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A New Gantry-Tau-Based Mechanism Using Spherical Wrist and Model Predictive Control-Based Motion Cueing Algorithm

Published online by Cambridge University Press:  31 October 2019

Mohammad Reza Chalak Qazani Open the ORCID record for Mohammad Reza Chalak Qazani [Opens in a new window] ,
Houshyar Asadi  and
Saeid Nahavandi
Mohammad Reza Chalak Qazani*
Affiliation:
Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds Campus, Geelong, Victoria 3217, Australia. E-mails: houshyar.asadi@deakin.edu.au, saeid.nahavandi@deakin.edu.au
Houshyar Asadi
Affiliation:
Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds Campus, Geelong, Victoria 3217, Australia. E-mails: houshyar.asadi@deakin.edu.au, saeid.nahavandi@deakin.edu.au
Saeid Nahavandi
Affiliation:
Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds Campus, Geelong, Victoria 3217, Australia. E-mails: houshyar.asadi@deakin.edu.au, saeid.nahavandi@deakin.edu.au
*
*Corresponding author. E-mail: m.r.chalakqazani@gmail.com
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Summary

The 3 degree-of-freedom Gantry-Tau manipulator with the addition of the spherical wrist mechanism which is called Gantry-Tau-3R is designed as a high-G simulation-based motion platform (SBMP) with the capability of generating the large linear and angular displacement. The combination of both parallel and serial manipulator in newly designed Gantry-Tau-3R mechanism improves the ability of the mechanism to regenerate larger motion signals with higher linear acceleration and angular velocity. The high-frequency signals are reproduced using the parallel part of the mechanism, and sustainable low-frequency accelerations are regenerated via the serial part due to the larger rotational motion capability, which will be used through motion cueing algorithm tilt coordination channel. The proportional integral derivative (PID) and fuzzy incremental controller (FIC) are developed for the proposed mechanism to show the high path tracking performance as a motion platform. FIC reduces the motion tracking error of the newly designed Gantry-Tau-3R and increases the motion fidelity for the users of the proposed SBMP. The proposed method is implemented using Matlab/Simulink software. Finally, the results demonstrate the accurate motion signal generation using linear model predictive motion cues with a fuzzy controller, which is not possible using the common parallel and serial manipulators.

Keywords

Simulation-based motion platformHigh-G Gantry-Tau-3R manipulatorPID controllerFICLinear Model Predictive-based Motion Cueing Algorithm

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Type
Articles
Information
Robotica , Volume 38 , Issue 8 , August 2020 , pp. 1359 - 1380
Copyright
© Cambridge University Press 2019

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References

Asadi, H., Mohamed, S., Lim, C. P. and Nahavandi, S., “Robust optimal motion cueing algorithm based on the linear quadratic regulator method and a genetic algorithm,” IEEE Trans. Syst. Man Cybern. Syst. 47(2), 238–254 (2017).Google Scholar
Haward, D., “The sanders teacher,” Flight 2(50), 1006–1007 (1910).Google Scholar
Page, R. L., “Brief History of Flight Simulation,” SimTecT 2000 Proceedings (John Wiley & Sons, Chichester, UK, 2000) pp. 11–17.Google Scholar
