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An O (logN) Parallel Algorithm for Newton Step Computation in Model Predictive Control
"... Abstract The use of Model Predictive Control in industry is steadily increasing as more complicated problems can be addressed. Due to that online optimization is usually performed, the main bottleneck with Model Predictive Control is the relatively high computational complexity. Hence, a lot of res ..."
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Abstract The use of Model Predictive Control in industry is steadily increasing as more complicated problems can be addressed. Due to that online optimization is usually performed, the main bottleneck with Model Predictive Control is the relatively high computational complexity. Hence, a lot of research has been performed to find efficient algorithms that solve the optimization problem. As parallelism is becoming more commonly used in hardware, the demand for efficient parallel solvers for Model Predictive Control has increased. In this paper, a tailored parallel algorithm that can adopt different levels of parallelism for solving the Newton step is presented. With sufficiently many processing units, it is capable of reducing the computational growth to logarithmic growth in the prediction horizon. Since the Newton step computation is where most computational effort is spent in both interiorpoint and activeset solvers, this new algorithm can significantly reduce the computational complexity of highly relevant solvers for Model Predictive Control.
A Parallel Riccati Factorization Algorithm with Applications to Model Predictive Control
"... AbstractModel Predictive Control (MPC) is increasing in popularity in industry as more efficient algorithms for solving the related optimization problem are developed. The main computational bottleneck in online MPC is often the computation of the search step direction, i.e. the Newton step, whic ..."
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AbstractModel Predictive Control (MPC) is increasing in popularity in industry as more efficient algorithms for solving the related optimization problem are developed. The main computational bottleneck in online MPC is often the computation of the search step direction, i.e. the Newton step, which is often done using generic sparsity exploiting algorithms or Riccati recursions. However, as parallel hardware is becoming increasingly popular the demand for efficient parallel algorithms for solving the Newton step is increasing. In this paper a tailored, noniterative parallel algorithm for computing the Riccati factorization is presented. The algorithm exploits the special structure in the MPC problem, and when sufficiently many processing units are available, the complexity of the algorithm scales logarithmically in the prediction horizon. Computing the Newton step is the main computational bottleneck in many MPC algorithms and the algorithm can significantly reduce the computation cost for popular stateoftheart MPC algorithms.
Model Predictive Control
, 2014
"... Technical reports from the Automatic Control group in Linköping are available from ..."
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Technical reports from the Automatic Control group in Linköping are available from