Robust Nonlinear Adaptive Control of Unmanned Aerial Vehicles Using Neural Networks

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The objective of this research is to develop and experimentally validate robust nonlinear adaptive controllers for unmanned aerial vehicles (UAVs) using neural networks. The approach is to use both pre-trained and on-line neural networks based on multilayer perceptrons. The concept of dynamic nonlinear damping will be applied to modify the adaptation law to increase the robustness in the presence of unmodeled dynamics. A stable weight adjustment rule for the on-line neural network will be derived using a Lyapunov stability theorem. Intellectual MeritThis research, through the use of adaptive controllers, will increase the robustness of UAV operations. The lack of robustness has limited the use of UAVs, despite their potential to replace manned vehicles for many types of missions. Also, as the level of UAV autonomy increases, it is important to design controllers that can work in the entire flight envelope. This research will involve an interdisciplinary team of faculty members from the Engineering and Computer Science Departments at Cal Poly Pomona. An existing fleet of UAVs and complimentary resources will be used. Broader ImpactIncreased UAV robustness will have potential to extend and increase the use of UAVs for rescue missions, border patrol, scientific research, etc., which will have a significant positive impact on society. This research will involve a number of undergraduate and underrepresented students. This research will also be helpful in curriculum improvement. Moreover, this project will help enhance the research capability of the University, a Hispanic Serving and Designated Undergraduate Institution. Research results will be disseminated to the Intelligent Systems community via conference presentations and publication in journals.