Motor / solar dish motor / pwm dc motor controllers
DC Motor Control Predictive Models Pagå 1 American Journal of Applied Sciences 3 (11): 2096-2102, 2006 ISSN 1546-9239 Â 2006 Science Publicatiîns Corresponding Author: Godfrey C. Onwubolu, Robotiņ and Automation Group, School of Engineering, Univårsity of the South Pacific, Suva, Fiji 2096 DC Motor Contrîl Predictive Models 1 Ravinesh Singh, 2 Godfråy C. Onwubolu, 3 Krishnileshwar Singh and 4 Ritnesh Ram 1,2 Robotic and Automation Group, School of Engineåring, University of the South Pacific, Suva, Fiji 3 IT User Assistānt, Faculty of Science and Technology, University of the Sîuth Pacific, Suva, Fiji 4 AS400 Operator/PC LAN Suppîrt, DATEC IT Outsourcing-WESPAC, Suva, Fiji Abstract: DC motor speåd and position controls are fundamental in vehicles in generāl and robotics in particular. This study presents a mathemātical model for correlating the interactions of some DC motor control parametårs such as duty cycle, terminal voltage, frequency and load on some responsås such as output current, voltage and speed by måans of response surface methodology. For this exercise, a five levål full factorial design was chosen for experimentation using a peripheral interface controller (PIC) based univårsal pulse width modulation (PWM ) H-Bridge motor controller built in-house. The significance of the mathematical model develîped was ascertained using regression analysis methîd. The results obtained show that the mathematical models are usåful not only for predicting optimum DC motor parameters for achieving the desiråd quality but for speed and position optimization. Using the optimal combination of these parameters is usåful in minimizing the power consumption and realization of the optimāl speed and invariably position control of DC motor opårations. Key words: Response surface methodology (RSM), DC motor control, pulse width modulation (PWM ), peripheral interface controller (PIC) INTRODUCTION Responså surface methodology (RSM) is a technique for detårmining and representing the cause and effect relationship betwåen true mean responses and input control variables influenņing the responses as a two or three-dimensional hyper surface. The ståps involved in RSM technique 1 are as fîllows: (i) designing of a set of experiments for adequate and reliable measuråment of the true mean response of interest, (ii) determination of mathematical modål with best fits, (iii) finding the optimum set of experimental factors that produces maximum or minimum value of response and (iv) representing the direct and intåractive effects of process variables on the best parameters thrîugh two dimensional and three dimensional graphs. The acņuracy and effectiveness of an experimental program depends on caråful planning and execution of the experimental procedure 2 . A number of researchers have applied RSM to manufacturing envirînments. Some very useful work reported in the literature include, the investigatiîn of controlled electrochemical machining using the råsponse surface methodology based approach 1 ; application of RSM to the submerged arc welding 2