Tracking Control of Robot Manipulators with Unknown Models: A Jacobian-Matrix-Adaption Method

Chen, Dechao; Zhang, Yinyan; Li, Shuai

doi:10.1109/tii.2017.2766455

Cited by 173 publications

(61 citation statements)

References 49 publications

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“…In the formulation of tracking control objective function, only the kinematic model of the manipulator is considered. The tracking control algorithm based on kinematic models are intensively studied for the control of the manipulator as shown by recent works [17], [37]. Apart from the academic research, kinematic control is also widely used in commercial robotic systems such as ping-pong manipulator [38], ABB IRB 360 [39], Adept Quattro 650HS [40], DOBOT, and UR 10 manipulator.…”

Section: A Tracking Controlmentioning

confidence: 99%

“…Similarly, several algorithms have been proposed to increase the repeatability of the manipulator during long-term operation [16]. Other approaches to improve the repeatability of the manipulator involves the learning algorithm to estimate the kinematic model of the manipulator in real-time [17]. The learning algorithm continually adapts to variation in the system model and compensate for them in real-time.…”

Section: Introductionmentioning

confidence: 99%

“…It should be further noted that the tracking controller presented in this paper is designed on position-level as opposed to velocity-level as done by the most traditional works on tracking control of redundant manipulators [11], [17], [19]. This approach is advantageous because it does not require that initial position of end-effector to lie on the reference trajectory, whereas the velocity-level controllers explicitly require moving the end-effector to the initial point on the reference trajectory.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach

Khan

Luo

2020

IEEE Trans. Ind. Inf.

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Section: A Tracking Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach

Khan

Luo

2020

IEEE Trans. Ind. Inf.

Self Cite

206

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“…Tracking control and correction: [164], [174], [175] [167], [180], [182] [184], [186], [190] [191] Collaboration: [179], [187] Mobile Robots Localization: [165], [169], [172] Navigation: [166], [168], [171] [173], [181], [188] Collaboration: [176]- [178], [185] Other operations: [163], [183], [192] to be one of the most efficient control schemes for complex industrial processes due to the difficulty in obtaining accurate mathematical control models [155] and to the frequent existence of nonlinearities and stochastic disturbances [156]. In fact, data delivery latency is among the most active topics in the NCS field recently.…”

Section: Stationary Robotsmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

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“…In recent decades, path planning has had important applications in many areas, such as mobile robots [1][2][3][4][5][6], unmanned aerial vehicle (UVA) [7,8], game artificial intelligence (AI) automatic pathfinding [9], etc. [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Dynamic Path Planning of Mobile Robots: A Novel Hybrid Heuristic Optimization Algorithm

Chen

Wang

et al. 2019

Sensors

Self Cite

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Mobile robots are becoming more and more widely used in industry and life, so the navigation of robots in dynamic environments has become an urgent problem to be solved. Dynamic path planning has, therefore, received more attention. This paper proposes a real-time dynamic path planning method for mobile robots that can avoid both static and dynamic obstacles. The proposed intelligent optimization method can not only get a better path but also has outstanding advantages in planning time. The algorithm used in the proposed method is a hybrid algorithm based on the beetle antennae search (BAS) algorithm and the artificial potential field (APF) algorithm, termed the BAS-APF method. By establishing a potential field, the convergence speed is accelerated, and the defect that the APF is easily trapped in the local minimum value is also avoided. At the same time, by setting a security scope to make the path closer to the available path in the real environment, the effectiveness and superiority of the proposed method are verified through simulative results.

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Tracking Control of Robot Manipulators with Unknown Models: A Jacobian-Matrix-Adaption Method

Cited by 173 publications

References 49 publications

Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach

Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach

Data Management in Industry 4.0: State of the Art and Open Challenges

Real-Time Dynamic Path Planning of Mobile Robots: A Novel Hybrid Heuristic Optimization Algorithm

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