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This paper adapts the so-called Shubert algorithm for Extremum Seeking Control (ESC) to seek the global extremum (in presence of local extrema) of general dynamic plants. Different from derivative based methods that are widely used in ESC, the Shubert algorithm is a good representative of sampling optimization methods. With knowledge of the Lipschitz constant of an unknown static mapping, this deterministic...
Compressed memory system is a high performance memory system that is capable to reduce memory size and improve memory performance. This project focuses on the memory management of compressed memory and a secondlevel memory and compares different compress memory allocation algorithms. Two dynamic memory allocation algorithms, which are lazy-fit and link-fit algorithms are analyzed to determine their...
Extremum seeking is a form of adaptive control where the steady-state input-output characteristic is optimized, without requiring any explicit knowledge about this input-output characteristic other than that it exists and that it has an extremum. Because extremum seeking is model free, it has proven to be both robust and effective in many different application domains. Equally being model free, there...
In this paper, interconnected iterative learning control loops are used to drive the output of general multi-input-multi-output nonlinear systems with general input uncertainties to the desired output. New convergence conditions are obtained to ensure the convergence of the overall system. A simulation example shows the effectiveness of the proposed method.
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