Allerton, D., Principles of Flight Simulation (John Wiley & Sons, Chichester, UK, 2009).Google Scholar
Casas, S., Olanda, R. and Dey, N., “Motion cueing algorithms: A review: algorithms, evaluation and tuning,” Int. J. Virtual Augmented Reality 1(1), 90–106 (2017).CrossRefGoogle Scholar
Nordmark, S., “Driving Simulators, Trends And Experiences,” Proceedings of the Driving Simulation Conference, Paris, France (1994).Google Scholar
Colombet, F., Dagdelen, M., Reymond, G., Pere, C., Merienne, F. and Kemeny, A., “Motion Cueing: What is the Impact on the Driver’s Behavior,” Proceedings of the Driving Simulation Conference, Paris, France (2008) pp. 171–181.Google Scholar
Challen, J., “Reality Bytes,” Driving Simulators (2008) pp. 50–53.Google Scholar
Amend, J., Cheng, N., Fakhouri, S. and Culley, B., “Soft robotics commercialization: Jamming grippers from research to product,” Soft. Rob. 3(4), 213–222 (2016).CrossRefGoogle Scholar
Sadeghian Borojeni, S., Boll, S. C., Heuten, W., Bülthoff, H. H. and Chuang, L., “Feel the Movement: Real Motion Influences Responses to Take-Over Requests in Highly Automated Vehicles,” Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Montreal, QC, Canada (ACM, 2018) p. 246.Google Scholar
Teufel, H., Nusseck, H.-G., Beykirch, K., Butler, J., Kerger, M. and Bülthoff, H., “MPI Motion Simulator: Development and Analysis of a Novel Motion Simulator,” AIAA Modeling and Simulation Technologies Conference and Exhibit, Hilton Head, South Carolina (2007) p. 6476.Google Scholar
Merlet, J.-P., Parallel Robots (Springer Science & Business Media, Berlin, 2006).Google Scholar
Dasgupta, B. and Mruthyunjaya, T., “The Stewart platform manipulator: A review,” Mech. Mach. Theory 35(1), 15–40 (2000).CrossRefGoogle Scholar
Tajaril, M. J., Pedrammehr, S., Qazani, M. R. C. and Nategh, M. J., “The Effects of Joint Clearance on the Kinematic Error of the Hexapod Tables,” 2017 5th RSI International Conference on Robotics and Mechatronics (ICRoM), Tehran, Iran (IEEE, 2017) pp. 39–44.CrossRefGoogle Scholar
Pedrammehr, S., Qazani, M. R. C., Asadi, H. and Nahavandi, S., “Control System Development of a Hexarotbased High-G Centrifugal Simulator,” 20th IEEE International Conference on Industrial Technology IEEE-ICIT 2019, Melbourne, Australia (2019).CrossRefGoogle Scholar
Pedrammehr, S., Qazani, M. R. C., Asadi, H. and Nahavandi, S., “Kinematic Manipulability Analysis of Hexarot Simulators,” 20th IEEE International Conference on Industrial Technology, IEEE-ICIT 2019, Melbourne, Australia (2019) pp. 13–15.Google Scholar
Pedrammehr, S., Qazani, M. R. C., Abdi, H. and Nahavandi, S., “Mathematical modelling of linear motion error for Hexarot parallel manipulators,” Appl. Math. Modell. 40(2), 942–954 (2016).CrossRefGoogle Scholar
Qazani, M. R. C., Pedrammehr, S. and Nategh, M. J., “A study on motion of machine tools’ hexapod table on freeform surfaces with circular interpolation,” Int. J. Adv. Manuf. Technol. 75(9–12), 1763–1771 (2014).CrossRefGoogle Scholar
Qazani, M. R. C., Pedrammehr, S., Rahmani, A., Danaei, B., Ettefagh, M. M., Rajab, A. K. S. and Abdi, H. “Kinematic analysis and workspace determination of hexarot-a novel 6-DOF parallel manipulator with a rotation-symmetric arm system,” Robotica 33(8), 1686–1703 (2015).CrossRefGoogle Scholar
