POSITION AND ORIENTATION CONTROL OF A MOBILE ROBOT USING INTELLIGENT ALGORITHMS BASED HYBRID CONTROL STRATEGIES

Abstract

ABSTRACT

This paper investigates the balancing and tracking control of the mobile robot using a strongly integrated controller. The two independently motorized wheels in this mechatronic system track the target reference and investigate a balancing at the gravity center above the axis of the wheels' rotation where model fluctuations and an external disruption are included in the consideration. In this work, the innovative controller is presented and tested as a coupling controller based on the notions to satisfy considered design objectives. The proposed controller depends on linking several algorithms with each other, where the integrated controller design passes through three phases that are sequential and dependent on each other. The input-output data of TWBMR generated from the closed loop control system is used to develop a neural network model. In this study, the neural networks model can be trained offline and then transferred into a process where adaptive online learning is carried out using Adaptive Network-Based Fuzzy Inference System ANFIS to improve the system performance. The simulation results verify that the considered identification and control strategies can achieve favorable control performance. The ANFIS control design approach does not require an accurate model of the plant. In addition, high-level knowledge of the system is not needed to build a set of rules for a fuzzy controller.  ANFIS achieved acceptable tracking accuracy in compared to FLC. Evaluation of navigation and balance abilities for TWMR are tested with different scenarios, the designed controller is investigated to observe the behavior of the robot on various targets, and its effectiveness is validated. The most significant advantages of designed controllers are that it renders the control system insensitive to external disturbances and model uncertainty.