Parameter optimization of model predictive control using PSO

Susuki, R.; Kawai, Fukiko; Nakazawa, Chikashi; Matsui, Tetsuro; Aiyoshi, Eitaro

doi:10.1109/sice.2008.4654987

Cited by 17 publications

(11 citation statements)

References 6 publications

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“…Initially developed by Kennedy and Eberhart in 1995 [1], this technique has proven to be effective for neural networks weight calculation [9], business optimization [10], and parameter estimations [11]. Both behaviour and efficiency of the algorithm rely on the parameters shown in Table 1 [1].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A time performance comparison of particle swarm optimization in mobile devices

et al. 2016

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Abstract. This paper deals with the comparison of three implementations of Particle Swarm Optimization (PSO), which is a powerful algorithm utilized for optimization purposes. Xamarin, a cross-platform development software, was used to build a single C# application capable of being executed on three different mobile operating systems (OS) devices, namely Android, iOS, and Windows Mobile 10, with native level performance. Seven thousand tests comprising PSO evaluations of seven benchmark functions were carried out per mobile OS. A statistical evaluation of time performance of the test set running on three similar devices -each running a different mobile OS-is presented and discussed. Our findings show that PSO running on Windows Mobile 10 and iOS devices have a better performance in computation time than in Android.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

A time performance comparison of particle swarm optimization in mobile devices

et al. 2016

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“…Time domain goals are also considered in Susuki et al (2008) to define a tuning method for unconstrained state-feedback controllers. The authors emphasized the role of the transient characteristics in process startups and they solved the tuning problem using the particle swarm optimization approach.…”

Section: Introductionmentioning

confidence: 99%

Tuning of Model Predictive Control With Multi-Objective Optimization

Yamashita

Zanin

Odloak

2016

Braz. J. Chem. Eng.

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-Two multi-objective optimization based tuning methods for model predictive control are proposed. Both methods consider the minimization of the error between the closed-loop response and an output reference trajectory as tuning goals. The first approach is based on the ranking of the outputs according to their importance to the plant operation and it is solved by a lexicographic optimization algorithm. The second method solves a compromise optimization problem. The former is designed for systems in which the number of inputs is equal to the number of outputs, while the latter can also be applied to non-square systems. The main contribution is an automated tuning framework based on a straightforward goal definition. The proposed methods are tested on a finite horizon model predictive controller in closed-loop with a 3x3 subsystem of the Shell Heavy Oil Fractionator benchmark system. The simulation results show that the methods proposed here can be a useful tool to reduce the commissioning time of the controller. The methods are compared to an existing multi-objective optimization based tuning approach. The computational time required to run the proposed tuning algorithms is considerably reduced when compared to the existing approach and, moreover, it does not need an a posteriori decision to select a solution from a set of Pareto optimal solutions.

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“…However, metaheuristics algorithms Yang (2010); Dréo et al (2006); Siarry and Michalewicz (2008); Gendreau and Potvin (2010), which have reached a remarkable maturity, especially the Particle Swarm Optimization technique, present a solution to such problems. Recently, Suzuki et al (2008) introduced an automatic tuning of predictive controller parameters using the standard PSO technique. Newly, Xinchao (2010) introduced a new "perturbed PSO" algorithm, denoted as pPSA.…”

Section: Introductionmentioning

confidence: 99%

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

2016

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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.

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Parameter optimization of model predictive control using PSO

Cited by 17 publications

References 6 publications

A time performance comparison of particle swarm optimization in mobile devices

A time performance comparison of particle swarm optimization in mobile devices

Tuning of Model Predictive Control With Multi-Objective Optimization

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

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