Johannesson, L., Berbyuk, V. and Brogårdh, T., “Gantry-Tau–A New Three Degrees of Freedom Parallel Kinematic Robot,” Proceedings of the Mekatronikmöte 2003, Göteborg, Sweden (2003) pp. 1–6.Google Scholar
Tyapin, I., Hovland, G. and Brogardh, T., “Workspace optimisation of a reconfigurable parallel kinematic manipulator,” 2007 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Switzerland (IEEE, 2007) pp. 1–6.Google Scholar
Williams, I., Hovland, G. and Brogardh, T., “Kinematic Error Calibration of the Gantry-Tau Parallel Manipulator,” Proceedings 2006 IEEE International Conference on Robotics and Automation, Orlando, Florida (IEEE, 2006), pp. 4199–4204.Google Scholar
Murray, M., Hovland, G. and Brogårdh, T., “Singularityfree Reconfiguration of the 5-DOF Gantry-Tau Parallel Kinematic Machine,” Proceedings of 2nd International Workshop on Fundamental Issues and Future Research Directions for Parallel Mechanisms and Manipulators, Citeseer, Montpellier (2008) pp. 21–22.Google Scholar
Dressler, I., “Modeling and Control of Stiff Robots for Flexible Manufacturing,” Ph.D. Thesis (2012).Google Scholar
Khosravi, M. A. and Taghirad, H. D., “Dynamic modeling and control of parallel robots with elastic cables: Singular perturbation approach,” IEEE Trans. Rob . 30(3), 694–704 (2014).CrossRefGoogle Scholar
Kim, H. S., Cho, Y. M. and Lee, K.-I., “Robust nonlinear task space control for 6 DOF parallel manipulator,” Automatica 41(9), 1591–1600 (2005).CrossRefGoogle Scholar
Mamdani, E. and Baaklini, N., “Prescriptive method for deriving control policy in a fuzzy-logic controller,” Electron. Lett. 11(25), 625–626 (1975).CrossRefGoogle Scholar
Mann, G. K., Hu, B.-G. and Gosine, R. G., “Two-level tuning of fuzzy PID controllers,” IEEE Trans. Syst. Man Cybern. Part B 31(2), 263–269 (2001).CrossRefGoogle ScholarPubMed
Asadi, H., Kaboli, S., Oladazimi, M. and Safari, M., “A review on Li-ion battery charger techniques and optimize battery charger performance by fuzzy logic,” ICICA 18(201), 89–96 (2011).Google Scholar
Asadi, H., Mohamed, S. and Nahavandi, S., “Incorporating human perception with the motion washout filter using fuzzy logic control,” IEEE/ASME Trans. Mech . 20(6), 3276–3284 (2015).CrossRefGoogle Scholar
Asadi, H., Mohammadi, A., Mohamed, S., Zadeh, D. R. and Nahavandi, S., “Adaptive Washout Algorithm based Fuzzy Tuning for Improving Human Perception,” International Conference on Neural Information Processing (Springer, Berlin, 2014) pp. 483–492.CrossRefGoogle Scholar
Asadi, H., Lim, C. P., Mohamed, S., Nahavandi, D. and Nahavandi, S., “Increasing motion fidelity in driving simulators using a fuzzy-based washout filter,” IEEE Trans. Intell. Veh. 4(2), 298–308 (2019).CrossRefGoogle Scholar
Asadi, H., Lim, C. P., Mohammadi, A., Mohamed, S., Nahavandi, S. and Shanmugam, L., “A genetic algorithm–based nonlinear scaling method for optimal motion cueing algorithm in driving simulator,” Proc. Inst. Mech. Eng. Part I: J. Syst. Cont. Eng. 232(8), 1025–1038 (2018).Google Scholar
Asadi, H., Mohamed, S., Nelson, K., Nahavandi, S. and Oladazimi, M., “An Optimal Washout Filter based on Genetic Algorithm Compensators for Improving Simulator Driver Perception,” DSC 2015: Proceedings of the Driving Simulation Conference & Exhibition (Max Planck Institute for the Advancement of Science, Germany, 2015) pp. 1–10.Google Scholar
