Fault accommodation of the two rotor aero-dynamical system using the state space neural networks based model predictive control

This paper deals with the application of state space neural network model to design a model predictive control for a laboratory stand of the Two Rotor Aero-dynamical system. The work describes approach based on the so-called instantaneous linearisation of the already trained nonlinear state space model of the system. With obtained linear model it is possible to derive a vector of future controls based on the minimisation of the cost function within one optimisation window. Repeating procedure in each step of simulation and applying the obtained change of the control signal allows for efficiently control of the nonlinear systems in case of faults. All data used in experiments is obtain from the real-time laboratory stand which is working in Matlab/Simulink RTW environment.