IEEE Transactions on Industrial Electronics

Volume 39, Number 6, Dec 1992 Access to the journal on IEEE XPLORE IE Transactions Home Page

39.6.1	T. Fukuda, T. Shibata, "Theory and applications of neural networks for industrial control systems," IEEE Trans. on Industrial Electronics, vol. 39, no. 6, pp. 472-489, Dec 1992. Abstract Link Full Text Abstract: The theory and the applications of artificial neural networks, especially in a control field, are described. Recurrent networks and feedforward networks are discussed. Application to pattern recognition, information processing, design, planning, diagnosis, and control are examined. Hybrid systems using the neural networks, fuzzy sets, and artificial intelligence (AI) technologies are surveyed
39.6.2	H. Hashimoto, T. Kubota, M. Sato, F. Harashima, "Visual control of robotic manipulator based on neural networks," IEEE Trans. on Industrial Electronics, vol. 39, no. 6, pp. 490-496, Dec 1992. Abstract Link Full Text Abstract: A control scheme for a robotic manipulator system that uses visual information to position and orient the end-effector is described. The control system directly integrates visual data into the servoing process without subdividing the process into determination of the position and orientation of the workplace and inverse kinematic calculation. The feature of the control scheme is the use of neural networks for the determination of the change in joint angles required in order to achieve the desired position and orientation. The proposed system is able to control the robot so that it can approach the desired position and orientation from arbitrary initial ones. Simulations for a robotic manipulator with six degrees of freedom are described. The validity and the effectiveness of the proposed control scheme are verified by computer simulations
39.6.3	T. Fukuda, T. Shibata, M. Tokita, T. Mitsuoka, "Neuromorphic control: adaptation and learning," IEEE Trans. on Industrial Electronics, vol. 39, no. 6, pp. 497-503, Dec 1992. Abstract Link Full Text Abstract: A structure for a neural network-based robotic motion controller is presented. Simulations of both position and force servos are carried out, and the approach is shown to be useful for a nonlinear system in an uncertain environment. The neural network comprises a four-layer network, including input/output layers and two hidden layers. Time delay elements are included in the first hidden layer, so that the neural network can learn dynamics of the system. The authors also implement a new learning method based on fuzzy logic, which is useful to accelerate learning and improve convergence
39.6.4	H.-M. Tai, J. Wang, K. Ashenayi, "A neural network-based tracking control system," IEEE Trans. on Industrial Electronics, vol. 39, no. 6, pp. 504-510, Dec 1992. Abstract Link Full Text Abstract: An application of the backpropagation neural network to the tracking control of industrial drive systems is presented. The merits of the approach lie in the simplicity of the scheme and its practicality for real-time control. Feedback error trajectories, rather than desired and/or actual trajectories, are employed as inputs to the neural network tracking controller. It can follow any arbitrarily prescribed trajectory even when the desired trajectory is changed to that not used in the training. Simulation was performed to demonstrate the feasibility and effectiveness of the proposed scheme
39.6.5	J. Tanomaru, S. Omatu, "Process control by on-line trained neural controllers," IEEE Trans. on Industrial Electronics, vol. 39, no. 6, pp. 511-521, Dec 1992. Abstract Link Full Text Abstract: The question of how to perform online training of multilayer neural controllers in order to reduce the training time is addressed. First, based on multilayer neural networks, structures for a plant emulator and a controller are described. Basic control configurations are briefly presented, and new online training methods, based on performing multiple updating operations during each sampling period, are proposed and described in algorithmic form. One method, the direct inverse control error approach, is effective for small adjustments of the neural controller when it is already reasonably trained; another, the predicted output error approach, directly minimizes the control error and greatly improves convergence of the controller. Simulation and experimental results using a simple plant show the effectiveness of the proposed control structures and training methods
39.6.6	G.L. Dempsey, E.S. McVey, "A Hough transform system based on neural networks," IEEE Trans. on Industrial Electronics, vol. 39, no. 6, pp. 522-528, Dec 1992. Abstract Link Full Text Abstract: Neural-like analog circuitry is suggested for image-to-parameter-space mapping, and a modified Hopfield optimization network is proposed for the parameter space peak detection. Solution time under 50 μs is obtainable with general-purpose operational amplifiers. Example system applications include autonomous navigation, tracking multiple targets, curve following, mensuration, and image recognition