Asadi, H., Mohamed, S., Nelson, K., Nahavandi, S. and Zadeh, D. R., “Human Perception-based Washout Filtering using Genetic Algorithm,” International Conference on Neural Information Processing (Springer, Berlin, 2015) pp. 401–411.CrossRefGoogle Scholar
Dagdelen, M., Reymond, G., Kemeny, A., Bordier, M. and Maïki, N., “MPC based Motion Cueing Algorithm: Development and Application to the ULTIMATE Driving Simulator,” Conférence Simulation de Conduite, Paris (2004) pp. 221–233.Google Scholar
Groen, E. and Bles, W., “How to use body tilt for the simulation of linear self motion,” J. Vestibular Res. 14(5), 375–385 (2004).Google ScholarPubMed
Garrett, N. J. and Best, M. C., “Model predictive driving simulator motion cueing algorithm with actuator-based constraints,” Veh. Sys. Dyn. 51(8), 1151–1172 (2013).CrossRefGoogle Scholar
Mohammadi, A., Asadi, H., Mohamed, S., Nelson, K. and Nahavandi, S., “MPC-based Motion Cueing Algorithm with Short Prediction Horizon using Exponential Weighting,” 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest (IEEE, 2016) pp. 000521–000526.CrossRefGoogle Scholar
Mohammadi, A., Asadi, S. H., Nelson, K. and Nahavandi, S., “Future Reference Prediction in Model Predictive Control Based Driving Simulators,” Australasian Conference on Robotics and Automation (ACRA2016), Brisbane, Australia (2016).Google Scholar
Mohammadi, A., Asadi, H., Mohamed, S., Nelson, K. and Nahavandi, S., “Optimizing model predictive control horizons using genetic algorithm for motion cueing algorithm,” Expert Syst. Appl . 92, 73–81 (2018).CrossRefGoogle Scholar
Mohammadi, A., Asadi, H., Mohamed, S., Nelson, K. and Nahavandi, S., “Multiobjective and interactive genetic algorithms for weight tuning of a model predictive control-based motion cueing algorithm,” IEEE Trans. Cybern . 49(9), 3471–3481 (2018).CrossRefGoogle Scholar
Qazani, M. R. C., Asadi, H. and Nahavandi, S., “A Decoupled Linear Model Predictive Control-Based Motion Cueing Algorithm for simulation-based motion platform with limited workspace,” IEEE International Conference on Systems, Man, and Cybernetics (SMC), Bari, Italy (2019) pp. 1–6.Google Scholar
Qazani, M. R. C., Asadi, H. and Nahavandi, S., “A Model Predictive Control-Based Motion Cueing Algorithm with Consideration of Joints’ Limitations for Hexapod Motion Platform,” SMC, Bari, Italy (IEEE, 2019).Google Scholar
Dressler, I., Robertsson, A. and Johansson, R., “Accuracy of Kinematic and Dynamic Models of a Gantry-Tau Parallel Kinematic Robot,” Proceedings 2007 IEEE International Conference on Robotics and Automation, Roma, Italy (IEEE, 2007) pp. 883–888.CrossRefGoogle Scholar
Qazani, M. R. C., Pedrammehr, S., Rahmani, A., Shahryari, M., Rajab, A. K. S. and Ettefagh, M. M., “An experimental study on motion error of hexarot parallel manipulator,” Int. J. Adv. Manuf. Technol. 72(9–12), pp. 1361–1376 (2014).CrossRefGoogle Scholar
Pedrammehr, S., Qazani, M. R. C. and Nahavandi, S., “A Novel Axis Symmetric Parallel Mechanism with Coaxial Actuated Arms,” 2018 4th International Conference on Control, Automation and Robotics (ICCAR), Auckland, New Zealand (IEEE, 2018) pp. 476–480.CrossRefGoogle Scholar
Pedrammehr, S., Qazani, M. R. C., Nahavandi, S. and Asadi, H., “Control System Development of a Hexarot-based High-G Centrifugal Simulator,” Presented at the The 20th IEEE International Conference on Industrial Technology, Melbourne, Australia (2019).CrossRefGoogle Scholar
