摘要 : This article introduces tripartite adaptive predictive control (APC): A novel task parallelization scheme for the original APC algorithm. Profiling studies of an optimized embedded implementation of the multiple-input–multiple-out... 展开 This article introduces tripartite adaptive predictive control (APC): A novel task parallelization scheme for the original APC algorithm. Profiling studies of an optimized embedded implementation of the multiple-input–multiple-output version of APC show that the recursion section of the algorithm greatly impacts the overall execution time, especially for larger prediction horizons and for processes with more inputs and outputs. This limits the control iteration speed of APC. Therefore, by leveraging the independent side of the adaptation and prediction goals of APC, a triple task split of the algorithm is proposed that allows the control law to run asynchronously with recursion. Tripartite APC consists of an adaptation task, which keeps prediction model coefficient matrices up-to-date, a recursion task, which projects such coefficient matrices over the prediction horizon, and a control law task, which uses the latest available projected coefficient matrices to generate the control signal. This way, tripartite APC allows up to 37x faster control iterations as exhibited by experimental performance evaluations of the scheme on an embedded system. Tripartite APC is able to control faster dynamical processes than what was originally possible in APC, while still adapting to changes in process dynamics and keeping closed-loop performance. 收起