Improvement of trajectory tracking control performance by using ILC
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This paper presents an iterative learning control (ILC) approach for tracking problems with specified data points thatare desired points at certain time instants. To design ILC systems for such problems, unlike traditional ILC approaches, an algorithmwhich updates not only the control signal but also the reference trajectory at each trial will be developed. The relationshipbetween the reference trajectory and ILC control in tracking problems where there are specified data points throughwhich the system should pass is investigated as the rate of convergence. In traditional ILC, the desired data is stored in atracking profile file. Due to the huge size of the data file containing the target points, it is important to reduce the computationalcost. Finally, simulation results of the presented technique are mentioned and compared to other related works to confirmthe effectiveness of proposed scheme.
Abstract
This paper presents an iterative learning control (ILC) approach for tracking problems with specified data points thatare desired points at certain time instants. To design ILC systems for such problems, unlike traditional ILC approaches, an algorithmwhich updates not only the control signal but also the reference trajectory at each trial will be developed. The relationshipbetween the reference trajectory and ILC control in tracking problems where there are specified data points throughwhich the system should pass is investigated as the rate of convergence. In traditional ILC, the desired data is stored in atracking profile file. Due to the huge size of the data file containing the target points, it is important to reduce the computationalcost. Finally, simulation results of the presented technique are mentioned and compared to other related works to confirmthe effectiveness of proposed scheme.
참고문헌 (16)
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