In order to solve the problem that the trajectory of a permanent magnet linear motor may be out of bounds in practice and eliminate the influence of uncertainty on trajectory tracking, we propose a robust bounded control based on inequality constraints. First, the dynamics model of the permanent magnet linear motor is established, and then, the constrained output state variables are converted into unconstrained state variables by state conversion. Secondly, based on the dynamics model after state transformation, a novel control method containing both nominal and robust terms is proposed. Finally, the stability of the PMLM system is proved by the Lyapunov minimax method. Simulations and experiments with different algorithms are carried out on the PMLM system and the CSPACE platform. Based on the analysis of the experimental results, it can be found that robust bounded control based on inequality constraints can significantly improve the dynamic performance of the PMLM system and achieve high accuracy.
KimJChoiSChoK, et al. Position estimation using linear hall sensors for permanent magnet linear motor systems. IRE Trans Ind Electron2016; 63(12): 7644–7652.
2.
CaoRChengMMiC, et al. A hybrid excitation flux-switching permanent magnet linear motor for urban rail transit. In: 2011 IEEE vehicle power and propulsion conference, pp. 1–5. New York, NY: IEEE.
3.
KimJChoKJungH, et al. A novel method on disturbance analysis and feed-forward compensation in permanent magnet linear motor system. In 2014 5th International conference on intelligent systems, modelling and simulation, 2014, pp.394–399. New York, NY: IEEE.
4.
El-SousyFFAbuhaselKA. Nonlinear robust optimal control via adaptive dynamic programming of permanent-magnet linear synchronous motor drive for uncertain two-axis motion control system. In 2018 IEEE Industry Applications Society annual meeting (IAS), 2018, pp.1–12. New York, NY: IEEE.
5.
LeMDGongZMLinW. Motion profile design to reduce residual vibration of high-speed positioning stages. IEEE/ASME Trans Mechatron2009; 14(2): 264–269.
6.
YanMTShiuYJ. Theory and application of a combined feedback–feedforward control and disturbance observer in linear motor drive wire-EDM machines. Int J Mach Tools Manuf2008; 48(3–4): 388–401.
7.
HuJLiuLMaDW, et al. Adaptive nonlinear feedback control of chaos in permanent-magnet synchronous motor system with parametric uncertainty. Proc IMechE, Part C: J Mechanical Engineering Science2015; 229(12): 2314–2323.
8.
LiYZhengLLiangY, et al. Adaptive compensation control of an electromagnetic active suspension system based on nonlinear characteristics of the linear motor. J Vib Control2020; 26(21-22): 1873–1885.
9.
LanYGuoQSunY. Optimization design of speed controller for permanent magnet linear motor servo system. Trans China Electrotech Soc2004; 19(5): 76–72.
10.
LanYPQiuCZhangZX. Design of robust controller for magnetic suspension permanent magnet linear motor suspension subsystem. Modul Mach Tool Autom Manuf Tech2012; (08): 66–69.
11.
GuanYHanQLYaoH, et al. Robust event-triggered controller design for vehicle active suspension systemsNonlinear Dyn2018; 94(1): 627–638.
12.
ChenYXWangYC. Design of robust control system of permanent magnet linear motor. J Huaqiao Univ (Nat Sci)2008; (03): 331–334.
13.
YanLDouMHuaZ, et al. Robustness improvement of FCS-MPTC for induction machine drives using disturbance feedforward compensation technique. IEEE Trans Power Electron2019; 34(3): 2874–2886.
14.
YangCMaTCheZ, et al. An adaptive-gain sliding mode observer for sensorless control of permanent magnet linear synchronous motors. IEEE Access2018; 6: 3469–3478.
15.
LuWTangBJiK, et al. A new load adaptive identification method based on an improved sliding mode observer for PMSM position servo system. IEEE Trans Power Electron2021; 36(3): 3211–3223.
16.
ZhaoXWangTJinH. Intelligent second-order sliding mode control for permanent magnet linear synchronous motor servo systems with robust compensator. IET Electric Power Appl2020; 14(9): 1661–1671.
17.
BousserhaneIBouchetaAHazzabA, et al. Fuzzy-sliding controller design for single sided linear induction motor position control. In: EUROCON 2007-The International Conference on" Computer as a Tool, 2007, pp.1942–1947. New York, NY: IEEE.
18.
LiWPLuoBHuangH. Active vibration control of flexible joint manipulator using input shaping and adaptive parameter auto disturbance rejection controller. J Sound Vib2016; 363: 97–125.
19.
ChuMChenGHuangFJ, et al. Active disturbance rejection control for trajectory tracking of manipulator joint with flexibility and friction. Appl Mech Mater2013; 325-326: 1229–1232.
20.
MesloubHBenchouiaMTGoléaA, et al. Predictive DTC schemes with pi regulator and particle swarm optimization for PMSM drive: comparative simulation and experimental study. Int J Adv Manuf Technol2016; 86: 3123–3134.
21.
WangZHuCZhuY, et al. Neural network learning adaptive robust control of an industrial linear motor-driven stage with disturbance rejection ability. IEEE Trans Ind Inform2017; 13(5): 2172–2183.
22.
ZhangDChenYAiW, et al. Precision motion control of permanent magnet linear motors. Int J Adv Manuf Technol2007; 35(3–4): 301–308.
23.
PrabuMJPoongodiPPremkumarK. Fuzzy supervised online coactive neuro-fuzzy inference system-based rotor position control of brushless DC motor. IET Power Electron2016; 9(11): 2229–2239.
24.
LinFJShenPHKungYS. Adaptive wavelet neural network control for linear synchronous motor servo drive. IEEE Trans Magn2005; 41(12): 4401–4412.
25.
YaoBHuCLuL, et al. Adaptive robust precision motion control of a high-speed industrial gantry with cogging force compensations. IEEE Trans Control Syst Technol2011; 19(5): 1149–1159.
26.
LuLYaoBWangQ, et al. Adaptive robust control of linear motors with dynamic friction compensation using modified lugre model. Automatica2009; 45(12): 2890–2896.
27.
QuynhNXNanWYYenVT. A novel robust adaptive control using RFWNNs and backstepping for industrial robot manipulators with dead-zone. J Intell Robot Syst2020; 98(3-4): 679–692.
28.
ChenSLHuangSTanKK. Relay-based force ripple and friction modeling for the permanent magnet linear motor. In: 2008 10th International conference on control, automation, robotics and vision, pp.2015–2019. New York, NY: IEEE.
29.
SlotineJLiWP. Applied nonlinear control. Beijing: China Machine Press, 1991.
30.
CorlessMLeitmannG. Continuous state feedback guaranteeing uniform ultimate boundedness for uncertain dynamic systems. IEEE Trans Automat Contr2003; 26(5): 1139–1144.
31.
TanKKHuangSNLeeTH. Robust adaptive numerical compensation for friction and force ripple in permanent-magnet linear motors. IEEE Trans Magn2002; 38(1): 221–228.
32.
TanKKLeeTHDouH, et al. PWM modeling and application to disturbance observer-based precision motion control. In: PowerCon 2000. 2000 International conference on power system technology. proceedings (Cat. No. 00EX409), 2000, vol. 3, pp.1669–1674. New York, NY: IEEE.
