INTELLIGENT FRACTIONAL ORDER ITERATIVE LEARNING CONTROL USING FEEDBACK LINEARIZATION FOR A SINGLE-LINK ROBOT
AbstractIn this paper, iterative learning control (ILC) is combined with an optimal fractional order derivative (BBO-Da-type ILC) and optimal fractional and proportional-derivative (BBO-PDa-type ILC). In the update law of Arimoto's derivative iterative learning control, a first order derivative of tracking error signal is used. In the proposed method, fractional order derivative of the error signal is stated in term of 'sa' where Â to update iterative learning control law. Two types of fractional order iterative learning control namely PDa-type ILC and Da-type ILC are gained for different value of a. In order to improve the performance of closed-loop control system, coefficients of both Â and Â learning law i.e. proportional , derivative Â and Â are optimized using Biogeography-Based optimization algorithm (BBO). Outcome of the simulation results are compared with those of the conventional fractional order iterative learning control to verify effectiveness of BBO-Da-type ILC and BBO-PDa-type ILC
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How to Cite
Ghasemi, I., Ranjbar Noei, A., & Sadati, J. (2017). INTELLIGENT FRACTIONAL ORDER ITERATIVE LEARNING CONTROL USING FEEDBACK LINEARIZATION FOR A SINGLE-LINK ROBOT. IIUM Engineering Journal, 18(1), 155–176. https://doi.org/10.31436/iiumej.v18i1.563
Mechatronics and Automation Engineering