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On the iterative learning control of fractional impulsive evolution equations in Banach spaces. (English) Zbl 1376.93048

Summary: In this paper, we study \(P\)-type, \(PI^\alpha\)-type, and \(D\)-type iterative learning control for fractional impulsive evolution equations in Banach spaces. We present triple convergence results for open-loop iterative learning schemes in the sense of \(\lambda\)-norm through rigorous analysis. The proposed iterative learning control schemes are effective to fractional hybrid infinite-dimensional distributed parameter systems. Finally, an example is given to illustrate our theoretical results.

MSC:

93C25 Control/observation systems in abstract spaces
68T05 Learning and adaptive systems in artificial intelligence
34A08 Fractional ordinary differential equations
34G20 Nonlinear differential equations in abstract spaces
92C40 Biochemistry, molecular biology
Full Text: DOI

References:

[1] UchiyamaM. Formulation of high‐speed motion pattern of a mechanical arm by trial. Transactions of the Society for Instrumentation and Control Engineering. 1978; 14: 706-712.
[2] ArimotoS, KawamuraS. Bettering operation of robots by learning. Journal of Robotic systems. 1984; 1: 123-140.
[3] ArimotoS. Mathematical theory of learning with applications to robot control. In Adaptive and Learning Systems: Theory and Applications, NarendraKS (ed.) (ed.). Springer: US, 1985; 379-388.
[4] RuanX, BienZZ, ParkKH. Decentralized iterative learning control to large‐scale industrial processes for nonrepetitive trajectory tracking. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans. 2008; 38: 238-252.
[5] HakvoortWBJ, AartsRGKM, DijkJV, JonkerJB. Lifted system iterative learning control applied to an industrial robot. Control Engineering Practice. 2008; 16: 377-391.
[6] VisioliA, ZilianiG, LegnaniG. Iterative‐learning hybrid force/velocity control for contour tracking. IEEE Transactions on Robotics. 2010; 26: 388-393.
[7] RuanX, BienZZ. Iterative learning controllers with time‐varying gains for large‐scale industrial processes to track trajectories with different magnitudes. International Journal of Systems Science. 2008; 39: 513-527. · Zbl 1167.93308
[8] LeeFS, ChienCJ, WangJC, LiuJJ. Application of a model‐based iterative learning technique to tracking control of a piezoelectric system. Asian Journal of Control. 2008; 7: 29-37.
[9] ChenHY, XingGS, SunHX, WangH. Indirect iterative learning control for robot manipulator with non‐Gaussian disturbances. IET Control Theory and Applications. 2013; 7: 2090-2102.
[10] ZhaoY, LinY, GuoS. Calibration‐based iterative learning control for path tracking of industrial robots. IEEE Transcations on Industrial Electronics. 2015; 62: 2921-2929.
[11] XuC, ArastooR, SchusterE. On iterative learning control of parabolic distributed parameter systems. In Proceedings of the 17th Mediterranean Conference on Control Automation: Makedonia Palace, Thessaloniki, Greece, 2009; 510-515.
[12] HuangD, XuJX. Steady‐state iterative learning control for a class of nonlinear PDE processes. Journal of Process Control. 2011; 21: 1155-1163.
[13] HuangD, LiX, XuJX, XuC, HeW. Iterative learning control of inhomogeneous distributed parameter systems‐frequency domain design and analysis. Systems & Control Letters. 2014; 72: 22-29. · Zbl 1297.93097
[14] GuoQ, HuangD, LuoC, ZhangW. Iterative learning control for a class of non‐affine‐in‐input processes in Hilbert space. International Journal of Adaptive Control and Signal Processing. 2014; 28: 40-51. · Zbl 1330.93085
[15] LiuS, WangJ, WeiW. A study on iterative learning control for impulsive differential equations. Communications in Nonlinear Science and Numerical Simulation. 2015; 24: 4-10. · Zbl 1440.34017
[16] LiX, CaraballoT, RakkiyappanR, HanX. On the stability of impulsive functional differential equations with infinite delays. Mathematical Methods in the Applied Sciences. 2014; 38. 10.1002/mma.3303. · Zbl 1334.34163
