In this paper, a general type-2 fuzzy logic controller (GT2FLC), which is optimized by the particle swarm optimization (PSO) algorithm, is applied to a power-line inspection (PLI) robot. The information fusion is used to design the GT2FLC to avoid the rule explosion. The proposed controller has the ability to deal with uncertainties when the PLI robot works on the insulated access cable. In order to compare the performance of the proposed controller with that of other controllers, the type-1 fuzzy logic controller (T1FLC) and the interval type-2 fuzzy logic controller (IT2FLC) are both optimized by the PSO to adjust the PLI robot. To show the ability of different controllers to deal with uncertainties, external disturbances and parameter perturbations are added to the PLI robot. According to simulations, the performance of the proposed controller is better than that of other controllers, and the proposed controller has better ability to deal with uncertainties.
DianS., ChenL., HoangS., et al., Dynamic balance control based on an adaptive gain-scheduled backstepping scheme for power-line inspection robots[J], IEEE/CAA Journal of Automatica Sinica6(1) (2019), 198–208.
2.
FuS., ZuoQ., HouZ.G., et al., Unsupervised learning of categories from sets of partially matching image features for power line inspection robot[C], IEEE International Joint Conference on Neural Networks, IEEE (2008), 2596–2603.
3.
ZhangT.Y., ZhangG.X. and JinJ., Dynamics modeling and analysis for power lines inspection robot[J], Journal of System Simulation20(18) (2008), 4982–4955.
4.
ChenC., WuG., WangQ., et al., Design of the gripper for power lines inspection robot[C], Intelligent Control and Automation, IEEE (2014), 3340–3344.
5.
KatrasnikJ., PernusF. and LikarB., A Survey of Mobile Robots for Distribution Power Line Inspection[J], IEEE Transactions on Power Delivery25(1) (2009), 485–493.
6.
MontambaultS. and PouliotN., Design and Validation of a Mobile Robot for Power Line Inspection and Maintenance[J], Springer Tracts in Advanced Robotics42 (2008), 495–504.
7.
YiJ. and YubazakiN., Stabilization fuzzy control of inverted pendulum systems[J], Artificial Intelligence in Engineering14(2) (2000), 153–163.
8.
TanakaS., XinX. and YamasakiT., Experimental verification of energy-based swing-up control for a rotational pendulum[C], Proceedings of SICE Annual Conference (SICE), IEEE (2012), 534–539.
9.
RahmatM.F., WahidH. and WahabNA., Application of intelligent controller in a ball and beam control system[J], International Journal on Smart Sensing and Intelligent Systems3(1) (2010), 45–60.
10.
AlmutairiN.B. and ZribiM., On the sliding mode control of a ball on a beam system[J], Nonlinear Dynamics59(1-2) (2010), 221.
11.
GhommamJ. and SaadM., Backstepping-based cooperative and adaptive tracking control design for a group of underactuated AUVs in horizontal plan[J], International Journal of Control87(5) (2014), 1076–1093.
12.
LiY., LiuL. and FengG., Robust adaptive output feedback control to a class of non-triangular stochastic nonlinear systems[J], Automatica89 (2018), 325–332.
13.
ChangX.H., Robust Nonfragile H Filtering of Fuzzy Systems With Linear Fractional Parametric Uncertainties[J], IEEE Trans Fuzzy Systems20(6) (2012), 1001–1011.
14.
XieX., YueD. and PengC., Multi-instant observer design of discrete-time fuzzy systems: A ranking-based switching approach[J], IEEE Transactions on Fuzzy Systems25(5) (2017), 1281–1292.
15.
MendelJ.M., LiuF. and ZhaiD., alpha-plane representation for type-2 fuzzy sets: Theory and applications[M], IEEE Press, 2009.
16.
ZhaiD. and MendelJ.M., Computing the Centroid of a General Type-2 Fuzzy Set by Means of the Centroid-Flow Algorithm[J], IEEE Transactions on Fuzzy Systems19(3) (2011), 401–422.
17.
MendelJ.M., General Type-2 Fuzzy Logic Systems Made Simple: A Tutorial[J], IEEE Transactions on Fuzzy Systems22(5) (2014), 1162–1182.
18.
LiuF., An efficient centroid type-reduction strategy for general type-2 fuzzy logic system[J], Information Sciences178(9) (2008), 2224–2236.
19.
ZhaiD. and MendelJ.M., Enhanced Centroid-Flow Algorithm for Computing the Centroid of General Type-2 Fuzzy Sets[J], IEEE Transactions on Fuzzy Systems20(5) (2012), 939–956.
20.
LindaO. and ManicM., Monotone Centroid Flow Algorithm for Type Reduction of General Type-2 Fuzzy Sets[J], IEEE Transactions on Fuzzy Systems20(5) (2012), 805–819.
21.
