Execution Time Decrease for Controllers Based on Adaptive Particle Swarm Optimization

Execution time is an important topic when using metaheuristic-based optimization algorithms within control structures. This is the case with Receding Horizon Control, whose controller makes predictions based on a metaheuristic algorithm. Because the closed loop's main time constraint is that the controller's run time must be smaller than the sampling period, this paper joins the authors' previous work in investigating decreasing execution time. In this context, good results have been obtained by introducing the reference control profile concept that leads to the idea of adapting the control variables' domains for each sampling period. This paper continues to address this concept, which is adjusted to harmonize with the Particle Swarm Optimization algorithm. Moreover, besides adapting the control variables' domains, the proposed controller's algorithm tunes these domains to avoid losing convergence. A simulation study validates the new techniques using a nontrivial process model and considering three modes in which the controller works. The results showed that the proposed techniques have practical relevance and significantly decrease execution time.

Automated summary

Execution time is crucial in control structures using metaheuristic-based optimization algorithms. This paper investigates decreasing execution time in the context of Receding Horizon Control, where a metaheuristic algorithm is used for prediction. The concept of adapting the control variables' domains for each sampling period is introduced, along with tuning the domains to maintain convergence. Simulation results demonstrate the practical relevance and significant reduction in execution time achieved using the proposed techniques.