Adaptive Iterative Learning Control of Robotic Systems Using Backstepping Design

Wang, Ying-Chung; Chien, Chiang‐Ju; Chuang, Chi-Nan

doi:10.1139/tcsme-2013-0047

Cited by 6 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed learning control technique is demonstrated as robust through the payload variation effect studies. In [32], the authors developed backstepping adaptive iterative learning control through integrating the merits of adaptive iterative learning control, the backstepping design approach, and the fuzzy neural network function estimation attribute for a robotic mechanism that conducts repetitive works. The purpose of employing the fuzzy neural network as a fuzzy neural learning segment is to compensate for the unknown certainty equivalent control system, and the purpose of employing the iterative learning control is to compensate for the uncertainties.…”

Section: Iterative Learning Control and Its Variantsmentioning

confidence: 99%

On the Development of Learning Control for Robotic Manipulators

Zhang

Wei

2017

Robotics

View full text Add to dashboard Cite

Learning control for robotic manipulators has been developed over the past decade and to the best of the authors' knowledge, it is still in its infant development stage; the authors believe that it will become one of the most promising directions in the control area in robotic manipulators. Learning control in robotic manipulators is mainly used to address the issue that the friction at the joints of robotic mechanisms and other uncertainties may exist in the dynamic models, which are very complex and may even be impossible to model mathematically. In this paper, the authors review and discuss the learning control in robotic manipulators and some issues in learning control for robotic manipulators are also illustrated. This review is able to give a general guideline for future research in learning control for robotic manipulators.

show abstract

Section: Iterative Learning Control and Its Variantsmentioning

confidence: 99%

On the Development of Learning Control for Robotic Manipulators

Zhang

Wei

2017

Robotics

View full text Add to dashboard Cite

show abstract

“…To improve the accuracy in trajectory tracking, various control schemes such as feedback control [1], robust control [2], and iterative learning control [3][4][5] have been developed. Iterative learning control (ILC) is a control methodology for tracking a desired trajectory in repetitive systems; those were widely applied in practical engineering such as robotics [6,7], semiconductors [8], and chemical processes [9,10]. The prime strategy of ILC algorithms is to refine the input from one trial in order to improve the performance of the system on the next trial.…”

Section: Introductionmentioning

confidence: 99%

High-Order Feedback Iterative Learning Control Algorithm with Forgetting Factor

Wang

Dong

Wang

2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

A novel iterative learning control (ILC) algorithm is proposed to produce output curves that pass close to the desired trajectory.The key advantage of the proposed algorithm is introducing forgetting factor, which is a function of the number of iterations. Due to the forgetting factor characteristic of ILC, the proposed scheme not only stabilizes the nonlinear system with uncertainties but also weakens interference on the tracking desired trajectory. Simulation examples are included to demonstrate feasibility and effectiveness of the proposed algorithm.

show abstract

“…It has been found in simulation outcome that the controller is able to provide exceptionally disturbance rejection and the framework overshoot is decreased to worthy level. Besides, a back-stepping versatile iterative learning control joining fuzzy neural network was implemented to surmise the unidentified and robust learning term to compensate the uncertainty of robotics systems with repetitive errand [9]. Simulation outcomes appeared that the controller allow a good tracking execution for both joint position and joint acceleration.…”

mentioning

confidence: 99%

Controlling the Non-parametric Modeling of Double Link Flexible Robotic Manipulator using Hybrid PID Tuned by PType ILA.

Annisa

Abidin

Darus

et al. 2018

IJIE

View full text Add to dashboard Cite

The wide advancement in different field of life such as domestics and industries make an incredible demand for flexible robot controller. Numerous robot controller applications are categorized as multiple-input-multiple-output (MIMO) frameworks owing to multi-link structure. The design and tuning of multi-loop controllers to meet certain conditions are regularly the pullback factors since there are interaction between the controllers. The framework must be decoupled to diminish the interaction or to form the framework diagonally dominant. Additionally, the existence of vibration on flexible structure of robot controller must be treated at the same time. The ceaseless stress delivered by the vibration can lead to structural deterioration, fatigue, instability and performance degradation. In this way, the decrease of vibration on flexible structure of robot controller is of foremost significance. In spite of the fact that numerous analysts Abstract: Utilization of robotic manipulator with multi-link structure encompasses a great influence in most of the present industries. However, controlling the motion of multi-link manipulator has become a troublesome errand particularly once the flexible structure is employed. As of now, the framework utilizes the complicated arithmetic to resolve desired hub angle with the coupling result and vibration within the framework. Hence, this research aims to develop a dynamic system and controller for double-link flexible robotics manipulator (DLFRM) with the enhancement on hub angle position and vibration concealment. The research utilised neural network because the model estimation supported NARX model structure. In the controllers' development, this research focuses on selftuning controller. P-Type iterative learning algorithm (ILA) control theme was enforced to adapt the controller parameters to fulfill the required performances once there is changes to the system. The hybrid of proportionalintegral-derivate (PID) controller was developed for hub motion and end-point vibration suppression of every link respectively. The controllers were tested in MATLAB/Simulink simulation setting. The performance of the controller was compared with the fixed hybrid PID-PID controller in term of input tracking and vibration concealment. The results indicated that the proposed controller was effective to maneuver the double-link flexible robotic manipulator to the specified position with reduction of the vibration at the tip of the DLFRM structure.

show abstract

Adaptive Iterative Learning Control of Robotic Systems Using Backstepping Design

Cited by 6 publications

References 0 publications

On the Development of Learning Control for Robotic Manipulators

On the Development of Learning Control for Robotic Manipulators

High-Order Feedback Iterative Learning Control Algorithm with Forgetting Factor

Controlling the Non-parametric Modeling of Double Link Flexible Robotic Manipulator using Hybrid PID Tuned by PType ILA.

Contact Info

Product

Resources

About