Control of Omnidirectional Robot Using Z-Number-Based Fuzzy System

The control of mobile robots in an uncertain environment that is densely cluttered with dynamic obstacles is an important problem in robotics. A fuzzy set theory is one of the important tools for describing uncertainty and designing control system of the mobile robot. In this paper, a Z-number-based fuzzy inference system for control of the omnidirectional soccer robot is proposed. To design the fuzzy control system of the robot, at first stage, the modeling of the kinematics and dynamics of the omnidirectional four-wheel-driven soccer robot was carried out. Then the design of control system is implemented for the linear and angular speed. Using Z-number-based fuzzy rule base and interpolative reasoning the Z-number-based fuzzy inference system is proposed. The proposed Z-number-based inference mechanism is then used for control of the omnidirectional robot. The fuzzy controller algorithm has been tested in simulations and provided satisfactory results at runtime. The proposed control algorithm minimizes the deviation between the current plant output signal and the reference signal. The results obtained in this paper demonstrate the suitability of using Z-number-based system in control of soccer robots.

[1]  Sankar K. Pal,et al.  A computational model for the endogenous arousal of thoughts through Z*-numbers , 2017, Inf. Sci..

[2]  Feng-Chun Tai,et al.  Fuzzy decentralized EIF-based pose tracking for autonomous omnidirectional mobile robot , 2014, 2014 International Conference on Machine Learning and Cybernetics.

[3]  Witold Pedrycz,et al.  Fuzzy optimality based decision making under imperfect information without utility , 2013, Fuzzy Optim. Decis. Mak..

[4]  Nabil Derbel,et al.  Fuzzy logic controllers design for omnidirectional mobile robot navigation , 2016, Appl. Soft Comput..

[5]  Sheng-sheng Zhang.  A Model of Based on Z-number and Fuzzy Analytic Hierarchy Process , 2017 .

[6]  Andrea Bonarini,et al.  Concepts and Fuzzy Models for Behavior-Based Robotics , 2003, WILF.

[7]  Vladik Kreinovich,et al.  Z-Numbers and Type-2 Fuzzy Sets: A Representation Result , 2018 .

[8]  Maani Ghaffari Jadidi,et al.  Model-based PI-fuzzy control of four-wheeled omni-directional mobile robots , 2011, Robotics Auton. Syst..

[9]  László T. Kóczy,et al.  Approximate reasoning by linear rule interpolation and general approximation , 1993, Int. J. Approx. Reason..

[10]  Xiang Li,et al.  Motion control of an omnidirectional mobile robot , 2007, ICINCO-RA.

[11]  R. H. Abiyev,et al.  Navigation of mobile robot in dynamic environments , 2012, 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE).

[12]  A. Isidori.  Nonlinear Control Systems , 1985 .

[13]  Guo-Xing Wen,et al.  Fuzzy Neural Network-Based Adaptive Control for a Class of Uncertain Nonlinear Stochastic Systems , 2014, IEEE Transactions on Cybernetics.

[14]  Yeong-Chan Chang.  Intelligent robust control for uncertain nonlinear time-varying systems and its application to robotic systems , 2005, IEEE Trans. Syst. Man Cybern. Part B.

[15]  T. Allahviranloo,et al.  The Parametric Form of Z‐Number and Its Application in Z‐Number Initial Value Problem , 2017, Int. J. Intell. Syst..

[16]  Yeong-Hwa Chang,et al.  Fuzzy Sliding-Mode Formation Control for Multirobot Systems: Design and Implementation , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[17]  Bingyi Kang,et al.  Decision Making Using Z-numbers under Uncertain Environment , 2012 .

[18]  Wei Zheng,et al.  Elman Fuzzy Adaptive Control for Obstacle Avoidance of Mobile Robots Using Hybrid Force/Position Incorporation , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[19]  Rahib H. Abiyev,et al.  Improved Path-Finding Algorithm for Robot Soccers , 2015 .

[20]  Alexander E. Gegov,et al.  Hybrid fuzzy MCDM model for Z-numbers using intuitive vectorial centroid , 2017, J. Intell. Fuzzy Syst..

[21]  S. Kovács,et al.  Distance based similarity measures of fuzzy sets , 2005 .

[22]  Yong Deng,et al.  Total utility of Z-number , 2017, Applied Intelligence.

