An Information-Based Learning Approach to Dual Control
Tansu Alpcan, Iman Shames
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | Published : 2015
Dual control aims to concurrently learn and control an unknown system. However, actively learning the system conflicts directly with any given control objective for it will disturb the system during exploration. This paper presents a receding horizon approach to dual control, where a multiobjective optimization problem is solved repeatedly and subject to constraints representing system dynamics. Balancing a standard finite-horizon control objective, a knowledge gain objective is defined to explicitly quantify the information acquired when learning the system dynamics. Measures from information theory, such as entropy-based uncertainty, Fisher information, and relative entropy, are studied an..View full abstract
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Awarded by Australian Research Council
This work was supported in part by the Australian Research Council through the Discovery Project under Grant DP140100819 and in part by a McKenzie Fellowship.