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A switching-based collaborative fractional order fuzzy logic controllers for robotic manipulators

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Abstract In the present work, a collaborative implementation of fractional order proportional-integral-derivative control and fractional order fuzzy logic control method is investigated for the controlled tracking performance of a manipulator… Click to show full abstract

Abstract In the present work, a collaborative implementation of fractional order proportional-integral-derivative control and fractional order fuzzy logic control method is investigated for the controlled tracking performance of a manipulator plant. An integral time absolute error measured between the desired and actual output trajectories, which is obtained by the optimal parameters, drives the switching mechanism between the two controllers, fractional order proportional-integral-derivative and fractional order fuzzy logic control, in order to implement the collaboration scheme. Different possible ways of switching mechanism between fractional order proportional-integral-derivative controller and fractional order fuzzy logic control scheme are explored. The resulting controller's parameters are investigated with meta-heuristic technique namely cuckoo search algorithm. The performance of the proposed collaborative scheme is compared with existing control schemes and basic proportional-integral-derivative control method for obtaining the controlled tracking performance. To generalize the use and effectiveness of designed control scheme, the simulation studies are also investigated for a first order plant and an inverted pendulum for the trajectory tracking problem. To analyse the effects of the parameter variations, disturbance rejection, and payload variations, the performance results for the proposed scheme are also presented for robotic manipulator. The proposed controller has a potential application in robotic manipulator control for different applications.

Keywords: control; order; order fuzzy; fractional order; fuzzy logic

Journal Title: Applied Mathematical Modelling
Year Published: 2019

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