The developed D α-type learning protocols based on initial state learning laws can guarantee each follower track perfectly the leader agent in the fixed time interval. Next, the sufficient convergent conditions of consensus tracking errors are provided. Moreover, the D α-type learning protocols are extended to nonlinear singular fraction-order multi-agent systems with iteration-varying topologies and initial state errors. Finally, two numerical examples are presented to verify the validity of the proposed D α-type learning scheme in this paper.

show abstract

Finite difference based iterative learning control with initial state learning for a class of fractional order two‐dimensional continuous‐discrete linear systems

Lan

Zheng

2022

Adaptive Control & Signal

View full text Add to dashboard Cite

This article presents a PI-type iterative learning control (ILC) law with initial state learning for a class of 𝛼 (0 < 𝛼 ≤ 1) fractional order two-dimensional (2D) linear systems. First, by using backward difference method, the finite difference approximation of the fractional order derivative is obtained, which leads to globally 2 − 𝛼 order accuracy. Then, a PI-ILC law is constructed at the nodes and the convergence analysis of the iterative scheme is proved. A linear matrix inequality-based sufficient condition is derived to guarantee that the tracking error is asymptotically convergent. The obtained convergence condition is fractional order dependent. Most of the classical ILC conditions for fractional order one-dimensional linear systems fall into the special cases of this article. Finally, the simulation results show the effectiveness of the proposed control method.

show abstract

D‐type iterative learning control for one‐sided Lipschitz nonlinear systems

Cited by 25 publications

References 34 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

Consensus Tracking Via Iterative Learning Control for Singular Fractional-Order Multi-Agent Systems Under Iteration-Varying Topologies and Initial State Errors

Finite difference based iterative learning control with initial state learning for a class of fractional order two‐dimensional continuous‐discrete linear systems

Contact Info

Product

Resources

About