This paper deals with robotic systems trajectory tracking problems by designing a new event-triggered sliding mode control (ET-SMC) algorithm with signal quantization. More precisely, an event-triggered control strategy is introduced to the sliding mode control algorithm with robustness to reduce the controller update frequency, so as to reduce the network communication resources consumption and maintain the control accuracy. In addition, the dynamic quantization method is adopted between the controller and the actuator for more communication efficiency. Unlike periodic time-triggered control strategy, a novel event triggering condition which requires no state-dependent variables is discussed for less triggering threshold computations. Furthermore, the minimum interval of adjacent triggering instant based on the new triggering condition can be obtained to avoid the Zeno phenomenon. Finally, simulation results demonstrate the validity of the presented control algorithm and practical experiments with a PHANToM Omni robotic device are given to verify the advanced performances. As a result, the trajectory tracking error is limited within a small range and the control update frequency is evidently reduced.
DuZLiWLiJ, et al. Event-triggered H-Infinity Control for continuous-time singular networked cascade control systems with state delay. IEEE Access2020; 8: 2760–2771.
2.
XavierNBandyopadhyayB. Practical sliding mode using state depended intermittent control. IEEE Trans Circuits Syst II Express Briefs2021; 68(1): 341–345.
3.
FerraraAIncremonaGP. Design of an integral suboptimal second-order sliding mode controller for the robust motion control of robot manipulators. IEEE Trans Control Syst Technol2015; 23(6): 2316–2325.
4.
TruongTNVoATKangHJ. A backstepping global fast terminal sliding mode control for trajectory tracking control of industrial robotic manipulators. IEEE Access2021; 9: 31921–31931.
5.
KlugelMMamduhiMAyanO, et al. Joint cross-layer optimization in real-time networked control systems. IEEE Trans Control Netw Syst2020; 7(4): 1903–1915.
6.
ChiXZhangYJiaX. Event-triggered predictive control for networked systems based on a dual-rate sampling switched observer. IEEE Access2021; 9: 89580–89592.
7.
LinLCaoJRutkowskiL. Robust event-triggered control invariance of probabilistic Boolean control networks. IEEE Trans Neural Netw Learn Syst2020; 31(3): 1060–1065.
8.
XuTHaoYDuanZ. Fully distributed containment control for multiple Euler-Lagrange systems over directed graphs: an event-triggered approach. IEEE Trans Circuits Syst I Regul Pap2020; 67(6): 2078–2090.
9.
WangYLShiPLimCC, et al. Event-triggered fault detection filter design for a continuous-time networked control system. IEEE Trans Cybern2016; 46(12): 3414–3426.
10.
ChengPHeSStojanovicV, et al. Fuzzy fault detection for Markov jump systems with partly accessible hidden information: an event-triggered approach. IEEE Trans Cybern2022; 52: 7352–7361.
11.
ZhuGMaYLiZ, et al. Event-triggered adaptive neural faulttolerant control of underactuated MSVS with input saturation. IEEE Trans Intell Transp Syst2022; 23: 7045–7057.
12.
HaoLYZhangHGuoG, et al. Quantized sliding mode control of Unmanned Marine Vehicles: various thruster faults tolerated with a unified model. IEEE Trans Syst Man Cybern Syst2019; 51(3):2012–2016.
13.
LiuQLiuMYuJ. Adaptive fault-tolerant control for attitude tracking of flexible spacecraft with limited data transmission. IEEE Trans Syst Man Cybern Syst2021; 51: 4400–4408.
14.
SunHHouLZongG, et al. Adaptive decentralized neural network tracking control for uncertain interconnected nonlinear systems with input quantization and time delay. IEEE Trans Neural Netw Learn Syst2020; 31(4): 1401–1409.
15.
YuanZXiongYSunG, et al. Event-triggered quantized communication-based consensus in multiagent systems via sliding mode. IEEE Trans Cybern2022; 52: 3925–3935.
16.
ZhaoKChenJ. Adaptive neural quantized control of MIMO nonlinear systems under actuation faults and time-varying output constraints. IEEE Trans Neural Netw Learn Syst2020; 31(9): 3471–3481.
17.
XueYZhengBCYuX. Robust sliding mode control for TS fuzzy systems via quantized state feedback. IEEE Trans Fuzzy Syst2018; 26(4): 2261–2272.
18.
MamduhiMHMaityDHircheS, et al. Delay-sensitive joint optimal control and resource management in multi-loop networked control systems. IEEE Trans Control Netw Syst2021; 8: 1093–1106.
19.
VoATKangHJ. A novel fault-tolerant control method for robot manipulators based on non-singular fast terminal sliding mode control and disturbance observer. IEEE Access2020; 8: 109388–109400.
20.
BaekJKwonWKangC. A new widely and stably adaptive sliding-mode control with nonsingular terminal sliding variable for robot manipulators. IEEE Access2020; 8: 43443–43454.
21.
NorsahperiNMHDanapalasingamKA. An improved optimal integral sliding mode control for uncertain robotic manipulators with reduced tracking error, chattering, and energy consumption. Mech Syst Signal Process2020; 142: 106747.
22.
WangJRuTXiaJ, et al. Asynchronous event-triggered sliding mode control for semi-markov jump systems within a finite-time interval. IEEE Trans Circuits Syst I Regul Pap2021; 68(1): 458–468.
23.
CaoZNiuYLamHK, et al. Sliding mode control of method. IEEE Trans Fuzzy Syst2020;29(10): 2902–2915.
24.
YaoDLiHLuR, et al. Distributed sliding-mode tracking control of second-order nonlinear multiagent systems: an event-triggered approach. IEEE Trans Cybern2020; 50(9): 3892–3902.
25.
HuangFZhangWChenZ, et al. RBFNN-based adaptive sliding mode control design for nonlinear bilateral teleoperation system under time-varying delays. IEEE Access2019; 7: 11905–11912.
26.
TangJHuangFChenZ, et al. Disturbance-observerbased sliding mode control design for nonlinear bilateral teleoperation system with four-channel architecture. IEEE Access2019; 7: 72672–72683.
27.
JiYWuWFuH, et al. Passivity-based filtering for networked semi-Markov robotic manipulators with mode-dependent quantization and event-triggered communication. Int J Adv Robot Syst2021; 18(2): 1729881420939864.
28.
MaCShiPZhaoX, et al. Consensus of Euler-Lagrange systems networked by sampled-data information with probabilistic time delays. IEEE Trans Cybern2015; 45(6): 1126–1133.
29.
HaoLYParkJHYeD. Integral sliding mode fault-tolerant control for uncertain linear systems over networks with signals quantization. IEEE Trans Neural Netw Learn Syst2017; 28(9): 2088–2100.
30.
ChenLLiuMHuangX, et al. Adaptive fuzzy sliding mode control for network-based nonlinear systems with actuator failures. IEEE Trans Fuzzy Syst2018; 26(3): 1311–1323.
31.
MustafaADharNKVermaNK. Event-triggered sliding mode control for trajectory tracking of nonlinear systems. IEEE/CAA Journal of Automatica Sinica2020; 7(1): 307–314.
32.
YanYYuSSunC. Quantization-based event-triggered sliding mode tracking control of mechanical systems. Inf Sci2020; 523: 296–306.
33.
FanXWangZ. Event-triggered sliding mode control for singular systems with disturbance. Nonlinear Anal Hybrid Syst2021; 40: 101011.
34.
SansanayuthTNilkhamhangITungpimolratK. Teleoperation with inverse dynamics control for phantom omni haptic device. In: 2012 Proceedings of SICE Annual Conference (SICE), pp.2121–2126. IEEE.
