On position/force tracking control problem of cooperative robot manipulators using adaptive fuzzy backstepping approach

Baigzadehnoe, Barmak; Rafiee, Zahra; Khosravi, Alireza; Rezaie, Behrooz

doi:10.1016/j.isatra.2017.07.029

Cited by 96 publications

(44 citation statements)

References 25 publications

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“…By using small-gain approaches, Zhang et al [20] and Su et al [21] designed adaptive fuzzy backstepping schemes for uncertain nonlinear systems. Moreover, many other neural and fuzzy backstepping controllers have been recently employed in a variety of applications, ranging from robotic manipulators [22][23][24] to spacecrafts [25][26][27]. However, although promising alternatives have already been proposed [28,29], the explosion of complexity, which is an inherent issue in backstepping, still poses some difficulties for the implementation of control schemes based on this method.…”

Section: Introductionmentioning

confidence: 99%

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Bessa

Brinkmann

Duecker

et al. 2018

Mathematical Problems in Engineering

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Mechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems.

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Section: Introductionmentioning

confidence: 99%

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Bessa

Brinkmann

Duecker

et al. 2018

Mathematical Problems in Engineering

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show abstract

“…The former is used to ensure high control accuracy and fast dynamic response in the whole control process; meanwhile, the latter is mainly used to provide extra robustness against unknown lumped disturbance. When the control performance is relatively satisfactory, the fast-TSM-type reaching law will dominate the whole combined reaching law (9) and bring in continuous high control performance. On the other hand, when the control errors tend to increase, the adaptive gainK( ) will increase rapidly and then the latter part will take over the combined reaching law.…”

Section: Tdc Scheme Design With Antsm Dynamicsmentioning

confidence: 99%

“…control accuracy, faster convergence, and stronger robustness have been clearly observed with our newly proposed method under measurement noise. (i) to analyze, we can see that when the reference trajectory changes suddenly, the adaptive mechanism will generate large extra control Figure 7: Simulation results of Case three: (a) and (b) are control errors within initial phase t [1,3]sec for joints 1 and 2, respectively; (c) and (d) are control errors within peak phase t [6,9]sec for joints 1 and 2, respectively; (e) and (f) are control errors within steady phase t [12,15]sec for joints 1 and 2, respectively. efforts enforcing the robot manipulator to accurately track the reference trajectory.…”

Section: Casementioning

confidence: 99%

“…Thanks to the effective combination of MPC and ISM schemes, satisfactory comprehensive control performance has been obtained with the proposed control scheme. In [9], a novel adaptive fuzzy control scheme was proposed for the position/force tracking control purpose of cooperative robot manipulators. By using the fuzzy logic to estimate the unknown system dynamics and adaptive control to ensure high control accuracy, the designed control scheme can ensure good comprehensive control performance.…”

Section: Introductionmentioning

confidence: 99%

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A New Model-Free Trajectory Tracking Control for Robot Manipulators

Wang

Zhu

Chen

et al. 2018

Mathematical Problems in Engineering

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In this paper, we propose a novel model-free trajectory tracking control for robot manipulators under complex disturbances. The proposed method utilizes time delay control (TDC) as its control framework to ensure a model-free scheme and uses adaptive nonsingular terminal sliding mode (ANTSM) to obtain high control accuracy and fast dynamic response under lumped disturbance. Thanks to the application of adaptive law, the proposed method can ensure high tracking accuracy and effective suppression of noise effect simultaneously. Stability of the closed-loop control system is proved using Lyapunov method. Finally, the effectiveness and advantages of the newly proposed TDC scheme with ANTSM dynamics are verified through several comparative simulations.

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“…The applications of fuzzy systems today are of great relevance in the control of robotic agents, for example, in displacement control applications [3] [4], learning reinforcement for navigation [5] and even for collaborative work among robotic agents [6]. But a robot to navigate and interact with its environment, requires a machine vision system to identify patterns as objects in the scene.…”

mentioning

confidence: 99%

RGB-D training for convolutional neural network with final fuzzy layer for depth weighting

Moreno¹,

Sánchez²,

Ovalle³

2017

ces

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This paper presents training of novel hybrid network based on three deep convolutional neural network architectures applied to object recognition, based on the depth information supplied for a RGBD camera. For this case, the depth information allows to set the dataset of training images of each network, its architecture and its characteristics, generating a dynamic recognition application by variation of the image capture point, whose final layer is determined by a diffuse inference system. The general architecture designed allows an efficient object recognition applicable to robotic mobile agents, whose perspective of the object varies when approaching or moving away from them, showing an overall performance of 90.19%.

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On position/force tracking control problem of cooperative robot manipulators using adaptive fuzzy backstepping approach

Cited by 96 publications

References 25 publications

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

A New Model-Free Trajectory Tracking Control for Robot Manipulators

RGB-D training for convolutional neural network with final fuzzy layer for depth weighting

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