Generalized Norm Optimal Iterative Learning Control with Intermediate Point and Sub-interval Tracking

David H. Owens; Chris T. Freeman; Bing Chu

doi:10.1007/s11633-015-0888-8

June 2015

Article Contents

David H. Owens, Chris T. Freeman and Bing Chu. Generalized Norm Optimal Iterative Learning Control with Intermediate Point and Sub-interval Tracking. International Journal of Automation and Computing, vol. 12, no. 3, pp. 243-253, 2015. doi: 10.1007/s11633-015-0888-8

Cite as: David H. Owens, Chris T. Freeman and Bing Chu. Generalized Norm Optimal Iterative Learning Control with Intermediate Point and Sub-interval Tracking. International Journal of Automation and Computing, vol. 12, no. 3, pp. 243-253, 2015. doi: 10.1007/s11633-015-0888-8

Generalized Norm Optimal Iterative Learning Control with Intermediate Point and Sub-interval Tracking

Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield S3JD, UK;
School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK;
Advanced Robotics Division, Italian Institute of Technology, Genoa, Italy

Received: 2014-03-26

Abstract

Norm optimal iterative learning control (NOILC) has recently been applied to iterative learning control (ILC) problems in which tracking is only required at a subset of isolated time points along the trial duration. This problem addresses the practical needs of many applications, including industrial automation, crane control, satellite positioning and motion control within a medical stroke rehabilitation context. This paper provides a substantial generalization of this framework by providing a solution to the problem of convergence at intermediate points with simultaneous tracking of subsets of outputs to reference trajectories on subintervals. This formulation enables the NOILC paradigm to tackle tasks which mix "point to point" movements with linear tracking requirements and hence substantially broadens the application domain to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments. A solution to the problem is presented in the framework of NOILC and inherits NOILC'swell-defined convergence properties. Design guidelines and supporting experimental results are included.
- Iterative learning control,
- learning control,
- optimization,
- linear systems,
- robotics

