A note on optimal experiment design for nonlinear systems using dynamic programming
John Maidens, Murat Arcak
TL;DR
This paper introduces an efficient dynamic programming approach for optimal experiment design in nonlinear systems, avoiding additional dispersion equations to improve computational efficiency.
Contribution
It proposes a novel dynamic programming formulation that simplifies the T-optimal design problem for nonlinear dynamical systems by excluding dispersion equations.
Findings
More efficient solutions compared to previous methods
Successful application to nonlinear system design problems
Potential for broader use in experimental optimization
Abstract
We present a method of solving the T-optimal design problem for nonlinear dynamical systems using dynamic programming. In contrast with previous dynamic programming formulations, we avoid adding an equation for the dispersion to the system state, allowing for more efficient solutions.
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Taxonomy
TopicsOptimal Experimental Design Methods · Advanced Multi-Objective Optimization Algorithms · Advanced Control Systems Optimization