To break through the limitation of conventional iterative learning control algorithm (ILC) that requires a fixed target, a new ILC algorithm is designed for tracking the varying-amplitude but morphologically similar targets. First, the problem is formulated, in which the tracking target is selected as varying-amplitude and biased-measured. Then, the process of the ILC algorithm based on a variable-gain-proportional-integral scheme is discussed, in which the magnitude coefficient is defined and calculated by orthogonal projection and utilised to redefine the error and build the refreshment algorithm of the input. Next, the convergence of the algorithm is analysed and the applicational simulation is conducted. Results show that the new ILC algorithm has the ability in dealing with the trajectory of the varying but morphologically similar target and the applicational scenario of the ILC algorithm is expanded.
AltinBWillemsJOomenT, et al. (2017) Iterative learning control of iteration-varying systems via robust update laws with experimental implementation. Control Engineering Practice62: 36–45.
2.
ArimotoSKawamuraSMiyazakiF (1984) Bettering operation of robots by learning. Journal of Robotic Systems1(2): 123–140.
3.
BedwaniWIsmailO (2001) Genetic optimization of variable structure PID control systems. In: Proceedings ACS/IEEE international conference on computer systems and applications, Beirut, Lebanon, 25–29 June, pp. 27–30. Piscataway, NJ: IEEE.
4.
BernardW (1987) Adaptive inverse control. Adaptive Systems in Control and Signal Processing1(1): 1–5.
5.
BondiPCasalinoGGambardellaL (1988) On the iterative learning control theory for robotic manipulators. IEEE Journal on Robotics and Automation4(1): 14–22.
6.
BoudjedirCBoukhetalaDBouriM (2019) Iterative learning control of multivariable uncertain nonlinear systems with nonrepetitive trajectory. Nonlinear Dynamics95(3): 2197–2208.
7.
ChenCDengCWangD (2021) Investigation of effect of the train body vibration on measurement of fluctuation pressure on the surface of high-speed train. Measurement168(1): 108442.
8.
ChenCNieXTangM (2013) Transfer function model of the air pressure inside CRH2 EMU under outside air pressure. China Railway Science34(4): 84–88.
9.
ChiRHouZXuJ (2008) Adaptive ILC for a class of discrete-time systems with iteration-varying trajectory and random initial condition. Automatica44(8): 2207–2213.
10.
DengCChenCSunQ, et al. (2020) Interference elimination of wall fluctuation pressure test signal of high-speed train based on phase space reconstruction and vibration interference model. Fluctuation and Noise Letters19(2): 2050020.
11.
DorfRBishopR (2016) Modern Control Systems. Upper Saddle River, NJ: Addison-Wesley.
12.
KapaniaNGerdesJ (2015) Path tracking of highly dynamic autonomous vehicle trajectories via iterative learning control. In: 2015 American control conference (ACC), Chicago, IL, 1–3 July. Pp. 2753–2758. Piscataway, NJ: IEEE.
13.
KikuchiK (2009) Continuous pressure waves generated by a train running in a tunnel. Journal of Low Frequency Noise Vibration & Active Control28(2): 117–132.
14.
LeeJLeeKKimW (2000) Model-based iterative learning control with a quadratic criterion for time-varying linear systems. Automatica36(5): 641–657.
15.
LiSZhouX (2009) Study on the control of pressure fluctuation in CRH2 Type 200 km/h EMUs. Electric Drive for Locomotives2009(2): 6–7.
16.
LiXXuJHuangD (2015a) Iterative learning control for nonlinear dynamic systems with randomly varying trial lengths. International Journal of Adaptive Control and Signal Processing29(11): 1341–1353.
17.
LiZHouZYinC (2015b) Iterative learning control for train trajectory tracking under speed constrains with iteration-varying parameter. Transactions of the Institute of Measurement and Control37(4): 485–493.
18.
LiuTChenMChenX, et al. (2019) Field test measurement of the dynamic tightness performance of high-speed trains and study on its influencing factors. Measurement138: 602–613.
19.
MooreKXuJ (2000) Special issue on iterative learning control. International Journal of Control73(10): 819–823.
20.
NorrlofM (2002) An adaptive iterative learning control algorithm with experiments on an industrial robot. IEEE Transactions on Robotics and Automation18(2): 245–251.
21.
OwensD (2016) Iterative Learning Control: An Optimization Paradigm. London: Springer.
22.
OwensDChuB (2010) Modelling of non-minimum phase effects in discrete-time norm optimal iterative learning control. International Journal of Control83(10): 2012–2027.
23.
RuanXChenFWanB (2008) Decentralized iterative learning controllers for nonlinear largescale systems to track trajectories with different magnitudes. Acta Automatica Sinica34(4): 426–432.
24.
SeelTSchauerTRaischJ (2011) Iterative learning control for variable pass length systems. IFAC Proceedings Volumes44(1): 4880–4885.
25.
ShenD (2016) Random Iterative Learning Control. Beijing, China: Science Press.
26.
ShenDLiX (2019) Iterative Learning Control for Systems with Iteration-varying Trial Lengths: Synthesis and Analysis. Berlin: Springer.
27.
StevenJ (2015) Linear Algebra with Applications. New York: Pearson.
28.
SuzukiH (1996) A review of research trends on passengers’ aural discomfort caused by rail tunnel pressure change. RTRI Report10(10): 41.
29.
TayebiA (2004) Adaptive iterative learning control for robot manipulators. Automatica40(7): 1195–1203.
30.
UchiyamaM (1978) Formation of high-speed motion pattern of a mechanical arm by trial. Transactions of the Society of Instrument and Control Engineers14(6): 706–712.
31.
WuBWangDPohE (2015) High precision satellite attitude tracking control via iterative learning control. Journal of Guidance, Control, and Dynamics38(3): 528–534.
32.
XuJ (1997) Direct learning of control efforts for trajectories with different magnitude scales. Automatica33(12): 2191–2195.
33.
YuMLiC (2017) Robust adaptive iterative learning control for discrete-time nonlinear systems with time-iteration-varying parameters. IEEE Transactions on Systems, Man, and Cybernetics: Systems47(7): 1737–1745.
