By Cornelius T. Leondes
Within the aggressive company enviornment businesses needs to constantly attempt to create new and higher items quicker, extra successfully, and extra affordably than their rivals to realize and retain the aggressive virtue. Computer-aided layout (CAD), computer-aided engineering (CAE), and computer-aided production (CAM) at the moment are the commonplace. those seven volumes provide the reader a entire therapy of the suggestions and purposes of CAD, CAE, and CAM.
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Extra resources for Computer-Aided Design Engineering and Manufacturing Systems Techniques and Applications
An event-based controller is composed of a control part and a diagnosis part. The control part is composed of a goal-driven-planner (GDP) and a control-output-generator (COG). The diagnosis part is made up of a simulator and an event-based model. These two parts are managed by the central controller which provides a main control algorithm. The logic of the control is as follows: • The event-based controller receives an external input Xc from threshold sensors, and sends control signals Yc to a plant if no error is detected.
The scheme may be viewed as a combined method of time-based diagnosis and state-based control. Experimental results showed that this scheme could be used to control a complex nonlinear plant and could be associated with the higher knowledge-based task management modules to construct more autonomous control system in further works [30, 31, 16]. Acknowledgments The authors would like to thank Dr. J. J. Song for his helpful suggestions. 10 Result of CSTR Plant Control. References 1. K. S. Narendra and K.
19. -J. Luh and B. P. Zeigler, “Abstracting Event-Based Control Models for High Autonomy Systems,” IEEE Trans. on Systems, Man and Cybernetics, vol. 23, pp. 42–54, JANUARY/FEBRUARY 1993. 20. C. L. Giles, G. M. Kuhn, and R. J. Williams, “Dynamic Recurrent Neural Networks: Theory and Applications,” IEEE Trans. on Neural Networks, vol. 5, pp. 153–155, Mar. 1994. 21. J. J. Song, Intelligent Control of Chemical Processes Using Neural Networks and Fuzzy Systems. D. thesis, KAIST, 1993. 22. P. J. Ramadge and W.