Aerospace launch vehicle control: an intelligent adaptive approach |
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| Institution: | 1. Control and Intelligent Processing Center of Excellence, Department of Mechanical Engineering, University of Tehran, P.O. Box 14875-347, Tehran, Iran;2. Control and Intelligent Processing Center of Excellence, Department of Electrical and Computer Engineering, University of Tehran, P.O. Box 14395-515, Tehran, Iran;3. School of Cognitive Science, IPM, Tehran, Iran;4. Department of Aerospace Engineering, K.N. Toosi University of Technology, P.O. Box 16765-3381, Tehran, Iran;1. National Institute for Space Research – INPE, Av. dos Astronautas, 1758, SP 12227-010, Brazil;2. Federal University of ABC – UFABC, Av. dos Estados, 5001, Santo André, SP 09210-580, Brazil;1. School of Mathematical Science, Dalian University of Technology, Dalian, Liaoning 116024, PR China;2. School of Environmental and Biological Science and Technology, Dalian University of Technology, Dalian, Liaoning 116012, PR China;1. School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China;2. School of Electrical and Electronic Engineering, The University of Manchester, Manchester, M13 9PL, UK;1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;2. Academy of Frontier Science, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;3. Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22903, USA |
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| Abstract: | A theoretical analysis of on-line autonomous intelligent adaptive tracking controller based on emotional learning model in mammalians brain (BELBIC) for aerospace launch vehicle is presented. The control algorithm is provided with some sensory inputs and reward signal, subsequently it autonomously seeks the proper control signal to be executed by actuators, thus eliminating tracking error without pre-knowledge of the plant dynamics. The algorithm is very robust and fast in adaptation with dynamical change in the plant, due to its on-line learning ability. Development and application of this algorithm for an aerospace launch vehicle during atmospheric flight in an experimental setting is presented to illustrate the performance of the control algorithm. |
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