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Mimo fuzzy control for autonomous mobile robot

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This paper addresses the design and implementation of multi-input multi-output (MIMO) fuzzy control for mobile robot. Firstly, MIMO fuzzy control is apply to track different desired trajectories. Secondly, the controller performs on robot for navigation purpose to avoid obstacles and reach defined target. | Journal of Automation and Control Engineering Vol. 4, No. 1, February 2016 MIMO Fuzzy Control for Autonomous Mobile Robot Thoa T. Mac1, 2, Cosmin Copot1 , Robin De Keyser1, Trung D. Tran2, and Thich Vu2 1 Department of Electrical Energy, Systems and Automation, Ghent University School of Mechanical Engineering, Hanoi University of Science and Technology (HUST) 2 Abstract— This paper addresses the design and implementation of multi-input multi-output (MIMO) fuzzy control for mobile robot. Firstly, MIMO fuzzy control is apply to track different desired trajectories. Secondly, the controller performs on robot for navigation purpose to avoid obstacles and reach defined target. The proposed MIMO fuzzy controller was investigated based on several conducted MATLAB simulation scenarios for mobile robot. The simulation results are presented to demonstrate the effectiveness of our new control algorithm. hardware, software, or a combination of both. Fuzzy logic approach to control problems mimics how a person would make decisions. The main advantages of a fuzzy navigation strategy lie in the ability to extract heuristic rules from human experience, and to obviate the need for an analytical model of the process. It is tolerant to noise and error in the information coming from the sensory system, and most importantly it is a factual reflection of the behavior of human expertise. This kind of controller is a form of artificial decision-making system. Several tasks of robot are investigated by using fuzzy logic control. Tracking trajectory is performed on mobile robot in [1] [2] [9]. In the first paper, the authors propose a path following approach based on a fuzzy-logic set of rules, which emulates the human driving behavior. The input to the fuzzy system is represented by approximate information concerning the next bend ahead the vehicle; the corresponding output is the cruise velocity that the vehicle needs to attain in order to safely drive on the path. [2] It has .