CastilloO., CervantesL., SoriaJ., et al., A generalized type-2 fuzzy granular approach with applications to aerospace[J], Information Sciences354 (2016), 165–177.
22.
CastilloO., Amador-AnguloL., CastroJ.R., 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[J], Information Sciences354 (2016), 257–274.
23.
EspositoM. and PietroG.D., Interval type-2 fuzzy logic for encoding clinical practice guidelines[J], Knowledge-Based Systems54(C) (2013), 329–341.
24.
ComasD.S., PastoreJ.I., BouchetA., et al., Interpretable interval type-2 fuzzy predicates for data clustering: A new automatic generation method based on self-organizing maps[J], Knowledge-Based Systems, 2017.
25.
CelikE., GulM., AydinN., et al., A comprehensive review of multi criteria decision making approaches based on interval type-2 fuzzy sets[J], Knowledge-Based Systems85 (2015), 329–341.
26.
ZhaoT. and DianS., State feedback control for interval type-2 fuzzy systems with time-varying delay and unreliable communication links[J], IEEE Transactions on Fuzzy Systems26(2) (2018), 951–966.
27.
LamH.K., LiH., DetersC., et al., Control design for interval type-2 fuzzy systems under imperfect premise matching[J], IEEE Transactions on Industrial Electronics61(2) (2014), 956–968.
28.
CastilloO. and MelinP., Intelligent systems with interval type-2 fuzzy logic[J], International Journal of Innovative Computing, Information and Control4(4) (2008), 771–783.
29.
ZhaoT., LiuJ. and DianS., Finite-time control for interval type-2 fuzzy time-delay systems with norm-bounded uncertainties and limited communication capacity[J], Information Sciences483 (2019), 153–173.
30.
WangL., ZhengS., WangX., et al., Fuzzy control of a double inverted pendulum based on information fusion[C], International Conference on Intelligent Control and Information Processing, IEEE (2010), 327–331.
31.
CaraveoC., ValdezF., CastilloO., et al., A New Meta-Heuristics of Optimization with Dynamic Adaptation of Parameters Using Type-2 Fuzzy Logic for Trajectory Control of a Mobile Robot[J], Algorithms10(3) (2017), 85.
32.
LuX.G., LiuM. and LiuJ.X., Design and Optimization of Interval Type-2 Fuzzy Logic Controller for Delta Parallel Robot Trajectory Control[J], International Journal of Fuzzy Systems19(1) (2017), 190–206.
33.
Martinez-SotoR., CastilloO., AguilarL.T., et al., Bio-inspired optimization of fuzzy logic controllers for autonomous mobile robots[C], Fuzzy Information Processing Society, IEEE (2012), 1–6.
34.
GuJ., Optimization of Fuzzy Logic Control Based on Genetic Algorithm[J], Journal of Southeast Univwrsity.
35.
CastilloO., MelinP., KacprzykJ., et al., Type-2 Fuzzy Logic: Theory and Applications[M], Springer Publishing Company, Incorporated (2008), 1998.
36.
CastilloO. and Amador-AnguloL., A generalized type-2 fuzzy logic approach for dynamic parameter adaptation in bee colony optimization applied to fuzzy controller design[J], Information Sciences460 (2018), 476–496.
37.
EberhartR. and KennedyJ., A new optimizer using particle swarm theory[C], International Symposium on MICRO Machine and Human Science, IEEE (2002), 39–43.
38.
ShiY. and EberhartR., Modified particle swarm optimizer[C], Proc of IEEE Icec Conference, 1999.
39.
ShiY. and EberhartR.C., Empirical study of particle swarm optimization[C], Evolutionary Computation, CEC 99, Proceedings of the 1999 Congress on. IEEE1 (2002), 320–324.
40.
EberhartR.C. and ShiY., Particle swarm optimization: Developments, applications and resources[C], Evolutionary Computation, Proceedings of the 2001 Congress on. IEEE1 (2002), 81–86.
41.
CoelloC.A.C., PulidoG.T. and LechugaM.S., Handling multiple objectives with particle swarm optimization[J], IEEE Transactions on Evolutionary Computation8(3) (2004), 256–279.
42.
CraigJohn J.. Introduction to Robotics: Mechanics and Control[M], 1986.
43.
ZhaoT. and DianS., Fuzzy dynamic output feedback H control for continuous-time TS fuzzy systems under imperfect premise matching[J], ISA Transactions70 (2017), 248–259.
44.
ZhaoT. and DianS., Delay-dependent stabilization of discrete-time interval type-2 TCS fuzzy systems with time-varying delay[J], Journal of the Franklin Institute354(3) (2017), 1542–1567.
45.
ZhaoT., HuangM. and DianS., Stability and stabilization of TS fuzzy systems with two additive time-varying delays[J], Information Sciences494 (2019), 174–192.
