In this research, an adaptive tracking control method for the nonholonomic robot system is addressed based on the hybrid Proportional–Integral–Derivative (PID) technique. The proposed hybrid PID scheme first applies the merits of the traditional PID method, with the online self-learning capability for the PID – gains, to force tracking errors to zero in the presence of uncertainties. Then, in order to improve the tracking performance, an adaptive Fuzzy Neural Networks (FNN) approximator and an adaptive robust controller type-compensator are utilized to relax the uncertainties problems of the robot control system. Moreover, the nonholonomic constraint force stability of the mobile manipulator robot is also considered by an adaptive control scheme. The design of online updating laws for the proposed controllers and FNN approximator are designed by applying the Lyapunov stability theorem. Thus, besides the improvement for tracking control performance, the stability of the proposed control system is also maintained. The effectiveness, robustness and adaptability of the proposed control strategy are verified by comparative numerical simulation results.
YamamotoYYunX.Effect of the dynamic interaction on coordinated control of mobile manipulators. IEEE Trans Robot Autom1996; 12: 816–824.
2.
WatanabeKSatoKIzumiK, et al. Analysis and control for an omnidirectional mobile manipulator. J Intell Robot Syst2000; 27: 3–20.
3.
TanJXiNWangY.Integrated task planning and control for mobile manipulators. Int J Robot Res2003; 22: 337–354.
4.
TsaiCCChengMBLinSC.Dynamic modeling and tracking control of a nonholonomic wheeled mobile manipulator with dual arms. J Intell Robot Syst2006; 47: 317–340.
5.
InoueFMuralamiTIhnishiK.A motion control of mobile manipulator with external force. IEEE/ASME Trans Mechatron2001; 6: 137–142.
6.
DongW.On trajectory and force tracking control of constrained mobile manipulators with parameter uncertainty. Automatica2002; 38: 1475–1484.
7.
LiZGeSSMingA.Adaptive robust motion/force control of holonomic-constrained non-holonomic mobile manipulators. IEEE Trans Syst Man Cybern B2007; 37: 607–616.
8.
LiZGeSSAdamsM, et al. Robust adaptive control of uncertain force/motion constrained nonholonomic mobile manipulators. Automatica2008; 44: 776–784.
9.
LiZKangY.Dynamic coupling switching control incorporating support vector machines for wheeled mobile manipulators with hybrid joints. Automatica2010; 46: 832–1352.
10.
LiZYangYLiJ.Adaptive motion/force control of mobile under actuated manipulators with dynamics uncertainties by dynamic coupling and output feedback. IEEE Trans Contr Syst Technol2010; 18: 1068–1079.
11.
LiZLiJKangY.Adaptive robust coordinated control of multiple mobile manipulators interacting with rigid environments. Automatica2010; 46: 2028–2034.
12.
AndaluzVRobertiFToiberoJM, et al. Adaptive unified motion control of mobile manipulators. Control Eng Pract2012; 20: 1337–1352.
13.
Gong-BoDYen-ChenL.Distributed coordination and cooperation control for networked mobile manipulators. IEEE Trans Ind Electron2017; 64: 5065–5074.
14.
ZhijunLTingZFeiC, et al. Reinforcement learning of manipulation and grasping using dynamical movement primitives for a humanoidlike mobile manipulator. IEEE/ASME Trans Mechatron2018; 23: 121–131.
15.
MouC.Robust tracking control for self-balancing mobile robots using disturbance observer. IEEE/CAA J Autom Sini2017; 4: 458–465.
16.
Mahdi SouzanchiKAliasgharAMohammadR, et al. Robust impedance control of uncertain mobile manipulators using time-delay compensation. IEEE Trans Cont Syst Tech2018; 26: 1942–1953.
17.
LinSGoldenbergAA.Neural-network control of mobile manipulators. IEEE Trans Neural Netw2001; 12: 1121–1133.
18.
LeeCYEomTDLeeJJ.Neuro-adaptive control of mobile manipulators base on compensation of approximation error. Electron Lett2002; 38: 935–936.
19.
LiZYangCGuJ.Neuro-adaptive compliant force/motion control for uncertain constrained wheeled mobile manipulator. Int J Robot Autom2007; 22: 206–214.
20.
XuDZhaoDYiJ, et al. Trajectory tracking control of omidirectional wheeled mobile manipulators: robust neural network-based sliding mode approach. IEEE Trans Syst Man Cybern, Part B. Cybern2009; 39: 788–799.
21.
LinXBolinLShuaiL, et al. Design and analysis of FTZNN applied to the real-time solution of a nonstationary lyapunov equation and tracking control of a wheeled mobile manipulator. IEEE Trans Ind Inf2018; 14: 98–105.
22.
DechaoCYunongZ.Robust zeroing neural-dynamics and its time-varying disturbances suppression model applied to mobile robot manipulators. IEEE Trans Neural Netw2018; 29: 4385–4397.
23.
TingZYueyueLZhijunL, et al. Adaptive control and optimization of mobile manipulation subject to input saturation and switching constraints. IEEE Trans Automat Sci Eng2019; 16: 1543–1555.
24.
XiaogangYMeiLLongJ, et al. New zeroing neural network models for solving nonstationary sylvester equation with verifications on mobile manipulators. IEEE Trans Ind Informa2019; 15: 5011–5022.
25.
