Learning control for minimizing a quadratic cost during repetitions of a taskIn many applications, control systems are asked to perform the same task repeatedly. Learning control laws have been developed over the last few years that allow the controller to improve its performance each repetition, and to converge to zero error in tracking a desired trajectory. This paper generates a new type of learning control law that learns to minimize a quadratic cost function for tracking. Besides being of interest in its own right, this objective alleviates the need to specify a desired trajectory that can actually be performed by the system. The approach used here is to adapt appropriate methods from numerical optimization theory in order to produce learning control algorithms that adjust the system command from repetition to repetition in order to converge to the quadratic cost optimal trajectory.
Document ID
19900065955
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Longman, Richard W. (Columbia Univ. New York, NY, United States)
Chang, Chi-Kuang (Columbia University New York, United States)