Balance Adjustment of Power-Line Inspection Robot Using General Type-2 Fractional Order Fuzzy PID Controller

In this study, a general type-2 fractional order fuzzy PID (GT2FO-FPID) controller is proposed to fulfil the balance adjustment of the Power-line Inspection (PLI) robot system. It is a combination of Mamdani general type-2 fuzzy logic controller (GT2-FLC) and fractional PID controller. Since the PLI robot system is an under-actuated system, it’s necessary to get complete information of the system. However, when all state variables are treated as input to the controller, there is a problem with the rule explosion. Because of this, the information fusion method is adopt to solve the problem and simplify the controller design. At the same time, fractional-order integral-differential operators and input-output scaling factors, which are taken as design variables and optimized by genetic algorithm (GA). To assess the performance of proposed controller based on symmetry criterion, we compared it against existing controllers, i.e., interval type-2 fractional order fuzzy PID (IT2FO-FPID), type-1 fractional order fuzzy PID (T1FO-FPID), and conventional fractional order (FOPID) controllers. Furthermore, to show the anti-inference ability of the proposed controller, three common perturbed process are tested. Finally, simulation results show that the GT2FO-FPID controller outperforms other controllers in the presence of external perturbations on the PLI robot system.

[1]  Jerry M. Mendel,et al.  $\alpha$-Plane Representation for Type-2 Fuzzy Sets: Theory and Applications , 2009, IEEE Transactions on Fuzzy Systems.

[2]  Jiahao Liu,et al.  Finite-time control for interval type-2 fuzzy time-delay systems with norm-bounded uncertainties and limited communication capacity , 2019, Inf. Sci..

[3]  Jerry M. Mendel,et al.  Computing the centroid of a general type-2 fuzzy set by means of the centroid-flow algorithm , 2011, IEEE Transactions on Fuzzy Systems.

[4]  G. Bohannan Analog Fractional Order Controller in Temperature and Motor Control Applications , 2008 .

[5]  Hani Hagras,et al.  Toward General Type-2 Fuzzy Logic Systems Based on zSlices , 2010, IEEE Transactions on Fuzzy Systems.

[6]  Songyi Dian,et al.  Delay-dependent stabilization of discrete-time interval type-2 T-S fuzzy systems with time-varying delay , 2017, J. Frankl. Inst..

[7]  Antonio Visioli,et al.  Tuning rules for optimal PID and fractional-order PID controllers , 2011 .

[8]  Hani Hagras,et al.  A Self-Tuning zSlices-Based General Type-2 Fuzzy PI Controller , 2015, IEEE Transactions on Fuzzy Systems.

[9]  Vineet Kumar,et al.  Performance analysis of fractional order fuzzy PID controllers applied to a robotic manipulator , 2014, Expert Syst. Appl..

[10]  Hak-Keung Lam,et al.  Adaptive Sliding Mode Control for Interval Type-2 Fuzzy Systems , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[11]  Junyong Liu,et al.  Dynamic balance control based on an adaptive gain-scheduled backstepping scheme for power-line inspection robots , 2019, IEEE/CAA Journal of Automatica Sinica.

[12]  Jerry M. Mendel,et al.  Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..

[13]  Songyi Dian,et al.  State Feedback Control for Interval Type-2 Fuzzy Systems With Time-Varying Delay and Unreliable Communication Links , 2018, IEEE Transactions on Fuzzy Systems.

[14]  Sofiane Gherbi,et al.  Robust stability and stabilization of networked control systems with stochastic time-varying network-induced delays , 2020 .

[15]  Chin-Teng Lin,et al.  Navigation Control of Mobile Robots Using an Interval Type-2 Fuzzy Controller Based on Dynamic-group Particle Swarm Optimization , 2018, International Journal of Control, Automation and Systems.

[16]  Tomislav Dragicevic,et al.  An optimal general type-2 fuzzy controller for Urban Traffic Network. , 2017, ISA transactions.

[17]  Alain Oustaloup,et al.  Frequency-band complex noninteger differentiator: characterization and synthesis , 2000 .

[18]  Songyi Dian,et al.  Stability and stabilization of T-S fuzzy systems with two additive time-varying delays , 2019, Inf. Sci..

[19]  Jerry M. Mendel,et al.  General Type-2 Fuzzy Logic Systems Made Simple: A Tutorial , 2014, IEEE Transactions on Fuzzy Systems.

[20]  Nasser Sadati,et al.  Design of a fractional order PID controller for an AVR using particle swarm optimization , 2009 .

[21]  Igor Podlubny,et al.  Fractional-order systems and PI/sup /spl lambda//D/sup /spl mu//-controllers , 1999 .

[22]  Yao Chen,et al.  General Type-2 Fuzzy Gain Scheduling PID Controller with Application to Power-Line Inspection Robots , 2019, International Journal of Fuzzy Systems.

[23]  Ligang Wu,et al.  Optimal Guaranteed Cost Sliding-Mode Control of Interval Type-2 Fuzzy Time-Delay Systems , 2018, IEEE Transactions on Fuzzy Systems.

[24]  Jianqiang Yi,et al.  Analysis and Design of Functionally Weighted Single-Input-Rule-Modules Connected Fuzzy Inference Systems , 2018, IEEE Transactions on Fuzzy Systems.

[25]  Vijay Kumar,et al.  A novel interval type-2 fractional order fuzzy PID controller: Design, performance evaluation, and its optimal time domain tuning. , 2017, ISA transactions.

[26]  H. Hagras,et al.  Type-2 FLCs: A New Generation of Fuzzy Controllers , 2007, IEEE Computational Intelligence Magazine.

[27]  Juan R. Castro,et al.  A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems , 2016, Inf. Sci..

[28]  YangQuan Chen,et al.  Tuning and auto-tuning of fractional order controllers for industry applications , 2008 .

[29]  Peng Shi,et al.  Control of Nonlinear Networked Systems With Packet Dropouts: Interval Type-2 Fuzzy Model-Based Approach , 2015, IEEE Transactions on Cybernetics.

[30]  Tao Zhao,et al.  Local stability and stabilization of uncertain nonlinear systems with two additive time-varying delays , 2020, Commun. Nonlinear Sci. Numer. Simul..