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The use of Model Predictive Control in industry is steadily increasing asmore complicated problems can be addressed. Due to that online optimization isusually performed, the main bottleneck with Model Predictive Control is therelatively high computational complexity. Hence, a lot of research has beenperformed to find efficient algorithms that solve the optimization problem. Asparallelism is becoming more commonly used in hardware, the demand forefficient parallel solvers for Model Predictive Control has increased. In thispaper, a tailored parallel algorithm that can adopt different levels ofparallelism for solving the Newton step is presented. With sufficiently manyprocessing units, it is capable of reducing the computational growth tologarithmic growth in the prediction horizon. Since the Newton step computationis where most computational effort is spent in both interior-point andactive-set solvers, this new algorithm can significantly reduce thecomputational complexity of highly relevant solvers for Model PredictiveControl.

An O(log N) Parallel Algorithm for Newton Step Computation in Model Predictive Control