Mitchell, M., An Introduction to Genetic Algorithms (MIT Press, Cambridge, MA, USA, 1998).CrossRefGoogle Scholar
Sadeghi, J., Sadeghi, S. and Niaki, S. T. A., “Optimizing a hybrid vendor-managed inventory and transportation problem with fuzzy demand: An improved particle swarm optimization algorithm,” Inf. Sci. 272, 126–144 (2014).CrossRefGoogle Scholar
Zadeh, L. A., Fuzzy Logic: Advanced Concepts and Structures (IEEE Educational Activities Department, MIT Press, Cambridge, Massachusetts, USA, 1992).Google Scholar
Lin, W.-Y., Lee, W.-Y. and Hong, T.-P., “Adapting crossover and mutation rates in genetic algorithms,” J. Inf. Sci. Eng. 19(5), 889–903 (2003).Google Scholar
Deb, K. and Agrawal, S., “Understanding Interactions among Genetic Algorithm Parameters,” In: FOGA (Morgan Kaufmann, San Francisco, CA, USA, 1998) pp. 265–286.Google Scholar
De Jong, K., “Adaptive system design: A genetic approach,” IEEE Trans. Syst. Man Cybern. 10(9), 566–574 (1980).CrossRefGoogle Scholar
De Jong, K. A., “Analysis of the behavior of a class of genetic adaptive systems,” PhD dissertation (University of Michigan Press, Ann Arbor, MI, USA, 1975).Google Scholar
Hesser, J. and Männer, R., “Towards an Optimal Mutation Probability for Genetic Algorithms,” International Conference on Parallel Problem Solving from Nature (Springer, Berlin, 1990) pp. 23–32.CrossRefGoogle Scholar
Ochoa, G., Harvey, I. and Buxton, H., “On Recombination and Optimal Mutation Rates,” Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation-Volume 1 (Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1999) pp. 488–495.Google Scholar
Roeva, O., Fidanova, S. and Paprzycki, M., “Population Size Influence on the Genetic and Ant Algorithms Performance in Case of Cultivation Process Modeling,” 2013 Federated Conference on Computer Science and Information Systems (FedCSIS), Krakow, Poland (2015) pp. 107–120.Google Scholar
Asadi, H., Mohamed, S., Lim, C. P. and Nahavandi, S., “A review on otolith models in human perception,” Behav. Brain Res. 309, 67–76 (2016).CrossRefGoogle ScholarPubMed
Houck, J. A., Telban, R. J. and Cardullo, F. M., “Motion cueing algorithm development: Human-centered linear and nonlinear approaches,” (NASA, Hampton, 2005). Available from: https://archive.org/details/NASA_NTRS_Archive_20050180246Google Scholar
Asadi, H., Mohamed, S., Lim, C. P., Nahavandi, S. and Nalivaiko, E., “Semicircular canal modeling in human perception,” Rev. Neurosci. 28(5), 537–549 (2017).CrossRefGoogle ScholarPubMed
Asadi, H., Mohamed, S., Rahim Zadeh, D. and Nahavandi, S., “Optimisation of nonlinear motion cueing algorithm based on genetic algorithm,” Veh. Syst. Dyn. 53(4), 526–545 (2015).CrossRefGoogle Scholar
Qazani, M. R. C., Asadi, H., Pedrammehr, S. and Nahavandi, S., “Performance Analysis and Dexterity Monitoring of Hexapod-Based Simulator,” 2018 4th International Conference on Control, Automation and Robotics (ICCAR), Auckland, New Zealand (IEEE, 2018) pp. 226–231.CrossRefGoogle Scholar
coin-or, qpOASES, https://projects.coin-or.org/qpOASES.Google Scholar
Pedrammehr, S., Qazani, M. R. C., Asadi, H. and Nahavandi, S., “Kinematic Manipulability Analysis of Hexarot Simulators,” 20th IEEE International Conference on Industrial Technology, Melbourne, Australia (2019).CrossRefGoogle Scholar