[17] D’AguìG, BellaBD, TersianS. Multiplicity results for superlinear boundary value problems with impulsive effects. Mathematical Methods in the Applied Sciences. 2015. 10.1002/mma.3545. · Zbl 1342.34046
[18] BaleanuD, MachadoJAT, LuoACJ. Fractional Dynamics and Control. Springer: New York, 2012.
[19] DiethelmK. The Analysis of Fractional Differential Equations. Lecture Notes in Mathematics: Berlin, 2010. · Zbl 1215.34001
[20] KilbasAA, SrivastavaHM, TrujilloJJ. Theory and Applications of Fractional Differential Equations. Elsevier Science B.V.: Amsterdam, 2006. · Zbl 1092.45003
[21] LakshmikanthamV, LeelaS, DeviJV. Theory of Fractional Dynamic Systems. Cambridge Scientific Publishers: Cambridge, 2009. · Zbl 1188.37002
[22] MillerKS, RossB. An Introduction to the Fractional Calculus and Differential Equations. John Wiley: New York, 1993. · Zbl 0789.26002
[23] PodlubnyI. Fractional Differential Equations. Academic Press: Salt Lake City, 1999. · Zbl 0924.34008
[24] TarasovVE. Fractional Dynamics: Application of Fractional Calculus to Dynamics of Particles, Fields and Media. Springer, HEP: Berlin, 2011.
[25] ZhouY. Basic Theory of Fractional Differential Equations. World Scientific: Singapore, 2014. · Zbl 1336.34001
[26] OdabasiM, MisirliE. On the solutions of the nonlinear fractional differential equations via the modified trial equation method. Mathematical Methods in the Applied Sciences. 2014. DOI: 10.1002/mma.3533. · Zbl 1384.35141
[27] DebboucheA, DumitruB. Controllability of fractional evolution nonlocal impulsive quasilinear delay integro‐differential systems. Computers & Mathematics with Applications. 2011; 62: 1442-1450. · Zbl 1228.45013
[28] KerbouaM, DebboucheA, BaleanuD. Approximate controllability of Sobolev type fractional stochastic nonlocal nonlinear differential equations in Hilbert spaces. Electronic Journal of Qualitative Theory of Differential Equations. 2014; 58: 1-16. · Zbl 1324.93020
[29] KerbouaM, DebboucheA, BaleanuD. Approximate controllability of Sobolev type nonlocal fractional stochastic dynamic systems in Hilbert spaces. Abstract and Applied Analysis. 2013; 2013. Art.ID 262191:1‐10. · Zbl 1291.93039
[30] LiY, ChenYQ, AhnHS. Fractional‐order iterative learning control for fractional‐order linear systems. Asian Journal of Control. 2011; 13: 1-10.
[31] LiY, ChenYQ, AhnHS. Convergence analysis of fractional‐order iterative learning control. Control Theory Applition. 2012; 29: 1031-1037. · Zbl 1274.93196
[32] LiY, ChenYQ, AhnHS, TianG. A survey on fractional‐order iterative learning control. Journal of Optimization Theory and Applications. 2013; 156: 127-140. · Zbl 1263.93100
[33] LanYH, ZhouY. Iterative learning control with initial state learning for fractional order nonlinear systems. Computers & Mathematics with Applications. 2012; 64: 3210-3216. · Zbl 1268.93054
[34] LanYH, ZhouY. D‐type iterative learning control for fractional order linear time‐delay systems. Asian Journal of Control. 2013; 15: 669-677. · Zbl 1327.93218
[35] LiY, JiangW. Fractional order nonlinear systems with delay in iterative learning control. Applied Mathematics and Computation. 2015; 257: 546-552. · Zbl 1338.93179
[36] YuX, WangJ. Uniform design and analysis of iterative learning control for a class of impulsive first‐order distributed parameter systems. Advances in Difference Equations. 2015; 261: 1-10. · Zbl 1422.93087
[37] WangJ, FečkanM, ZhouY. On the new concept of solutions and existence results for impulsive fractional evolution equations. Dynamics of Partial Differential Equations. 2011; 8: 345-361. · Zbl 1264.34014
[38] ZhouY, JiaoF. Existence of mild solutions for fractional neutral evolution equations. Computers & Mathematics with Applications. 2010; 59: 1063-1077. · Zbl 1189.34154
[39] WangJ, ZhouY, FečkanM. Nonlinear impulsive problems for fractional differential equations and Ulam stability. Computers & Mathematics with Applications. 2012; 64: 3389-3405. · Zbl 1268.34033
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