Abstract
This paper presents a systematic tuning approach for Model Predictive Control (MPC) parameters’ using an original LabVIEW-implementation of advanced metaheuristics algorithms. Perturbed Particle Swarm Optimization (pPSO), Gravitational Search Algorithm (GSA), Teaching-Learning Based Optimization (TLBO) and Grey Wolf Optimizer (GWO) metaheuristics are proposed to solve the formulated MPC tuning problem under operational constraints. The MPC tuning strategy is done offline for the selection of both prediction and control horizons as well as the weightings matrices. All proposed algorithms are firstly evaluated and validated on a benchmark of standard test functions. The same algorithms were then used to solve the formulated MPC tuning problem for two dynamical systems such as the magnetic levitation system MAGLEV 33-006, and the three-tank DTS200 process. Demonstrative results, in terms of statistical metrics and closed-loop systems responses, are presented and discussed in order to show the effectiveness and superiority of the proposed metaheuristics-tuned approach. The developed CAD interface for the LabVIEW implementation of the proposed metaheuristics is given and freely accessible for extended optimization puposes.
Highlights
In recent decades, the Model Predictive Control (MPC) has emerged as a leading control strategy due to its effectiveness and robust performance on complex systems under operational constraints
The standard deviation (STD) metric has a small value which means that the according algorithms are repeatable over the independent 30 runs, especially for the Grey Wolf Optimizer (GWO) one. From such a graphicalbased implementation of advanced metaheuristics, it is observed that there is no algorithm that excels in solving all considered functions
OPTIMIZATION RESULTS FOR THE MAGLEV SYSTEM OVER 30 RUNS
Summary
The Model Predictive Control (MPC) has emerged as a leading control strategy due to its effectiveness and robust performance on complex systems under operational constraints. There www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 10, No 6, 2019 have been a few research works that successfully integrated the metaheuristics-based optimization and MPC approach Most of these given works use classic and old metaheuristics such as the Genetic Algorithms (GA), standard Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and so on [18,19,20,21,22,23]. The perturbed Particle Swarm Optimization (pPSO) [24], Gravitational Search Algorithm (GSA) [25], TeachingLearning Based Optimization (TLBO) [26] and Grey Wolf Optimizer (GWO) [27] are have attracted considerable interest due to their effectiveness and wide range of applicability Their suitability to solve the MPC parameters’ tuning, formulated as a constrained optimization problem, presents a promising alternative for reducing the complexity of the MPC strategy.
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