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A universal iterative learning stabilizer for a class of MIMO systems.

By Ping Jiang, H. Chen and C.A. Bamforth


NoDesign of iterative learning control (ILC) often requires some prior knowledge about a system's control matrix. In some applications, such as uncalibrated visual servoing, this kind of knowledge may be unavailable so that a stable learning control cannot always be achieved. In this paper, a universal ILC is proposed for a class of multi-input multi-output (MIMO) uncertain nonlinear systems with no prior knowledge about the system control gain matrix. It consists of a gain matrix selector from the unmixing set and a learned compensator in a form of the positive definite discrete matrix kernel, corresponding to rough gain matrix probing and refined uncertainty compensating, respectively. Asymptotic convergence for a trajectory tracking within a finite time interval is achieved through repetitive tracking. Simulations and experiments of uncalibrated visual servoing are carried out in order to verify the validity of the proposed control method

Topics: Iterative learning control, Universal control, Unknown control gain;, MIMO, Uncalibrated visual servoing
Year: 2006
DOI identifier: 10.1016/j.automatica.2006.02.001
OAI identifier:
Provided by: Bradford Scholars
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