References

[1]	S. Arimoto, F. Miyazaki, S. Kawamura. Bettering operation of robots by learning. Journal of Robotic Systems, vol.1, no. 2, pp. 123-140, 1984.
[2]	D. A. Bristow, M. Tharayil, A. G. Alleyne. A survey of iterative learning control: A learning-based method for highperformance tracking control. IEEE Control Systems Magazine, vol. 26, no. 3, pp. 96-114, 2006.
[3]	H. S. Ahn, Y. Chen, K. L. Moore. Iterative learning control: Brief survey and categorization. IEEE Transactions on Systems, Man, and Cybernetics, Part C, vol. 37, no. 6, 1099-1121, 2007.
[4]	D. H. Owens, C. T. Freeman, B. Chu. Multivariable norm optimal iterative learning control with auxiliary optimization. International Journal of Control, vol. 86, no. 6, pp. 1026-1045, 2013.
[5]	D. H. Owens, C. T. Freeman, T. Van Dinh. Norm optimal iterative learning control with intermediate point weighting: Theory, algorithms, and experimental evaluation. IEEE Transactions on Control Systems Technology, vol. 21, no. 3, pp. 999-1007, 2013.
[6]	G. Pipeleers, J. Swevers. A data-driven constrained normoptimal iterative learning control framework for LTI systems. IEEE Transactions on Control Systems Technology, vol. 21, no. 2, pp. 546-551, 2013.
[7]	T. D. Son, H. S. Ahn, K. L. Moore. Iterative learning control in optimal tracking problems with specified data points. Automatica, vol. 49, no. 5, pp. 1465-1472, 2013.
[8]	S. H. Zhou, Y. Tan, D. Oetomo, C. T. Freeman, E. Burdet, I. Mareels. Point-to-point learning in human motor systems. In Proceedings of the American Control Conference, IEEE, Washington, USA, pp. 5923-5928, 2013.
[9]	C. T. Freeman, T. Exell, K. L. Meadmore, E. Hallewell, A. M. Hughes. Computational models of upper-limb motion during functional reaching tasks for application in FESbased stroke rehabilitation. Biomedical Engineering, to be published.
[10]	K. L. Moore, M. Ghosh, Y. Q. Chen. Spatial-based iterative learning control for motion control applications. Meccanica, vol. 42, no. 2, pp. 167-175, 2007.
[11]	S. K. Sahoo, S. K. Panda, J. X. Xu. Application of spatial iterative learning control for direct torque control of switched reluctance motor drive. In Proceedings of IEEE Power Engineering Society General Meeting, IEEE, Tampa, USA, pp. 1-7, 2007.
[12]	Y. H. Yang, C. L. Chen. Spatial-based adaptive iterative learning control of nonlinear rotary systems with spatially periodic parametric variation. International Journal of Innovative Computing, Information and Control, vol. 7, no. 6, pp. 3407-3417, 2011.
[13]	K. Furuta, M. Yamakita. The design of a learning control system for multivariable systems. In Proceedings of IEEE International Symposium on Intelligent Control, IEEE, Philadelphia, USA, pp. 371-376, 1987.
[14]	K. Kinosita, T. Sogo, N. Adachi. Iterative learning control using adjoint systems and stable inversion. Asian Journal of Control, vol. 4, no. 1, pp. 60-67, 2002.
[15]	D. H. Owens, J. J. Hatonen, S. Daley. Robust monotone gradient-based discrete-time iterative learning control. International Journal of Robust and Nonlinear Control, vol. 19, no. 6, pp. 634-661, 2009.
[16]	N. Amann, D. H. Owens, E. Rogers. Iterative learning control using optimal feedback and feed-forward actions. International Journal of Control, vol. 65, no. 2, pp. 277-293, 1996.
[17]	S. Gunnarsson, M. Norrlöf. On the design of ILC algorithms using optimization. Automatica, vol. 37, no. 12, pp. 2011-2016, 2001.
[18]	J. H. Lee, K. S. Lee, W. C. Kim. Model-based iterative learning control with a quadratic criterion for timevarying linear systems. Automatica, vol. 36, no. 5, pp. 641-657, 2000.
[19]	K. L. Barton, A. G. Alleyne. A norm optimal approach to time-varying ILC with application to a multi-axis robotic testbed. IEEE Transactions on Control Systems Technology, vol. 19, no. 1, pp. 166-180, 2011.
[20]	E. Rogers, D. H. Owens, H. Werner, C. T. Freeman, P. L. Lewin, S. Kichhoff, C. Schmidt, G. Lichtenberg. Norm optimal iterative learning control with application to problems in accelerator based free electron lasers and rehabilitation robotics. European Journal of Control, vol. 16, no. 5, pp. 497-524, 2010.
[21]	N. Amann, D. H. Owens, E. Rogers. Iterative learning control for discrete-time systems with exponential rate of convergence. IEE Proceedings of Control Theory and Applications, vol. 143, no. 2, 217-224, 1996.
[22]	N. Amann, D. H. Owens, E. Rogers. Predictive optimal iterative learning control. International Journal of Control, vol. 69, no. 2, pp. 203-226, 1998.
[23]	B. Chu, D. H. Owens. Accelerated norm-optimal iterative learning control algorithms using successive projection. International Journal of Control, vol. 82, no. 8, pp. 1469-1484, 2009.
[24]	B. Chu, D. H. Owens. Iterative learning control for constrained linear systems. International Journal of Control, vol. 83, no. 7, pp. 1397-1413, 2010.
[25]	D. H. Owens, B. Chu, E. Rogers, C. T. Freeman, and P. L. Lewin. Influence of nonminimum phase zeros on the performance of optimal continuous-time iterative learning control. IEEE Transactions on Control Systems Technology, vol. 22, no. 3, pp. 1151-1158, 2014.
[26]	D. H. Owens, B. Chu. Modelling of non-minimum phase effects in discrete-time norm optimal iterative learning control. International Journal of Control, vol. 83, no. 10, pp. 2012-2027, 2010.

