Multiple Neuro-Adaptive Control of Robot Manipulators Using Visual Cues

Lee, Choon-Young; Lee, J.-J.

doi:10.1109/tie.2004.841080

Cited by 16 publications

(2 citation statements)

References 28 publications

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“…In a multi-robot system, if there exist two robots that work together to sweep in a workspace, it may be viewed as a multiple neural network system [58]. Each robot needs one neural network and all robots share the same workspace information.…”

Section: Model Algorithmmentioning

confidence: 99%

Biologically Inspired Intelligence with Applications on Robot Navigation

Luo¹,

Jan²,

Chu³

et al. 2018

Artificial Intelligence - Emerging Trends and Applications

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Biologically inspired intelligence technique, an important embranchment of series on computational intelligence, plays a crucial role for robotics. The autonomous robot and vehicle industry has had an immense impact on our economy and society and this trend will continue with biologically inspired neural network techniques. In this chapter, multiple robots cooperate to achieve a common coverage goal efficiently, which can improve the work capacity, share the coverage tasks, and reduce the completion time by a biologically inspired intelligence technique, is addressed. In many real-world applications, the coverage task has to be completed without any prior knowledge of the environment. In this chapter, a neural dynamics approach is proposed for complete area coverage by multiple robots. A bio-inspired neural network is designed to model the dynamic environment and to guide a team of robots for the coverage task. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting neural equation. Each mobile robot treats the other robots as moving obstacles. Each robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot position. The proposed model algorithm is computationally simple. The feasibility is validated by four simulation studies.

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Section: Model Algorithmmentioning

confidence: 99%

Biologically Inspired Intelligence with Applications on Robot Navigation

Luo¹,

Jan²,

Chu³

et al. 2018

Artificial Intelligence - Emerging Trends and Applications

View full text Add to dashboard Cite

show abstract

“…Furthermore, the traditional adaptive robust controllers are based on the assumption that the plant parameters are constant, which is not guaranteed in many cases. For example, when a robot manipulator picks up an object, its payload jumps [5], [6]. If the load changes suddenly in a servomechanism, the traditional ARC may lead to a long-time transient response and a large output tracking error.…”

mentioning

confidence: 99%

Identifier-Based Adaptive Robust Control for Servomechanisms With Improved Transient Performance

Zhang

Chen

2010

IEEE Trans. Ind. Electron.

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Abstract-This paper focuses on the adaptive robust control (ARC) for servomechanisms whose dynamic models are subject to unknown parameters, disturbance, and parameter sudden changes. To improve the control performance of the traditional ARC, a novel identifier-based ARC (IFARC) scheme is proposed. In this scheme, an identifier is utilized to accelerate the parameter tuning process and to heighten the accuracy of parameter estimation. A switching logic component based on a given performance index is introduced to select the better parameter estimate vector from those provided by the identifier and the adaptation law. As a result, transient performance can be improved according to the certainty equivalence principle. In addition, the exact reconstruction of the unknown parameters and exponential decay of the tracking error can be achieved under certain conditions. The stability and performance of IFARC are theoretically analyzed. Finally, simulation results show that the IFARC can achieve favorable tracking performance.

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Sliding Mode Control (SMC) of Robot Manipulator via Intelligent Controllers

Kapoor

Ohri

2016

J. Inst. Eng. India Ser. B

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Inspite of so much research, key technical problem, naming chattering of conventional, simple and robust SMC is still a challenge to the researchers and hence limits its practical application. However, newly developed soft computing based techniques can provide solution. In order to have advantages of conventional and heuristic soft computing based control techniques, in this paper various commonly used intelligent techniques, neural network, fuzzy logic and adaptive neuro fuzzy inference system (ANFIS) have been combined with sliding mode controller (SMC). For validation, proposed hybrid control schemes have been implemented for tracking a predefined trajectory by robotic manipulator, incorporating structured and unstructured uncertainties in the system. After reviewing numerous papers, all the commonly occurring uncertainties like continuous disturbance, uniform random white noise, static friction like coulomb friction and viscous friction, dynamic friction like Dhal friction and LuGre friction have been inserted in the system. Various performance indices like norm of tracking error, chattering in control input, norm of input torque, disturbance rejection, chattering rejection have been used. Comparative results show that with almost eliminated chattering the intelligent SMC controllers are found to be more efficient over simple SMC. It has also been observed from results that ANFIS based controller has the best tracking performance with the reduced burden on the system. No paper in the literature has found to have all these structured and unstructured uncertainties together for motion control of robotic manipulator.Keywords Non-linear system Á Sliding mode control (SMC) Á Heuristic intelligent controllers Á Neural network (NN) Á Fuzzy logic (FL) and adaptive neuro fuzzy inference system (ANFIS)

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Multiple Neuro-Adaptive Control of Robot Manipulators Using Visual Cues

Cited by 16 publications

References 28 publications

Biologically Inspired Intelligence with Applications on Robot Navigation

Biologically Inspired Intelligence with Applications on Robot Navigation

Identifier-Based Adaptive Robust Control for Servomechanisms With Improved Transient Performance

Sliding Mode Control (SMC) of Robot Manipulator via Intelligent Controllers

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