Multi-Objective Optimization of PID Controller for Temperature Control in  Centrifugal Machines Using Genetic Algorithm

Singh, Sanjay Kumar; Boolchandani, sup>D.; Modani, S. G.; Katal, sup>Nitish

doi:10.19026/rjaset.7.464

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…Cascaded DC motor controllers are optimized using multi-objective optimization evolutionary algorithms (MOEAs) selecting different solution on the Pareto-set in [29]. Optimal PID controllers are developed by NSGA-II algorithm, and compared to the traditional Ziegler Nichols methods in [30] and [23].…”

Section: Controller Multi-objective Optimizationmentioning

confidence: 99%

“…In the cited optimization research group, the Pareto solution is determined without researching any further robustness or additional criteria to select the final solution. For example, in [30] the solution is selected based on only the primary objective from the Pareto set of two-objectives without any further investigation or explanation. In papers where weighted aggregation is performed to create singleobjective from multi-objectives, the weights are not mentioned explicitly [26], or defined empirically.…”

Section: Controller Multi-objective Optimizationmentioning

confidence: 99%

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Multi-Scenario Multi-Objective Optimization of a Fuzzy Motor Controller for the Szabad(ka)-II Hexapod Robot

Kecskés¹,

Ódry²

2018

ACTA POLYTECH HUNG

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The aim of this research was to develop a robust motor controller for the Szabad(ka)-II hexapod robot. A Fuzzy-PI controller that utilized a lookup table was chosen because of its reported promising performance and its ability to be embedded in the microcontrollers of the robot. The variables of the controller were defined by a particle swarm optimization method to minimize the five quality objectives related to the walking of the robot. The preferences of the five objectives were successfully expressed by a biasedweighted geometric mean utility function. The resulting optimal solutions were significantly altered by changing the bias and exponential weights of preferences. Therefore, we checked the robustness of the solution against the controller's variables as a secondary objective.

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Section: Controller Multi-objective Optimizationmentioning

confidence: 99%

Section: Controller Multi-objective Optimizationmentioning

confidence: 99%