Metrics

[1]	Marwa Taleb, Edouard Leclercq, Dimitri Lefebvre. Model Predictive Control for Discrete and Continuous Timed Petri Nets . International Journal of Automation and Computing, 2018, 15(1): 25-38. doi: 10.1007/s11633-016-1046-7
[2]	A. Mallikarjuna Rao, K. Ramji, B.S. K. Sundara Siva Rao, V. Vasu, C. Puneeth. Navigation of Non-holonomic Mobile Robot Using Neuro-fuzzy Logic with Integrated Safe Boundary Algorithm . International Journal of Automation and Computing, 2017, 14(3): 285-294. doi: 10.1007/s11633-016-1042-y
[3]	Deqing Huang, Jian-Xin Xu, Xin Deng, Venkatakrishnan Venkataramanan, The Cat Tuong Huynh. Differential Evolution Based High-order Peak Filter Design with Application to Compensation of Contact-induced Vibration in HDD Servo Systems . International Journal of Automation and Computing, 2017, 14(1): 45-56. doi: 10.1007/s11633-016-1034-y
[4]	Edi Kurniawan, Zhen-Wei Cao, Maria Mitrevska, Zhi-Hong Man. Design of Decentralized Multi-input Multi-output Repetitive Control Systems . International Journal of Automation and Computing, 2016, 13(6): 615-623. doi: 10.1007/s11633-016-1013-3
[5]	Xue-Fang Li, Jian-Xin Xu. Lifted System Framework for Learning Control with Different Trial Lengths . International Journal of Automation and Computing, 2015, 12(3): 273-280. doi: 10.1007/s11633-015-0882-1
[6]	Chiang-Ju Chien, Yu-Chung Hung, Rong-Hu Chi. On the Current Error Based Sampled-data Iterative Learning Control with Reduced Memory Capacity . International Journal of Automation and Computing, 2015, 12(3): 307-315. doi: 10.1007/s11633-015-0890-1
[7]	Xiao-E Ruan, Zhao-Zhen Li, Z. Z. Bien. Discrete-frequency Convergence of Iterative Learning Control for Linear Time-invariant systems with Higher-order Relative Degree . International Journal of Automation and Computing, 2015, 12(3): 281-288. doi: 10.1007/s11633-015-0884-z
[8]	Jian Ding, Blazej Cichy, Krzysztof Galkowski, Eric Rogers, Hui-Zhong Yang. Robust Fault-tolerant Iterative Learning Control for Discrete Systems via Linear Repetitive Processes Theory . International Journal of Automation and Computing, 2015, 12(3): 254-265. doi: 10.1007/s11633-015-0883-0
[9]	Wei Zhou, Miao Yu, De-Qing Huang. A High-order Internal Model Based Iterative Learning Control Scheme for Discrete Linear Time-varying Systems . International Journal of Automation and Computing, 2015, 12(3): 330-336. doi: 10.1007/s11633-015-0886-x
[10]	Xi-Sheng Dai, Sen-Ping Tian, Ya-Jun Guo. Iterative Learning Control for Discrete Parabolic Distributed Parameter Systems . International Journal of Automation and Computing, 2015, 12(3): 316-322. doi: 10.1007/s11633-015-0892-z
[11]	Bao-Chang Xu, Ying-Ying Zhang. An Improved Gravitational Search Algorithm for Dynamic Neural Network Identification . International Journal of Automation and Computing, 2014, 11(4): 434-440. doi: 10.1007/s11633-014-0810-9
[12]	. Developing Bi-CG and Bi-CR Methods to Solve Generalized Sylvester-transpose Matrix Equations . International Journal of Automation and Computing, 2014, 11(1): 25-29. doi: 10.1007/s11633-014-0762-0
[13]	Chun-Li Zhang, Jun-Min Li. Adaptive Iterative Learning Control of Non-uniform Trajectory Tracking for Strict Feedback Nonlinear Time-varying Systems . International Journal of Automation and Computing, 2014, 11(6): 621-626. doi: 10.1007/s11633-014-0819-0
[14]	M. H. A. Majid, M. R. Khan, S. N. Sidek. Development of Wheel-less Snake Robot with Two Distinct Gaits and Gait Transition Capability . International Journal of Automation and Computing, 2013, 10(6): 534-544. doi: 10.1007/s11633-013-0751-8
[15]	Fei Chao, Xin Zhang, Hai-Xiong Lin, Chang-Le Zhou, Min Jiang. Learning Robotic Hand-eye Coordination Through a Developmental Constraint Driven Approach . International Journal of Automation and Computing, 2013, 10(5): 414-424. doi: 10.1007/s11633-013-0738-5
[16]	Xin-Min Xu, Yao Chen, Wen-Yao Xu, Fang Gong. An Efficient Algorithm for Mobile Localization in Sensor Networks . International Journal of Automation and Computing, 2012, 9(6): 594-599 . doi: 10.1007/s11633-012-0684-7
[17]	Song-Gui Yuan, Min Wu, Bao-Gang Xu, Rui-Juan Liu. Design of Discrete-time Repetitive Control System Based on Two-dimensional Model . International Journal of Automation and Computing, 2012, 9(2): 165-170. doi: 10.1007/s11633-012-0629-1
[18]	. Novel Adaptive Learning Control of Linear Systems with Completely Unknown Time Delays . International Journal of Automation and Computing, 2009, 6(2): 177-185. doi: 10.1007/s11633-009-0177-5
[19]	George V. Lauder, Peter G. A. Madden. Learning from Fish: Kinematics and Experimental Hydrodynamics for Roboticists . International Journal of Automation and Computing, 2006, 3(4): 325-335. doi: 10.1007/s11633-006-0325-0
[20]	David H. Owens, Maria Tomas-Rodriguez, Jari J. Hatönen. Limiting Behaviour in Parameter Optimal Iterative Learning Control . International Journal of Automation and Computing, 2006, 3(3): 222-228. doi: 10.1007/s11633-006-0222-6