[23]  Dogan Ibrahim,et al.  Behaviour Tree Based Control for Efficient Navigation of holonomic robots , 2014, Int. J. Robotics Autom..

[24]  László T. Kóczy,et al.  Interpolative reasoning with insufficient evidence in sparse fuzzy rule bases , 1993, Inf. Sci..

[25]  Rahib Hidayat Abiyev.  Z Number Based Fuzzy Inference System for Dynamic Plant Control , 2016, Adv. Fuzzy Syst..

[26]  Pau-Lo Hsu,et al.  The T-S Fuzzy Design and Implementation for Nonlinear Motion Systems with Control Redundancy , 2008 .

[27]  Christopher I. Byrnes,et al.  Output regulation for nonlinear systems: an overview , 2000 .

[28]  G. Bortolan,et al.  A review of some methods for ranking fuzzy subsets , 1985 .

[29]  Alberto Isidori,et al.  Nonlinear control systems: an introduction (2nd ed.) , 1989 .

[30]  Young-Jae Ryoo,et al.  Fuzzy steering control of three-wheels based omnidirectional mobile robot , 2016, 2016 International Conference on Fuzzy Theory and Its Applications (iFuzzy).

[31]  Alexander Gloye,et al.  Holonomic Control of a robot with an omni- directional drive , 2005 .

[32]  Witold Pedrycz,et al.  Approximate Reasoning on a Basis of Z-Number-Valued If–Then Rules , 2017, IEEE Transactions on Fuzzy Systems.

[33]  Uthman A. Baroudi,et al.  A Behavioral Adaptive Fuzzy controller of multi robots in a cluster space , 2016, Appl. Soft Comput..

[34]  Shyi-Ming Chen,et al.  A new interpolative reasoning method in sparse rule-based systems , 1998, Fuzzy Sets Syst..

[35]  Chunhe Xie,et al.  Failure Mode and Effects Analysis based on Z-numbers , 2018 .

[36]  Wen Jiang,et al.  A new medical diagnosis method based on Z-numbers , 2017, Applied Intelligence.

[37]  Hsu-Chih Huang,et al.  Intelligent fuzzy motion control of three-wheeled omnidirectional mobile robots for trajectory tracking and stabilization , 2012, 2012 International conference on Fuzzy Theory and Its Applications (iFUZZY2012).

[38]  Chidentree Treesatayapun,et al.  Linearization based on Fuzzy Rules Emulated Networks for nonaffine discrete-time systems controller , 2009, TENCON 2009 - 2009 IEEE Region 10 Conference.

[39]  Okyay Kaynak,et al.  A servo system control with time-varying and nonlinear load conditions using type-2 TSK fuzzy neural system , 2011, Appl. Soft Comput..

[40]  Rahib H. Abiyev,et al.  Robot Soccer Control Using Behaviour Trees and Fuzzy Logic , 2016 .

[41]  Ali Azadeh,et al.  Z-number DEA: A new possibilistic DEA in the context of Z-numbers , 2016, Adv. Eng. Informatics.

[42]  KEIGO WATANABE,et al.  Feedback Control of an Omnidirectional Autonomous Platform for Mobile Service Robots , 1998, J. Intell. Robotic Syst..

[43]  Yong Deng,et al.  A Method of Converting Z-number to Classical Fuzzy Number , 2012 .

[44]  Simon G. Fabri,et al.  Dual Adaptive Dynamic Control of Mobile Robots Using Neural Networks , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[45]  Anup Kumar Panda,et al.  Fuzzy logic techniques for navigation of several mobile robots , 2009, Appl. Soft Comput..

[46]  Francisco G. Rossomando,et al.  Identification and control of nonlinear dynamics of a mobile robot in discrete time using an adaptive technique based on neural PID , 2014, Neural Computing and Applications.

[47]  Ahmad Fakharian,et al.  Design and Implementation of Self-Adaptive PD Controller Based on Fuzzy Logic Algorithm for Omni-Directional Fast Robots in Presence of Model Uncertainties , 2016, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[48]  Ronald R. Yager,et al.  On Z‐valuations using Zadeh's Z‐numbers , 2012, Int. J. Intell. Syst..

[49]  J. Jim Zhu,et al.  Omni-directional mobile robot controller based on trajectory linearization , 2008, Robotics Auton. Syst..