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In this paper, an optimization-based iterative learning control approach is presented. Given a desired trajectory to be followed, the proposed learning algorithm improves the system performance from trial to trial by exploiting the experience gained from previous repetitions. Taking advantage of the a-priori knowledge about the systems dominating dynamics, a data-based update rule is derived which...
The research topic of autonomous underwater vehicles (AUVs) has attracted much attention over years since they provide marine researchers easy ways to access the ocean for surveying and site investigation, etc. To accomplish these applications, an AUV has to know its position accurately. Therefore, AUV localization is very important problem. In this paper, we propose an interactive multiple model...
In this paper, an optimization-based adaptive Kalman filtering method is proposed. The method produces an estimate of the process noise covariance matrix Q by solving an optimization problem over a short window of data. The algorithm recovers the observations h(x) from a system x = f (x); y = h(x)+v without a priori knowledge of system dynamics. Potential applications include target tracking using...
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