This paper presents a novel data-driven predictive iterative learning control (DDPILC) scheme based on a new dynamic linearization technique along the iteration axis for a class of repeatable multiple-input and multiple-output nonlinear discrete-time systems. The proposed DDPILC scheme combines iterative learning control with predictive control and the distinct feature of the scheme is that the controller design depends only on the measured input/output data without using any model information of the controlled plant. In addition, if the control system is subjected to input and output constraints, the constrained DDPILC scheme is also proposed. The theoretical analysis shows that with random initial operation conditions, the proposed unconstrained DDPILC scheme guarantees monotonic and pointwise convergence while the constrained DDPILC scheme guarantees the asymptotic and pointwise convergence. The applicability and effectiveness of the proposed DDPILC schemes are further verified through simulations.
AmannNOwensDHRogersE (1998) Predictive optimal iterative learning control. International Journal of Control69(2): 203–226.
2.
ArimotoSKawamuraSMiyazakiF (1984) Bettering operation of robots by learning. Journal of Robotic Systems1(2): 123–140.
3.
BellRDÅströmKJ (1987) Dynamic models for boiler-turbine-alternator units: Data logs and parameter estimation for a 160 MW unit. Report no.TFRT-3192, December. Sweden: Lund Institute of Technology.
4.
ChiRHHouZSXuJX (2008) Adaptive ILC for a class of discrete-time systems with iteration-varying trajectory and random initial condition. Automatica44(8): 2207–2213.
5.
ChuBOwensDH (2010) Iterative learning control for constrained linear systems. International Journal of Control83(7): 1397–1413.
6.
ChyMMIBouletB (2012) Iterative learning model predictive controller of plastic sheet temperature for a thermoforming process. In: Proceedings of American Control Conference. Montral, 27–29 June 2012, pp.627–633. Piscataway, NJ: IEEE.
7.
FreemanCTLewinPLRogersE. (2010) Iterative learning control applied to a gantry robot and conveyor system. Transactions of the Institute of Mesasurement and Control32: 251–264.
8.
FreemanCTTanY (2013) Iterative learning control with mixed constraints for point-to-point tracking. IEEE Transactions on Control Systems Technology21(3): 604–616.
9.
GerschgorinS (1931) Uber die abgrenzung der eigenwerte einer matrix [On the delimitation of the eigenvalues of a matrix]. Bulletin de l’Academie des Sciences de l’URSS. Classe des sciences mathematiques et na7(6): 749–754.
10.
GoodallaRKortumW (2002) Mechatronic developments for railway vehicles of the future. Control Engineering Practice10(8): 887–898.
11.
HanZG (1984) On the identification of time-varying parameters in dynamic systems. Acta automatica sinica10(4): 330–337.
12.
HätönenJOwensDH (2004) Convex modifications to an iterative learning control law. Automatica40(7): 1213–1220.
13.
HouZS (1994) The parameter identification, adaptive control and model free learning adaptive control for nonlinear systems. PhD Thesis, Northeastern University, Shenyang, China.
14.
HouZSHuangWH (1997) The model-free learning adaptive control of a class of SISO nonlinear systems. In: Proceedings of American control conference, Albuquerque, USA, 4–6 Jun 1997, pp. 343–344. Piscataway, NJ: IEEE.
15.
HouZSJinST (2011a) Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems. IEEE Transactions on Neural Networks22(12): 2173–2188.
16.
HouZSJinST (2011b) A novel data-driven control approach for a class of discrete-time nonlinear systems. IEEE Transactions on Control Systems Technology19(6): 1549–1558.
17.
HouZSWangZ (2013) From model-based control to data-driven control: Survey, classification and perspective. Information Sciences235: 3–35.
18.
HouZSXuJX (2009) On data-driven control theory: The state of the art and perspective. Acta Automatica Sinica35(6): 650–667.
19.
HouZSZhuYM (2013) Controller-dynamic-linearization-based model free adaptive control for discrete-time nonlinear systems. IEEE Transactions on Industrial Informatics9(4): 2301–2309.
20.
JanssensPPipeleersGSweversJ (2013) A data-driven constrained norm-optimal iterative learning control framework for LTI systems. IEEE Transactions on Control Systems Technology21(2): 546–551.
21.
JinSTHouZSChiRH (2014) A novel data-driven terminal iterative learning control with iteration prediction algorithm for a class of discrete-time nonlinear systems. Journal of Applied Mathematics2014: 1–9.
22.
LeeKSLeeJH (2000) Convergence of constrained model-based predictive control for batch processes. IEEE Transactions on Automatic Control45(10): 1928–1932.
23.
LeeKSChinISLeeHJ. (1999) Model predictive control technique combined with iterative learning for batch processes. AIChE Journal45(10): 2175–2187.
24.
LiuTWangYQ (2012) A synthetic approach for robust constrained iterative learning control of piecewise affine batch processes. Automatica48(11): 2762–2775.
25.
LiuXJKongXB (2013) Nonlinear fuzzy model predictive iterative learning control for drum-type boiler-turbine system. Journal of Process Control23(8): 1023–1040.
26.
MoonUCLeeKY (2011) An adaptive dynamic matrix control with fuzzy-interpolated step-response model for a drum-type boiler-turbine system. IEEE Transactions on Energy Conversion26(2): 393–401.
27.
SaabSS (1995) A discrete-time learning control algorithm for a class of linear time-invariant systems. IEEE Transactions on Automatic Control40(6): 1138–1142.
28.
SunHQHouZS (2010) A data-driven adaptive iterative learning predictive control for a class of discrete-time nonlinear systems. In: Proceedings of the 29th Chinese control conference. Beijing, China, 29–31 July 2010, pp.5871–5876. Piscataway, NJ: IEEE.
29.
SunHQHouZSLiDY (2013) Coordinated iterative learning control schemes for train trajectory tracking with overspeed protection. IEEE Transactions on Automation Science and Engineering10(2): 323–333.
30.
SunMXWangDW (2003) Initial shift issues on discrete-time iterative learning control with system relative degree. IEEE Transactions on Automatic Control48(1): 144–148.
31.
TanKKLeeTHuangS. (2001) Adaptive-predictive control of a class of SISO nonlinear systems. Dynamics and Control11(2): 151–174.
32.
TayebiAChienCJ (2007) A unified adaptive iterative learning control framework for uncertain nonlinear systems. IEEE Transactions on Automatic Control52(10): 1907–1913.
33.
WangLPFreemanCTChaiS. (2013) Predictive-repetitive control with constraints: From design to implementation. Journal of Process Control23(7): 956–967.
34.
WangYBoydS (2010) Fast model predictive control using online optimization. IEEE Transactions on Control Systems Technology18(2): 267–278.
35.
WangYQZhouDHGaoFR (2008) Iterative learning model predictive control for multi-phase batch processes. Journal of Process Control18(6): 543–557.
36.
WenCYZhouJWangW (2009) Decentralized adaptive backstepping stabilization of interconnected systems with dynamic input and output interactions. Automatica45(1): 55–67.
37.
XuJXTanY (2003) Linear and Nonlinear Iterative Learning Control. Berlin, Germany: Springer.
38.
XuJXXuJ (2004) On iterative learning from different tracking tasks in the presence of time-varying uncertainties. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics34(1): 589–597.
39.
ZafiriouEChiouHW (1993) Output constraint softening for SISO model predictive control. In: Proceedings of American Control Conference, San Francisco, USA, 2–4 June 1993, pp.372–376. Piscataway, NJ: IEEE.
40.
ZhangBZhangWD (2006) Adaptive predictive functional control of a class of nonlinear systems. ISA Transactions45(2): 175–183.
