No / Design 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.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/3416 |
| Date | January 2006 |
| Creators | Jiang, Ping, Chen, H., Bamforth, C.A. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, No full-text available in the repository |
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