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• W2076532922 startingPage "28" @default.
• W2076532922 abstract "When attempting to estimate parameters in a dynamical system, it is often beneficial to strategically design experimental trajectories that facilitate the estimation process. This paper presents an optimization algorithm which improves conditioning of estimation problems by modifying the experimental trajectory. An objective function which minimizes the condition number of the Hessian of the least-squares identification method is derived and a least-squares method is used to estimate parameters of the nonlinear system. A software-simulated example demonstrates that an arbitrarily designed trajectory can lead to an ill-conditioned least-squares estimation problem, which in turn leads to slower convergence to the best estimate and, in the presence of experimental uncertainties, may lead to no convergence at all. A physical experiment with a robot-controlled suspended mass also shows improved estimation results in practice in the presence of noise and uncertainty using the optimized trajectory." @default.
• W2076532922 created "2016-06-24" @default.
• W2076532922 creator A5045172246 @default.
• W2076532922 creator A5067725461 @default.
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• W2076532922 date "2015-01-01" @default.
• W2076532922 modified "2023-09-24" @default.
• W2076532922 title "Trajectory Optimization for Well-Conditioned Parameter Estimation" @default.
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• W2076532922 doi "https://doi.org/10.1109/tase.2014.2323934" @default.
• W2076532922 hasPublicationYear "2015" @default.
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