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• W651056422 abstract "The size of modern technological systems has grown significantly and the need for fast, online optimization-based control is a necessity. The complexity of these systems along with the need for increased accuracy in the computed outcome dictate the usage of optimization methods for the computation of the solution. Besides this, new data arrive in real time and in fast rates, that have to be incorporated efficiently in the control policy under design. Exploiting the state-of-the-art, multicore, computing architectures, we can achieve fast online solutions of optimal control problems by splitting a large problem into several smaller ones that can be solved in parallel. In this thesis, we apply an operator splitting technique to a generic convex optimal control problem. The resulting algorithm alternates between a quadratic regulator iteration, and a step in which single-period optimization problems are solved in parallel. Depending on the constraints and non-quadratic objective terms, these single-period problems can be solved extremely quickly, or even analytically in many cases. In the time-invariant case, pre-computing the gain matrices in the quadratic regulator problem gives another speed-up, as well as an algorithm that requires no division, and is therefore suitable for implementation with fixed-point processor. The method is demonstrated on several examples arising in different application areas. Furthermore, we develop an extension to this algorithm so that it can handle more generic optimal control problems and propose a different decomposition approach that achieves an even higher level of parallelization." @default.
• W651056422 created "2016-06-24" @default.
• W651056422 creator A5033109367 @default.
• W651056422 date "2012-06-22" @default.
• W651056422 modified "2023-09-24" @default.
• W651056422 title "Fast optimization-based control and estimation using operator splitting methods" @default.
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