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EVOLUTIONARY TUNING OF MODULAR FUZZY CONTROLLER FOR TWO-WHEELED WHEELCHAIR

Department of Mechatronics, International Islamic University Malaysia, 50728 Kuala Lumpur, Malaysia

School of Computing and Intelligent Systems, Cognitive Robotics Group, University of Ulster, Northland Road, Londonderry, NI, UK

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M. O. TOKHI

Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK

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https://doi.org/10.1142/S1469026812500095Cited by:2 (Source: Crossref)

Abstract

In this work, an optimization technique is adopted to manipulate the input and output scaling factors of a modular fuzzy logic controller (MFC) for lifting and stabilizing the front wheels of a wheelchair in two-wheeled mode. A virtual wheelchair (WC) model is developed within Visual Nastran (VN) software environment where the model is further linked with Matlab/Simulink for control purposes. The lifting of the chair is done by transforming the first link (Link1), attached to the front wheels (casters) to the upright position while maintaining stability of the second link (Link2) where the payload is attached. General rules of thumb allow heuristic tuning (trial and error) of the parameters but such heuristic method does not guarantee that the system tuned with current data set will represent future system states. A global optimization mechanism such as genetic algorithm is necessary to improve the system performance. Due to its significant advantages over other searching methods, a genetic algorithm approach is used to optimize the scaling factors of the MFC and results show that the optimized parameters give better system performance for such a complex, highly nonlinear two-wheeled wheelchair system.

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