|International Computer Science and Engineering Society (ICSES)|
International Transactions on Evolutionary and Metaheuristic Algorithms
Vol. 5, No. 2, Dec. 2019
An Optimal PD-Type Iterative Learning Control Design for Precise Position Controls | Original Paper
Minh Y Nguyen a,, Hoang Hua b
Highlights and Novelties
In many industrial applications, the system needs to perform a motion repeatedly in which the conventional feedback controller cannot achieve the desired accuracy. This paper presents a PD-Type algorithm of Iterative Learning Control (ILC) the for repeated position tracking control problems of DC motors. In this scheme, the control action is computed considering not only the error of the system output and references but also the recognition of the previous performance. By this means, the control signal is updated continually which will mitigate the error to an acceptable range after some iterations, i.e., learning operations. The monotonic convergence is employed to formulate the cost function and determine the proper learning operators. The proposed control scheme is applied to control the trajectory of the position of DC motors. The result shows the advantages of ILC versus the conventional control techniques such as PID controllers, etc. After few iterations, the system output will be adjusted close to the reference in the whole trajectory of motions and the accuracy is accepted (10–3).
© Copyright was transferred to International Computer Science and Engineering Society (ICSES) by all the Authors.
Cite this manuscript as
Minh Y Nguyen, Hoang Hua, "An Optimal PD-Type Iterative Learning Control Design for Precise Position Controls," International Transactions on Evolutionary and Metaheuristic Algorithms (ITEMA), vol. 5, no. 2, pp. 1-8, Dec. 2019.
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