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EXTENDED DIRECT LEARNING CONTROL FOR MULTI-INPUT MULTI-OUTPUT NONLINEAR SYSTEMS
Hyun-Sik Ahn†, Joong-Min Park†, Do-Hyun Kim†, Ick Choy‡,
Joong-Ho Song‡ and Masayoshi Tomizuka¶
† School of Electrical Engineering, Kookmin University, Korea
‡ Intelligent System Control Research Center, KIST, Korea
¶ Department of Mechanical Engineering, University of California at Berkeley, USA
For a class of nonlinear systems which perform a given task repetitively, an extended type of a direct learning control (DLC) is proposed using the information on the (vector) relative degree of the a multi-input multi-output system. DLC method can generate the desired control input directly from prestored control input profiles with different time scales without any repetitive learning process. It is shown that existing DLC methods can be applied only to a certain limited class of nonlinear systems and the information on the relative degree of a nonlinear system is essential to find the desired control input if the system has higher relative degree.
Keywords: Learning control, nonlinear systems, relative degree, iterative methods, robot manipulators, tracking systems
Session slot T-Th-E16: Design of Adaptive and Learning Controllers/Area code 2a : Control Design
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