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Cite This Article

PDF

XML

Entire Issue PDF

用微信扫码二维码

分享至好友和朋友圈

Metrics

Abstract Views (5584) PDF downloads (1872) Citations (0)

Related Articles

Generalized Norm Optimal Iterative Learning Control with Intermediate Point and Sub-interval Tracking

Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield S3JD, UK;

School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK;

Advanced Robotics Division, Italian Institute of Technology, Genoa, Italy

Received: 2014-03-26

Key words:

Abstract: Norm optimal iterative learning control (NOILC) has recently been applied to iterative learning control (ILC) problems in which tracking is only required at a subset of isolated time points along the trial duration. This problem addresses the practical needs of many applications, including industrial automation, crane control, satellite positioning and motion control within a medical stroke rehabilitation context. This paper provides a substantial generalization of this framework by providing a solution to the problem of convergence at intermediate points with simultaneous tracking of subsets of outputs to reference trajectories on subintervals. This formulation enables the NOILC paradigm to tackle tasks which mix "point to point" movements with linear tracking requirements and hence substantially broadens the application domain to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments. A solution to the problem is presented in the framework of NOILC and inherits NOILC'swell-defined convergence properties. Design guidelines and supporting experimental results are included.

HTML

Reference (26)

[1]	S. Arimoto, F. Miyazaki, S. Kawamura. Bettering operation of robots by learning. Journal of Robotic Systems, vol.1, no. 2, pp. 123-140, 1984.
[2]	D. A. Bristow, M. Tharayil, A. G. Alleyne. A survey of iterative learning control: A learning-based method for highperformance tracking control. IEEE Control Systems Magazine, vol. 26, no. 3, pp. 96-114, 2006.
[3]	H. S. Ahn, Y. Chen, K. L. Moore. Iterative learning control: Brief survey and categorization. IEEE Transactions on Systems, Man, and Cybernetics, Part C, vol. 37, no. 6, 1099-1121, 2007.
[4]	D. H. Owens, C. T. Freeman, B. Chu. Multivariable norm optimal iterative learning control with auxiliary optimization. International Journal of Control, vol. 86, no. 6, pp. 1026-1045, 2013.
[5]	D. H. Owens, C. T. Freeman, T. Van Dinh. Norm optimal iterative learning control with intermediate point weighting: Theory, algorithms, and experimental evaluation. IEEE Transactions on Control Systems Technology, vol. 21, no. 3, pp. 999-1007, 2013.
[6]	G. Pipeleers, J. Swevers. A data-driven constrained normoptimal iterative learning control framework for LTI systems. IEEE Transactions on Control Systems Technology, vol. 21, no. 2, pp. 546-551, 2013.
[7]	T. D. Son, H. S. Ahn, K. L. Moore. Iterative learning control in optimal tracking problems with specified data points. Automatica, vol. 49, no. 5, pp. 1465-1472, 2013.
[8]	S. H. Zhou, Y. Tan, D. Oetomo, C. T. Freeman, E. Burdet, I. Mareels. Point-to-point learning in human motor systems. In Proceedings of the American Control Conference, IEEE, Washington, USA, pp. 5923-5928, 2013.
[9]	C. T. Freeman, T. Exell, K. L. Meadmore, E. Hallewell, A. M. Hughes. Computational models of upper-limb motion during functional reaching tasks for application in FESbased stroke rehabilitation. Biomedical Engineering, to be published.
[10]	K. L. Moore, M. Ghosh, Y. Q. Chen. Spatial-based iterative learning control for motion control applications. Meccanica, vol. 42, no. 2, pp. 167-175, 2007.
