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Generalized Nonlinear Optimal Predictive Control using Iterative State-space Trajectories: Applications to Autonomous Flight of UAVs

Marina H. Murillo*, Alejandro C. Limache, Pablo S. Rojas Fredini, and Leonardo L. Giovanini
International Journal of Control, Automation, and Systems, vol. 13, no. 2, pp.361-370, 2015

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 air-plane 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.

Keyword : "Non-linear model predictive control, optimization, state-space trajectories prediction, unmanned aerial vehicles."

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