عنوان مقاله [English]
نویسندگان [English]چکیده [English]
Skilled pilot training in aviation industry is of great importance. This in real terms due to high costs and safety considerations, is facing with serious obstacles. Thus providing low cost, efficient and repeatable flight training conditions attracted a broad range of researches. Flight simulators are such devices for training the skilled pilots. These tools aim to provide the same feeling of real flying for pilots, and give the opportunity to pilot to react under terms of the flight conditions. Motion cueing design algorithm is of the salient challenges in the design of these devices. The algorithm receives the linear acceleration and angular velocity of the real flight as the inputs and is due to calculate the appropriate movements of the actuators such that the infinite plane motions to get constricted to finite motions in the limited work space of the flight simulator. This has to be done in a way that the pilot in flight simulator feels as could as the same sense of motion of the pilot in aircraft. The authors aim to develop an optimal motion cueing algorithm for 6 DoF flight simulator with fuzzy compensated system specially focusing on the maximum use of workspace of the simulator to obtain more appropriate sense of motion. Comparing the results of the proposed fuzzy compensated and the conventional optimal motion cueing algorithms, shows an evident improvements in terms of smaller movements of actuators while achieving more appropriate motions of the flight simulator.
 Winslow, C. D., 1917. With the French flying corps. Scribner.
 Ray. L., 2000. Brief history of flight simulation. SimTecT 2000 Proceedings: 11-17.
 Parrish, R. V., J. E. Dieudonne, and D. J. J. 1975. Coordinated adaptive washout for motion simulators. Journal of Aircraft, 12(1): 44-50.
 Reid, L. D., and Nahon, A. M. 1985. Flight Simulation Motion-Base Drive Algorithms: Part 1- Developing and Testing the Equations. Institute for Aerospace Studies, Toronto University.
 Reid, L. D., and Nahon, A. M., 1986. Flight Simulation Motion-Base Drive Algorithms.: Part 2- Selecting The System Parameters. Institute for Aerospace Studies, Toronto University.
 Sivan, R., Ish-Shalom, J., and Huang, K. J., 1982. An optimal control approach to the design of moving flight simulators. Systems, Man and Cybernetics, IEEE Transactions on, 12(6): 818-827.
 Wu, W., and Cardullo M. F., 1997. Is there an optimum motion cueing algorithm. In Proceedings of the AIAA Modelling and Simulation Technologies Conference, NewOrleans 13: 23-29.
 Wu, W., 1997. Development of Cueing Algorithm for the Control of Simulator Motion Systems. MS Thesis, State University of New York at Binghamton.
 Houck, J. A., Telban J. R., and Cardullo F. M., 1999. Developments in human centered cueing algorithms for control of flight simulator motion systems. American Institute of Aeronautics and Astronautics, AIAA-99-4328.
 Telban, J. R., 2002. A nonlinear motion cueing algorithm with a human perception model. Energy, Simulation-training, Ocean Engineering and Instrumentation: Research Papers of the Link Foundation Fellows 2: 97-127.
 Telban, J. R., and Cardullo M. F., 2005. Motion cueing algorithm development: Human-centered linear and nonlinear approaches. NASA TechReport CR, 213747.
 Telban, J. R., Cardullo M. F., and Kelly, C. L., 2005. Motion cueing algorithm development: new motion cueing program implementation and tuning. NASA Report CR, 213746.
 Telban, J. R., Wu, W., and Cardullo, M. F., 2000. Motion cueing algorithm development: Initial investigation and redesign of the algorithms. National Aeronautics and Space Administration, Langley Research Center.
 Martin, J., Dennis. J. 1977. A Digital Program for Motion Washout on Langley's Six-Degree-of-Freedom Motion Simulator. NASA CR-145219.
 Aminzadeh, M., A. Mahmoodi, and M. Sabzehparvar. 2012. Optimal Motion-Cueing Algorithm using Motion System Kinematics. European Journal of Control, 18(4): 363-375.
 Naseri, A., and P. Grant. 2005. An improved adaptive motion drive algorithm. American Institute of Aeronautics and Astronautics, AIAA.
 Telban, R. J., F. M. Cardullo, and J. A. Houck. 2002. A nonlinear, human-centered approach to motion cueing with a neurocomputing solver. In AIAA Modeling and Simulation Technologies Conference and exhibit.
 Zaychik, K. B., and F. M. Cardullo. 2012. Nonlinear Motion Cueing Algorithm: Filtering at Pilot Station and Development of the Nonlinear Optimal Filters for Pitch and Roll. NASA TechReport CR, 217567.
 Nehaoua, L., H. Mohellebi, A. Amouri, H. Arioui, S. Espié, and A. Kheddar. 2008. Design and control of a small-clearance driving simulator. Vehicular Technology, IEEE Transactions 57(2): 736-746.
 Arioui, H., Hima, S., Nehaoua L., Bertin J. R., and Espié, S., 2011. From design to experiments of a 2-DOF vehicle driving simulator. Vehicular Technology, IEEE Transactions on, 60(2): 357-368.
 Wang, X., Li, L., and Zhang, W., 2008. Research on fuzzy control washout algorithm of locomotive driving simulator. IEEE Intelligent Control and Automation, 7: 3737-3741.
 Chen, S. H., and Fu, C. L., 2010. An optimal washout filter design for a motion platform with senseless and angular scaling maneuvers. IEEE American Control Conference (ACC) : 4295-4300.
 Chen, S. H., and Fu, C. L., 2011. An optimal washout filter design with fuzzy compensation for a motion platform. In 18th IFAC World Congress Milano, Italy.
 Guiatni, M., K. Fellah, and Morsly, Y., 2013. Fuzzy/PSO BasedWashout Filter for Inertial Stimuli Restitution in Flight Simulation., 7th International Conference on Sensor Technologies and Applications SENSORCOMM: 236-242.
 Day, B. L., and Fitzpatrick C. R. 2005. The vestibular system. Current Biology 15: 583-586.
 Mahmoodi, A., Sayadi, A., and Menhaj B. M., 2014. Solution of forward kinematics in Stewart platform using six rotary sensors on joints of three legs. Advanced Robotics, 28(1): 27-37.