[11]	S. K. Sahoo, S. K. Panda, J. X. Xu. Application of spatial iterative learning control for direct torque control of switched reluctance motor drive. In Proceedings of IEEE Power Engineering Society General Meeting, IEEE, Tampa, USA, pp. 1-7, 2007.
[12]	Y. H. Yang, C. L. Chen. Spatial-based adaptive iterative learning control of nonlinear rotary systems with spatially periodic parametric variation. International Journal of Innovative Computing, Information and Control, vol. 7, no. 6, pp. 3407-3417, 2011.
[13]	K. Furuta, M. Yamakita. The design of a learning control system for multivariable systems. In Proceedings of IEEE International Symposium on Intelligent Control, IEEE, Philadelphia, USA, pp. 371-376, 1987.
[14]	K. Kinosita, T. Sogo, N. Adachi. Iterative learning control using adjoint systems and stable inversion. Asian Journal of Control, vol. 4, no. 1, pp. 60-67, 2002.
[15]	D. H. Owens, J. J. Hatonen, S. Daley. Robust monotone gradient-based discrete-time iterative learning control. International Journal of Robust and Nonlinear Control, vol. 19, no. 6, pp. 634-661, 2009.
[16]	N. Amann, D. H. Owens, E. Rogers. Iterative learning control using optimal feedback and feed-forward actions. International Journal of Control, vol. 65, no. 2, pp. 277-293, 1996.
[17]	S. Gunnarsson, M. Norrlöf. On the design of ILC algorithms using optimization. Automatica, vol. 37, no. 12, pp. 2011-2016, 2001.
[18]	J. H. Lee, K. S. Lee, W. C. Kim. Model-based iterative learning control with a quadratic criterion for timevarying linear systems. Automatica, vol. 36, no. 5, pp. 641-657, 2000.
[19]	K. L. Barton, A. G. Alleyne. A norm optimal approach to time-varying ILC with application to a multi-axis robotic testbed. IEEE Transactions on Control Systems Technology, vol. 19, no. 1, pp. 166-180, 2011.
[20]	E. Rogers, D. H. Owens, H. Werner, C. T. Freeman, P. L. Lewin, S. Kichhoff, C. Schmidt, G. Lichtenberg. Norm optimal iterative learning control with application to problems in accelerator based free electron lasers and rehabilitation robotics. European Journal of Control, vol. 16, no. 5, pp. 497-524, 2010.
[21]	N. Amann, D. H. Owens, E. Rogers. Iterative learning control for discrete-time systems with exponential rate of convergence. IEE Proceedings of Control Theory and Applications, vol. 143, no. 2, 217-224, 1996.
[22]	N. Amann, D. H. Owens, E. Rogers. Predictive optimal iterative learning control. International Journal of Control, vol. 69, no. 2, pp. 203-226, 1998.
[23]	B. Chu, D. H. Owens. Accelerated norm-optimal iterative learning control algorithms using successive projection. International Journal of Control, vol. 82, no. 8, pp. 1469-1484, 2009.
[24]	B. Chu, D. H. Owens. Iterative learning control for constrained linear systems. International Journal of Control, vol. 83, no. 7, pp. 1397-1413, 2010.
[25]	D. H. Owens, B. Chu, E. Rogers, C. T. Freeman, and P. L. Lewin. Influence of nonminimum phase zeros on the performance of optimal continuous-time iterative learning control. IEEE Transactions on Control Systems Technology, vol. 22, no. 3, pp. 1151-1158, 2014.
[26]	D. H. Owens, B. Chu. Modelling of non-minimum phase effects in discrete-time norm optimal iterative learning control. International Journal of Control, vol. 83, no. 10, pp. 2012-2027, 2010.

Generalized Norm Optimal Iterative Learning Control with Intermediate Point and Sub-interval Tracking

Abstract

References

Metrics

Related Articles

通讯作者: 陈斌, bchen63@163.com

Metrics

Generalized Norm Optimal Iterative Learning Control with Intermediate Point and Sub-interval Tracking

Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield S3JD, UK; School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK; Advanced Robotics Division, Italian Institute of Technology, Genoa, Italy

HTML

Catalog

Export File

Citation

Format

Content