Model predictive control of an autonomous blimp with input and output constraints

This paper focuses on how to design autonomous flight control systems taking into account input constraints due to actuator saturations and output limitations from the viewpoint of security. Model predictive control (MPC) is one of the most systematic ways to handle such constraints. To implement MPC, we first construct a simple linear model connected to a deadzone nonlinearity based on experimental data. Then, MPC controllers are derived offline as piecewise affine state feedback laws based on a robust MPC approach to take into account additive uncertainties. Indoor experiments are performed to investigate the effectiveness of the MPC controllers obtained based on the simple model.

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