Fisher, R. A., “Statistical methods for research workers,” In: Breakthroughs in Statistics (Springer, Berlin, 1992) pp. 66–70.CrossRefGoogle Scholar
Kendall, M. G. and Stuart, A., “The advanced theory of statistics,” (Charles Griffin & Company Limited, London, UK, 1945).Google Scholar
Press, W. H., Teukolsky, S. A., Vetterling, W. T. and Flannery, B. P., Numerical Recipes in C++: The Art of Scientific Computing, vol. 2 (Cambridge University Press, Cambridge, 2007) p. 1002.Google Scholar
Moro, S., Ramos, P., Esmerado, J. and Jalali, S. M. J., “Can we trace back hotel online reviews’ characteristics using gamification features?” Int. J. Inf. Manage. 44, 88–95 (2019).CrossRefGoogle Scholar
Jalali, S. M. J., Mahdizadeh, E., Mahmoudi, M. R. and Moro, S., “Analytical assessment process of e-learning domain research between 1980 and 2014,” Int. J. Manage. Educ. 12(1), 43–56 (2018).CrossRefGoogle Scholar
Jalali, J., Mohammad, S. and Park, H. W., “Conversations about open data on Twitter,” IJCT 13(1), 31–37 (2017).Google Scholar
Zheng, H., Zhang, J. and Nahavandi, S., “Learning to detect texture objects by artificial immune approaches,” Future Gener. Comput. Syst. 20(7), 1197–1208 (2004).CrossRefGoogle Scholar
Amiri, M. J., Abedi-Koupai, J., Jafar Jalali, S. M. and Mousavi, S. F., “Modeling of fixed-bed column system of Hg (II) ions on ostrich bone ash/nZVI composite by artificial neural network,” J. Environ. Eng. 143(9), 04017061 (2017).CrossRefGoogle Scholar
Jalali, S. M. J., Khosravi, A., Alizadehsani, R., Salaken, S. M., Kebria, P. M., Puri, R. and Nahavandi, S., “Parsimonious Evolutionary-based Model Development for Detecting Artery Disease,” 20th IEEE International Conference on Industrial Technology, Melbourne, Australia (2019) pp. 800–805.Google Scholar
Oladazimi, M., Vaneghi, F. M., Safari, M., Asadi, H. and Kaboli, S. H. A., “A Review for Feature Extraction of EMG Signal Processing,” 4th International Conference on Computer and Automation Engineering (ICCAE 2012), Mumbai, India (ASME Press, 2012).Google Scholar
Jalali, S., Moro, S., Mahmoudi, M. R., Ghaffary, K. A., Maleki, M. and Alidoostan, A., “A comparative analysis of classifiers in cancer prediction using multiple data mining techniques,” Comp. Anal. Classifiers Cancer Prediction Multiple Data Min. Tech. 1(2), 166–178 (2017).Google Scholar
Vanani, I. R. and Jalali, S. M. J., “A comparative analysis of emerging scientific themes in business analytics,” Int. J. Bus. Inf. Syst. 29(2), 183–206 (2018).Google Scholar
Jalali, S. M. J. and Park, H. W., “State of the art in business analytics: Themes and collaborations,” Qual. Quant. 52(2), 627–633 (2018).CrossRefGoogle Scholar
Vanani, I. R. and Jalali, S. M. J., “Analytical evaluation of emerging scientific trends in business intelligence through the utilisation of burst detection algorithm,” Int. J. Bibliometr. Bus. Manage. 1(1), 70–79 (2017).CrossRefGoogle Scholar
Jalali, S. M. J. and Mahizadeh, E., “The investigation of e-business trends by using social network analysis technique during 1980–2015,” J. Inf. Tech. Manage. 8(3), 499–518 (2016).Google Scholar
Mohammadi, A., Asadi, H., Mohamed, S., Nelson, K. and Nahavandi, S., “openGA, a C++ Genetic Algorithm Library,” 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, Canada (IEEE, 2017) pp. 2051–2056.CrossRefGoogle Scholar