A universal iterative learning stabilizer for a class of MIMO systems

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. 2006 Elsevier Ltd. All rights reserved....

• Type: Journal
• Year: 2006
• Where: AUTOMATICA
• Authors: Ping Jiang, Huadong Chen, Leon C. A. Bamforth
• Keywords: AUTOMATICA 2006, Learning Control, Matrix, Prior Knowledge