LabVIEW Perturbed Particle Swarm Optimization Based Approach for Model Predictive Control Tuning

Abstract

In this paper, a new Model Predictive Controller (MPC) parameters tuning strategy is proposed using a LabVIEW-based perturbed Particle Swarm Algorithm (pPSA). This original LabVIEW implementation of this metaheuristic algorithm is firstly validated on some test functions in order to show its efficiency and validity. The optimization results are compared with the standard PSO approach. The parameters tuning problem, i.e. the weighting factors on the output error and input increments of the MPC algorithm, is then formulated and systematically solved, using the proposed LabVIEW pPSA algorithm. The case of a Magnetic Levitation (MAGLEV) system is investigated to illustrate the robustness and superiority of the proposed pPSA-based tuning MPC approach. All obtained simulation results, as well as the statistical analysis tests for the formulated control problem with and without constraints, are discussed and compared with the Genetic Algorithm Optimization (GAO)-based technique in order to improve the effectiveness of the proposed pPSA-based MPC tuning methodology.

Design and simulation of a 2DOF PID controller based on particle swarm optimization algorithms for a thermal phase of hybrid vehicle

“…The convergence and parameters selection of this algorithm are proved [22]. PSO theory has been enormously successful in various industrial domains, but more significantly in control and optimal engineering design as shown in [21], [22].…”

“…Eberhart [20]. This advanced global technique is inspired by the swarming behavior of biological populations [21]. In comparison with an others methods of metaheuristics, this optimization technique it is a simple concept, it is computationally efficient algorithm and easy to implement.…”

Advanced Metaheuristics-based Tuning of Effective Design Parameters for Model Predictive Control Approach

“…These control laws are updated at each sampling time k in order to minimize the cost function (1) under various operational constrains [5,6,23]:…”

“…Demonstrative results and analyses are given and discussed through this subsection. The position dynamics of the metal sphere of MAGLEV system is modeled as follows [28,29]: (23) Since that the MAGLEV 33-006 system is SISO type, the unknown weighting matrices Q and R of the MPC approach are scalars. Therefore, the formulated optimization problem (5) has four decision variables, i.e.…”

“…Up to date, there www.ijacsa.thesai.org 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]. Unfortunately, all these algorithms for the formulated problems present limitations in terms of premature convergence and unbalanced exploitation/exploration mechanism.…”