Abstract
Model Predictive Control (MPC) is a modern technique that, nowadays, encapsulates different optimal control techniques. For the case of non-linear dynamics, many possible variants can be developed which can lead to new control algorithms. In this manuscript a novel generic control system method is presented. This method can be applied to control, in an optimal way, different systems having non-linear dynamics. Particularly, in this paper, the proposed technique is presented in the context of developing a control system for autonomous flight of UAVs. This technique can be used for different types of aerial vehicles having any type of generic non-linear dynamics. The presented method is based on the use of iteratively defined optimal candidate state-space trajectories in global state-space. The method uses a generalized linearization process which, opposite to standard methods, does not need to be predefined in a certain equilibrium state but instead it is performed along any arbitrary state. The technique allows the inclusion of constraints with ease. The presented technique is used as a centralized control system unit that is able to control the full aircraft dynamics without the need of decoupling the system in different reduced modes. The technique is tested by making a Cessna 172 airplane model to perform the following autonomous unmanned maneuvers: climbing at constant speed to a desired altitude, heading change to a desired flight direction, and, coordinate turn.
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Marina H. Murillo received her B.S. degree in Electrical Engineering from Universidad Nacional de Rosario in 2008. She received a CONICET scholarship to start her Ph.D. studies in 2010. Her research interests include control theory and development of navigation & control systems for unmanned aerial vehicles.
Alejandro C. Limache received his Ph.D. degree in Aerospace Engineering from Virginia Polytechnic Institute & State University (Virginia Tech) in 2000. His research interests include real-time simulation, multiphysics based on particle meshless methods, control theory and development of navigation & control systems for unmanned aerial vehicles.
Pablo S. Rojas Fredini received his B.S. degree in Computer Science from Universidad Nacional del Litoral in 2009. He received a CONICET scholarship to start his Ph.D. studies in 2009. His research interests include real time simulation, high performance and parallel algorithms, computer graphics, virtual reality and videogames.
Leonardo L. Giovanini received his Ph.D. degree in Engineering, Computational Mechanics mention from Universidad Nacional del Litoral in 2000. His research interests include modelling and analysis of complex systems, multiagents systems, parameter estimation and fault detection, control and estimation using receding horizons techniques.
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Murillo, M.H., Limache, A.C., Rojas Fredini, P.S. et al. Generalized nonlinear optimal predictive control using iterative state-space trajectories: Applications to autonomous flight of UAVs. Int. J. Control Autom. Syst. 13, 361–370 (2015). https://doi.org/10.1007/s12555-013-0416-y
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DOI: https://doi.org/10.1007/s12555-013-0416-y