RaniMKumarNSinghHP.Singh: motion/force control scheme for electrically driven cooperative multiple mobile manipulators. Control Eng Pract2019; 88: 52–64.
26.
MbedeJBElePAbiaCMM, et al. Intelligent mobile manipulator navigation using adaptive neuro-fuzzy systems. Inf Sci2005; 171: 447–474.
27.
ThanglongMYaonanW.Adaptive force/motion control system based on recurrent fuzzy wavelet CMAC neural networks for condenser cleaning crawler-type mobile manipulator robot. IEEE Trans Control Syst Technol2014; 22: 1973–1982.
28.
ThangLongMYaoNanW.Adaptive position tracking system and force control strategy for mobile robot manipulators using fuzzy wavelet neural networks. J Intell Robot Syst2015; 79: 317–340.
29.
Di-HuaZYuanqingX.Adaptive fuzzy control of multilateral asymmetric teleoperation for coordinated multiple mobile manipulators. IEEE Trans Fuzzy Syst2016; 24: 57–70.
30.
LewisFLLiuKYesildirekA.Neural net robot controller with guaranteed tracking performance. IEEE Trans Neural Netw1995; 6: 703–713.
31.
JangJSRSunCTMizutaniE.Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Hoboken, NJ: New Jersey Prentice-Hall, 1997.
32.
HoDWCZhangPAXuJH.Fuzzy wavelet networks for function learning. IEEE Trans Fuzzy Sys2001; 9: 200–211.
33.
YilmazSOysalY.Fuzzy wavelet neural network models for prediction and identification of dynamical systems. IEEE Trans Neural Netw2010; 21: 1599–1609.
34.
LuCH.Wavelet fuzzy neural networks model for identification and predictive control of dynamical systems. IEEE Trans Ind Electron2011; 58: 3046–3058.
DavanipoorMZekriMSheikholeslamF.Fuzzy wavelet neural network with an accelerated hybrid learning algorithm. IEEE Trans Fuzzy Syst2012; 20: 463–469.
37.
LewisFLDarrenMDChaoukiTA.Robot manipulator control: theory and pratice. New York, NY: Marcel Dekker, Inc, 2004.
38.
AhmetDTzong-EnL.Integrated PID-type learning and fuzzy control for flexible-joint manipulators. J Intell Robot Syst1997; 18: 47–66.
39.
YoungjinCWanKC.PID performance tuning methods for a robotic manipulator based on ISS. Asian J Cont2008; 5: 206–216.
40.
MezaJLSantibanezVSotoR, et al. Llama: fuzzy self-tuning PID semiglobal regulator for robot manipulators. IEEE Trans Ind Electron2012; 59: 2709–2717.
41.
WenYJacobR.Neural PID control of robot manipulators with application to an upper limb exoskeleton. IEEE Trans Cybern2013; 43: 673–684.
42.
Lih-ChangLKoksalE.Trajectory tracking control for a 3-DOF parallel manipulator using fractional-order PIλDμ control. IEEE Trans Ind Electron2014; 61: 3417–3426.
43.
Jose de JesusRPanuncioCLuis AlbertoP.PID anti-vibration control of a robotic arm. IEEE Latin America Trans2016; 14: 3144–3150.
44.
DongshengGFengXLaichengY.New pseudoinverse-based path-planning scheme with PID characteristic for redundant robot manipulators in the presence of noise. IEEE Trans Cont Syst Tech2018; 26: 2008–2019.
45.
Prada-JiménezVNiáo-SuarézPAPortilla-FloresEA, et al. Tuning a PD+ controller by means of dynamic optimization in a mobile manipulator with coupled dynamics. IEEE Access2019; 7: 124712–124726.
46.
YongpingPXiangLHaoyongY.Efficient PID tracking control of robotic manipulators driven by compliant actuators. IEEE Trans Contr Syst Technol2019; 27: 915–922.
47.
MienVMichalisMShuzhiSG.An adaptive backstepping nonsingular fast terminal sliding mode control for robust fault tolerant control of robot manipulators. IEEE Trans Syst Man Cyber Syst2019; 49: 1448–1458.
48.
AliKK.PID-based target tracking control of a tractor-trailer mobile robot.Proc IMechE, Part C: J Mechanical Engineering Science2019; 233: 4776–4787.
49.
KaramiMTavakolpour-SalehARNorouziA.Optimal nonlinear PID control of a micro-robot equipped with vibratory actuator using ant colony algorithm: simulation and experiment. J Intell Robot Syst2020; 99: 773–796.
50.
YingTLing-YuSMing-LuZ, et al. Fuzzy PID control mobile manipulator with passive suspension. AMM2014; 490–491: 920–924.
51.
ShojaeiKKazemyA.Adaptive neural feedback linearizing control of type (m,s) mobile manipulators with a guaranteed prescribed performance. Robotica2019; 37: 1937–1955.
52.
ShojaeiK.An adaptive output feedback proportional-integral-derivative controller for n-link type (m,s) electrically driven mobile manipulators. J Dyn Sys Meas Cont2019; 141: 091001.
53.
RonghuaiQAmirKWilliamWM.Modeling, tracking, vibration and balance control of an underactuated mobile manipulator (UMM). Control Eng Pract2019; 93: 104159.
54.
ChenSHoWChouJ.Robust controllability of T-S fuzzy-model-based control systems using sliding-mode technology. IEEE Trans Fuzzy Syst2009; 17: 1324–1335.
