Use of Soft Computing Components to Improve Transient/Steady-State of Control Systems

  • PenChen Chou
  • TsiChian Hwang
  • TsiChow Chang
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 4)

In recent years, soft computing techniques have been widely applied to the controller design of a variety of control systems in which fuzzy-logic controllers are used for unknown plant or mathematical model that is highly nonlinear or hardly derived. Knowledge engineers translate the idea of control discipline from expert operators to applicable fuzzy inference systems (FIS). Secondly, artificial neural networks (NN) are wisely used for pattern recognition, classification, function approximations, fault diagnosis, prediction of stock markets, and neural controls. The necessary requirement for neural control relies on the possibility of identifying result as well as the learning algorithm suitable for the specified problem. Both fuzzylogic controllers (FLC) and neural controllers (NC) are nonlinear types; however, NC shows smoother variation than does FLC. Besides, ranges of input/output fuzzy sets are dominant selection factors. With wrong selection or no inclusion of corresponding scaling factors for scaling the input/output range, even an FLC, without knowing the characteristics of the plant, would not work properly. This restriction is apparently relieved by NC control. As to NC design, identification of dynamic systems becomes important; otherwise, there is no information from which the NC can learn. If an NC is used in an open-loop control for the convenience of learning an easy to apply, robustness is not generally achieved, whether on parameter variation or noise immunity capability. On the other hand, if NC is used in a closed-loop way, availability of weights becomes difficult because the learning algorithm can no longer be applied. Therefore, smart optimization algorithms such as genetic algorithms (GA) or particle swarm intelligence (PSO) can be candidates for finding the best values of weights for NC.


Fuzzy Inference System Neural Controller Fuzzy Logic Controller Inverted Pendulum Soft Computing Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • PenChen Chou
    • 1
  • TsiChian Hwang
    • 1
  • TsiChow Chang
    • 1
  1. 1.EE DepartmentDaYeh UniversityChina

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