Optimality of first-order ILC among higher order ILC
Higher order iterative learning control (HO-ILC) algorithms use past system control information from more than one past iterative cycle. This class of ILC algorithms have been proposed aiming at improving the learning efficiency and performance. This paper addresses the optimality of HO-ILC in the s...
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| Veröffentlicht in: | IEEE transactions on automatic control 2006-08, Vol.51 (8), p.1332-1336 |
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| container_title | IEEE transactions on automatic control |
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| creator | Saab, S.S. |
| description | Higher order iterative learning control (HO-ILC) algorithms use past system control information from more than one past iterative cycle. This class of ILC algorithms have been proposed aiming at improving the learning efficiency and performance. This paper addresses the optimality of HO-ILC in the sense of minimizing the trace of the control error covariance matrix in the presence of a class of uncorrelated random disturbances. It is shown that the optimal weighting matrices corresponding to the control information associated with more than one cycle preceding the current cycle are zero. That is, an optimal HO-ILC does not add to the optimality of standard first-order ILC in the sense of minimizing the trace of the control error covariance matrix. The system under consideration is a linear discrete-time varying systems with different relative degree between the input and each output |
| doi_str_mv | 10.1109/TAC.2006.878734 |
| format | Article |
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This class of ILC algorithms have been proposed aiming at improving the learning efficiency and performance. This paper addresses the optimality of HO-ILC in the sense of minimizing the trace of the control error covariance matrix in the presence of a class of uncorrelated random disturbances. It is shown that the optimal weighting matrices corresponding to the control information associated with more than one cycle preceding the current cycle are zero. That is, an optimal HO-ILC does not add to the optimality of standard first-order ILC in the sense of minimizing the trace of the control error covariance matrix. 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This class of ILC algorithms have been proposed aiming at improving the learning efficiency and performance. This paper addresses the optimality of HO-ILC in the sense of minimizing the trace of the control error covariance matrix in the presence of a class of uncorrelated random disturbances. It is shown that the optimal weighting matrices corresponding to the control information associated with more than one cycle preceding the current cycle are zero. That is, an optimal HO-ILC does not add to the optimality of standard first-order ILC in the sense of minimizing the trace of the control error covariance matrix. The system under consideration is a linear discrete-time varying systems with different relative degree between the input and each output</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TAC.2006.878734</doi><tpages>5</tpages></addata></record> |
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| issn | 0018-9286 1558-2523 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Applied sciences Automatic control Computer science control theory systems Control system analysis Control systems Control theory. Systems Covariance matrix Delay effects Delay systems Differential equations Discrete-time systems Disturbances Error correction Error detection Exact sciences and technology iterative learning control (ILC) Learning Mathematical analysis Miscellaneous Modelling and identification monotonic convergence Nonlinear control systems Optimal control Optimization relative degree Relays tracking control |
| title | Optimality of first-order ILC among higher order ILC |
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