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Image of Journal of Applied Nonlinear Dynamics
ISSN:2164-6457 (print)
ISSN:2164-6473 (online)
Journal of Applied Nonlinear Dynamics
Miguel A. F. Sanjuan (editor), Albert C.J. Luo (editor)
Miguel A. F. Sanjuan (editor)

Department of Physics, Universidad Rey Juan Carlos, 28933 Mostoles, Madrid, Spain

Email: miguel.sanjuan@urjc.es

Albert C.J. Luo (editor)

Department of Mechanical and Industrial Engineering, Southern Illinois University Ed-wardsville, IL 62026-1805, USA

Fax: +1 618 650 2555 Email: aluo@siue.edu

On Dynamics and Control of an Adaptable Wheeled Climbing Robot

Journal of Applied Nonlinear Dynamics 2(1) (2012) 33--57 | DOI:10.5890/JAND.2012.08.001

A.H. Bazargan; M. Mehrandezh; L. Dai

Industrial Systems Engineering, University of Regina, Canada

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Abstract

This paper introduces a re-configurable wheeled climbing robot, which is capable of doing a multitude of tasks that no other single robot could do in the past. It can climb staircases, move inside empty ducts and pipes, climb up the ropes and poles of varying cross sections, and even jump over obstacles with proper motion coordination. It can also move inside narrow passageways by reconfiguring itself. In this paper, a comprehensive dynamic model of the robot is derived for the first time. A real-time simulator to test different control strategies by a human operator using conventional human-machine interfaces has been developed. This simulator can be employed to quickly size the electromechanical actuators and synthesize different control strategies.The data obtained was further used to design a human-analogous autonomous controller. A novel human-analogous control strategy based on an Adaptive Network-based Fuzzy Inference System (ANFIS) was implemented to control the position of the robot climbing a straight pole against gravity, based on data obtained from the real-time Human-In-The-Loop (HITL) simulator. Relevant input/output data were stored, filtered, and used offline to tune the parameters of an ANFIS-based controller. The ANFIS controller whose parameters were optimized was then implemented on the real system autonomously. Based on the information obtained via the HITL simulator system, the controller can extrapolate needed data for untrained cases. This control strategy provides some advantages over conventional controllers; for example, this avoids actuator saturation, minimizes the energy expenditure, and circumvents the gain-scheduling process.

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