39.6.7	M.S. Obaidat, D.S. Abu-Saymeh, "Methodologies for characterizing ultrasonic transducers using neural network and pattern recognition techniques," IEEE Trans. on Industrial Electronics, vol. 39, no. 6, pp. 529-536, Dec 1992. Abstract Link Full Text Abstract: System hardware for characterizing ultrasonic transducers and the associated data acquisition software and characterizing algorithms are considered. The hardware consists mainly of a workstation computer, a receiver/pulser with gated peak detector, various monitoring devices, a microcomputer-based 3D positioning controller, and an A/D converter. The characterization algorithms are based on neural network and pattern recognition techniques. It is found that artificial neural network techniques provide far better classification results than the pattern recognition techniques. A multilayer backpropagation neural network which provides a classification accuracy of 94% is developed. Two other multilayer neural networks-sum-of-products and a newly devised neural network called hybrid sum-of-products-have a classification accuracy of 90% and 93%, respectively. The most successful pattern recognition technique for this application is found to be the perceptron, which provides a classification accuracy of 77%
39.6.8	K. Saga, T. Sugasaka, M. Sekiguchi, S. Nagata, K. Asakawa, "Mobile robot control by neural networks using self-supervised learning," IEEE Trans. on Industrial Electronics, vol. 39, no. 6, pp. 537-542, Dec 1992. Abstract Link Full Text Abstract: A reinforcement learning algorithm based on supervised learning is described. It uses associative search to discover and learn actions that make the system perform a desired task. One problem with associative search is that the system's actions are often inconsistent. In the searching process, the system's actions are always decided stochastically, so the system cannot perform learned actions more than once, even if they have been determined to be suitable actions for the desired task. To solve this problem, a neural network that can predict an evaluation of an action and control the influence of the stochastic element is used. Results from computer simulations using the algorithms to control a mobile robot are described
39.6.9	H. Kita, H. Odani, Y. Nishikawa, "Solving a placement problem by means of an analog neural network ," IEEE Trans. on Industrial Electronics, vol. 39, no. 6, pp. 543-551, Dec 1992. Abstract Link Full Text Abstract: The effectiveness of the Hopfield model is examined through its application to a circuit block placement problem. The results of computer simulation show that, although the Hopfield model is not effective enough if it is used without sophisticated preexamination of combinatorial problems, it has the ability to yield quite satisfactory solutions when it is endowed with an appropriate form and parameters of the energy function. The meaning of appropriate parameter values yielding good solutions is also investigated theoretically
39.6.10	S.P. Eberhardt, R. Tawel, T.X. Brown, T. Daud, A.P. Thakoor, "Analog VLSI neural networks: implementation issues and examples in optimization and supervised learning," IEEE Trans. on Industrial Electronics, vol. 39, no. 6, pp. 552-564, Dec 1992. Abstract Link Full Text Abstract: Time-critical neural network applications that require fully parallel hardware implementations for maximal throughput are considered. The rich array of technologies that are being pursued is surveyed, and the analog CMOS VLSI medium approach is focused on. This medium is messy in that limited dynamic range, offset voltages, and noise sources all reduce precision. The authors examine how neural networks can be directly implemented in analog VLSI, giving examples of approaches that have been pursued to date. Two important application areas are highlighted: optimization, because neural hardware may offer a speed advantage of orders of magnitude over other methods; and supervised learning, because of the widespread use and generality of gradient-descent learning algorithms as applied to feedforward networks
39.6.11	A. Ishiguro, T. Furuhashi, S. Okuma, Y. Uchikawa, "A neural network compensator for uncertainties of robotics manipulators," IEEE Trans. on Industrial Electronics, vol. 39, no. 6, pp. 565-570, Dec 1992. Abstract Link Full Text Abstract: A neural network controller for trajectory control of robotic manipulators that is used not to internalize the inverse dynamic model of the controlled object but to compensate only the uncertainties of the robotic manipulator is presented. Its performance is compared with that of the conventional adaptive scheme. The results show the ability of the neural network controller to adapt to unstructured effects. A learning method for the neural network compensator with true teaching signals is shown. The tracking error of the robotic manipulator was greatly reduced when this controller was used
39.6.12	W.B. Lawrance, W. Mielczarski, "Harmonic current reduction in a three-phase diode bridge rectifier ," IEEE Trans. on Industrial Electronics, vol. 39, no. 6, pp. 571-576, Dec 1992. Abstract Link Full Text Abstract: A novel method for reducing harmonic currents on the AC supply side of a three-phase bridge rectifier is presented. The principle of the method is to modify the current waveforms in the DC windings of the converter transformer by injecting a third harmonic current into the neutral point of the transformer. Passive LC filters connected between the rectifier output and the secondary neutral point act as third harmonic current sources. The effectiveness of the method is confirmed by laboratory recordings