Browsing by Author "Mudi, R. K."

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A PID controller with dynamic set-point weighting
(2006)
Dey, Chanchal
;
Mudi, R. K.
;
Lee, T. T.
;
電控工程研究所
;
Institute of Electrical and Control Engineering
A new set point weighting technique is proposed to improve the set point response and load rejection characteristics of Ziegler-Nichols tuned PID controllers. Instead of a fixed (single or multi-valued) set point weighting factor, here dynamic set point weighting is suggested based on the change of error (Delta e) of the controlled variable and normalized dead time of the process under control. The proposed scheme is very simple to implement and can be easily incorporated in an existing control loop. Effectiveness of the proposed scheme is tested on different linear and nonlinear dead time processes under both set point change and load disturbance. Performance of the proposed PID controller with dynamic set point weighting (DSW-PID) is compared with that of a PID controller with variable set point weighting (VSW-PID) reported in the literature as well as Ziegler-Nichols tuned PED controller (ZN-PID) in terms of several performance indices. The result shows a considerably improved performance of the proposed DSW-PID over the others.
Neuro-fuzzy implementation of a self-tuning fuzzy controller
(2006)
Mudi, R. K.
;
Dey, Chanchal
;
Lee, T. T.
;
電控工程研究所
;
Institute of Electrical and Control Engineering
A self-tuning fuzzy PI controller (STFPIC) is designed elsewhere, using parallelly operated two rule-bases; one control rule-base, and the other gain rule-base, each having 49 rules. The output scaling factor (SF) of STFPIC is modified online by a gain updating factor following an operator's strategy. STFPlC is found to provide significantly improved performance for a wide range of processes. This study is an attempt for neuro-fuzzy implementations of STFPIC with considerably lesser number of rules, which are complete and capable of realizing almost similar performance as that of STFPIC. We consider two different structures of the proposed neuro-fuzzy PI controller (NFPIC); called NFPIC-1 and NFPIC-2, having only 50 and 49 rules respectively against 98 original rules of STFPIC. NFPIC-1 is similar in structure to that of STFPIC with two parallel rule-bases, each having 25 rules, whereas, the structure of NFPIC-2 is same as that of a conventional fuzzy controller with a single rule-base. Effectiveness of the developed neuro-fuzzy controllers (NFPIC-1 and NFPIC-2) is demonstrated using second-order linear as well as nonlinear processes.