Multiobjective and interactive genetic algorithms for weight tuning of a model predictive control-based motion cueing algorithm

Mohammadi, Arash, Asadi, Houshyar, Mohamed, Shady, Nelson, Kyle and Nahavandi, Saeid 2018, Multiobjective and interactive genetic algorithms for weight tuning of a model predictive control-based motion cueing algorithm, IEEE Transactions on Cybernetics, doi: 10.1109/TCYB.2018.2845661.

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Title Multiobjective and interactive genetic algorithms for weight tuning of a model predictive control-based motion cueing algorithm
Author(s) Mohammadi, ArashORCID iD for Mohammadi, Arash orcid.org/0000-0001-7634-8212
Asadi, HoushyarORCID iD for Asadi, Houshyar orcid.org/0000-0002-3620-8693
Mohamed, ShadyORCID iD for Mohamed, Shady orcid.org/0000-0002-8851-1635
Nelson, KyleORCID iD for Nelson, Kyle orcid.org/0000-0003-1956-5493
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name IEEE Transactions on Cybernetics
Total pages 11
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2018-06-26
ISSN 2168-2267
2168-2275
Keyword(s) interactive genetic algorithm (GA)
model predictive control (MPC)
motion cueing algorithm (MCA)
multiobjective GA (MO-GA)
tuning
weight selection
Summary Driving simulators are effective tools for training, virtual prototyping, and safety assessment which can minimize the cost and maximize road safety. Despite the aim of a realistic motion generation for the impression of real-world driving, motion simulators are bound in a limited workspace. Motion cueing algorithms (MCAs) aim to plan an acceptable motion feeling for drivers, without infringing the simulated boundaries. Recently, model predictive control (MPC) has been widely used in MCAs; however, the tuning process for finding the best weights of the MPC optimization is still a challenge. As there are several objectives for the optimization without any standard weighting for solution evaluations, a nonbiased scalarization of solutions for the purpose of comparison is impossible. In this paper, a clear method for obtaining the best MPC weighting has been proposed. This method searches for the best tune of MPC cost function weights, reduces the user burden for weight tuning while receiving feedback from the user satisfaction. The MPC-based MCA weights are optimized using a multiobjective genetic algorithm (GA) considering objectives, such as minimization of motion inputs (linear acceleration and angular velocity), input rates, output displacements and the sensed motion errors. Any process based on trial-and-error has been omitted. The adjusted weights have to satisfy a set of predefined conditions related to maximum tolerated error and maximum displacement. The obtained Pareto-front is used for decision making via an interactive GA (IGA), aiming for maximization of the decision maker's satisfaction. A Web interface is developed to interact with the IGA and to influence the region of searching. Simulation results show the superiority of the proposed method compared with the previous empirical tuning method. The sensed motion error is minimized using the proposed method and with the same available workspace, a more realistic motion can be rendered to the driver.
Language eng
DOI 10.1109/TCYB.2018.2845661
Field of Research 080110 Simulation and Modelling
Copyright notice ©2018, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30110952

Document type: Journal Article
Collections: Centre for Intelligent Systems Research
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