Robust Control Systems with Genetic Algorithms (Control Series)
Robust Control Systems Genetic Algorithms by Jamshidi Krohling Renato Dos Coelho
The resulting two-phase algorithm explores the orthogonal array in Taguchi method to conduct a series of experiments so that key parameters pertaining to the control factors, noise factors, and quality factors can be determined. In the first phase, a matrix-type experiment is conducted to determine the configuration for parameter optimization.
The second phase then applies parameter optimization method to determine the controller parameter that leads to robust performance. The combined two-phase approach is effective and efficient in controller synthesis. The proposed algorithm is applied to a control-design benchmark problem. The resulting design is shown to have a superior performance to other existing controllers.
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Robust Control Systems with Genetic Algorithms
Log In. Paper Titles. Article Preview. Applied Mechanics and Materials Volumes Main Theme:. Innovation for Applied Science and Technology. Edited by:. Wen-Hsiang Hsieh. Online since:. In every physical system, there are number of factors that cause uncertainty in the performance.
A robot arm is an example of such systems. Although QFT design technique has been successfully used for plants having structured parameter uncertainty, there are some difficulties that a designer encounters. In this paper we investigated the effects of parameter uncertainties of a SCARA robot on frequency response of open loop system.
With consideration of important parameters, the next step in QFT design procedure is loop-shaping. In the presented method the controller is designed directly by choosing and optimization of coefficients of transfer function by using genetic algorithm. In optimization procedure, stability and bounds of the system were considered as the constraints of the problem. Non-linear simulations on the tracking problem are performed and the results highlight the success of the designed controllers.
The results indicate that applying the proposed technique successfully overcomes the obstacles to robust control of non-linear SCARA robots.
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Robust Control Systems with Genetic Algorithms | Sensor Review | Vol 23, No 3
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