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Optimizing Constrained Control Using Linear and Mixed Integer Programming

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Abstract:
In the first part, a novel design technique for a non-linear feedback control law, which drives the state of a linear discrete-time system to the origin in minimum-time subject to magnitude constraints on the state and actuator variables as well as subject to constraints on the actuator rate, is presented. The off-line, optimization-based design technique requires the solution of a series of mixed integer linear programs. Extension of the proposed algorithm are presented for mixed logical systems, specifically for piece-wise linear systems. In the second part, a novel method of generating time optimal reference signals, which can be used to drive a rigid link manipulator in minimum time along a given prescribed path without violating constraints on the joint velocities and/or torques, is presented. The algorithm is based on linear programming. The theoretical results are demonstrated by simulations on a MATLAB model of a puma 560 robot.

http://www.iert.tuwien.ac.at
Type of Seminar:
Public Seminar
Speaker:
Dr. Heinz-Peter Rothwangl
Institute of Automatic Control Vienna University of Technology Gusshausstr. 27-29/375 A-1040 Vienna /Austria
Date/Time:
Oct 10, 2001   17:15
Location:

ETH Zentrum, ETZ E6, Gloriastr. 35, 8006 Zurich
Contact Person:

Prof. M. Morari
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Biographical Sketch:
Heinz P. Rothwangl received the diploma engineer degree in automatic control from the Technical University of Graz, Austria, in 1996. During his study, he also made research at the Institute of System Laboratories, Stanford University, where he wrote main parts of his diploma thesis. In 1996, he joined Bernecker&Rainer, a company near Salzburg, where he developed software for industrial control applications. In December 1997, he joined the Institute of Automatic Control, Vienna University of Technology, where he received his Ph.D. degree in April 2001. His current research interests include besides others optimal robot control, mixed logical systems and neuronal networks.