Trajectory control of robotic manipulators using neural networks

Ozaki, Takeshi; Suzuki, Tatsuya; Furuhashi, Takeshi; Okuma, Shigeru; Uchikawa, Yoshiki

doi:10.1109/41.87587

Cited by 210 publications

(78 citation statements)

References 8 publications

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“…Therefore, advanced driver assistance systems may perfectly use this kind of system. In addition, an autonomous robot, and, in general, any kind of autonomous navigation system, may eventually benefit from the use of this cooperative, extremely fast, and reliable TNNs to make their navigation safer by detecting the contour of different possible objects that surround them [37], [38].…”

Section: Resultsmentioning

confidence: 99%

Reconfigurable Hardware Architecture of a Shape Recognition System Based on Specialized Tiny Neural Networks With Online Training

Moreno¹,

Alarcon²,

Salvador³

et al. 2009

IEEE Trans. Ind. Electron.

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Abstract-Neural networks are widely used in pattern recognition, security applications, and robot control. We propose a hardware architecture system using tiny neural networks (TNNs) specialized in image recognition. The generic TNN architecture allows for expandability by means of mapping several basic units (layers) and dynamic reconflguration, depending on the application speciflc demands. One of the most important features of TNNs is their learning ability. Weight modiflcation and architecture reconflguration can be carried out at run-time. Our system performs objects identiflcation by the interpretation of characteristics elements of their shapes. This is achieved by interconnecting several specialized TNNs. The results of several tests in different conditions are reported in this paper. The system accurately detects a test shape in most of the experiments performed. This paper also contains a detailed description of the system architecture and the processing steps. In order to valídate the research, the system has been implemented and conflgured as a perceptron network with back-propagation learning, choosing as reference application the recognition of shapes. Simulation results show that this architecture has signiflcant performance beneflts.

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Section: Resultsmentioning

confidence: 99%

Reconfigurable Hardware Architecture of a Shape Recognition System Based on Specialized Tiny Neural Networks With Online Training

Moreno¹,

Alarcon²,

Salvador³

et al. 2009

IEEE Trans. Ind. Electron.

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“…There have been some developments in the use of neural networks for the control purposes, Miller et al (1987); Miyamoto et al (1988); Ozaki et al (1991); Saad et al (1994). In general, neural network control design has two steps.…”

Section: Tracking Control Of the Joint Self-impact Systemmentioning

confidence: 99%

Tracking control of a planar five-link bipedal walking system with point contact, considering self-impact joint constraint by adaptive neural network method

Bazargan-Lari

Eghtesad

Khoogar

et al. 2015

Lat. Am. j. solids struct.

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In order to achieve the practical characteristics of natural bipedal walking, a key feature is to realize "the straight knee state of walking" during stance and swing motions. Considering a straight knee necessitates that the shank link of each leg not to undergo the rotation angles which are greater than that of the thigh link. For this purpose, various methods have been proposed; the joint selfimpact constraint has been suggested for energy-efficient (natural) bipedal walking while realizing the straight knee constraint. The prominent objective of this research is to present a model based control method for trajectory tracking of a normal humanlike bipedal walking, by considering the joint self-impact constraint. To achieve this objective, the dynamical equations of motion of an unconstrained biped are taken, developed and then modified to consider the joint self-impact constraint at the knee joint. To control this complicated dynamical system, the available anthropometric normal gait cycle data are taken to generate the desired trajectories of the thigh and knee joints of the self-impact biped. Due to the existence of complex nonlinear terms in the dynamical governing equations of self-impact biped, the authors propose to design a nonlinear intelligent controller by taking advantage of the adaptive neural network control method, which neither requires the evaluation of inverse dynamical model nor the time consuming training process. According to the simulation results, the tracking control of the biped robot is accomplished well and the biped walking seems naturally, despite of involving complex nonlinear terms in the dynamical governing equations of the self-impact biped.

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“…Maintenance and sensor failure detection was reported in [82], check valves operating in a nuclear power plant [57], [114], and vibration monitoring in rolling element bearings [2]. It was widely applied in feedback control [19], [40], [52], [59], [87], [89], [109], [110] and fault diagnosis of robotic systems [116]. This structure was also used in a temperature control system [63], [64], monitoring feed water flow rate and component thermal performance of pressurized water reactors [61], and fault diagnosis in a heat exchanger continuous stirred tank reactor system [102].…”

Section: A Mlpsmentioning

confidence: 99%

“…They can be classified into some major methods, such as supervised control, inverse control, neural adaptive control, back-propagation of utility (which is an extended method of a back-propagation through time) and adaptive critics (which is an extended method of reinforcement learning algorithm) [2]. MLP structures were used for digital current regulation of inverter drives [16], to predict trajectories in robotic environments [19], [40], [52], [73], [79], [87], [89], [110], to control turbo generators [117], to monitor feed water flow rate and component thermal performance of pressurized water reactors [61], to regulate temperature [64], and to predict natural gas consumption [65]. Dynamical versions of MLP networks were used to control a nonlinear dynamic model of a robot [60], [97], to control manufacturing cells [92], and to implement a programmable cascaded low-pass filter [101].…”

Section: Process Controlmentioning

confidence: 99%

A comprehensive review for industrial applicability of artificial neural networks

Meireles

Almeida

Simões

2003

IEEE Trans. Ind. Electron.

365

130

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Abstract-This paper presents a comprehensive review of the industrial applications of artificial neural networks (ANNs), in the last 12 years. Common questions that arise to practitioners and control engineers while deciding how to use NNs for specific industrial tasks are answered. Workable issues regarding implementation details, training and performance evaluation of such algorithms are also discussed, based on a judiciously chronological organization of topologies and training methods effectively used in the past years. The most popular ANN topologies and training methods are listed and briefly discussed, as a reference to the application engineer. Finally, ANN industrial applications are grouped and tabulated by their main functions and what they actually performed on the referenced papers. The authors prepared this paper bearing in mind that an organized and normalized review would be suitable to help industrial managing and operational personnel decide which kind of ANN topology and training method would be adequate for their specific problems.

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Trajectory control of robotic manipulators using neural networks

Cited by 210 publications

References 8 publications

Reconfigurable Hardware Architecture of a Shape Recognition System Based on Specialized Tiny Neural Networks With Online Training

Reconfigurable Hardware Architecture of a Shape Recognition System Based on Specialized Tiny Neural Networks With Online Training

Tracking control of a planar five-link bipedal walking system with point contact, considering self-impact joint constraint by adaptive neural network method

A comprehensive review for industrial applicability of artificial neural networks

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