The Research On The 3-DOF Helicopter Model System Based On Adaptive Inverse Control Method

The helicopter system is a typical multi-variable complex nonlinear system, which is difficult to establish precise mathematical model. there are a variety of unknown element in the actual flight. The control system must have strong robustness to achieve good control of the helicopter. In the paper, the 3-DOF helicopter model system see as controlled object to study the adaptive inverse control method.Firstly, the mathematical model of the 3-DOF helicopter is established. The system stability, the controllability and the observability are studied and analyzed.Secondly, the existence of the inverse model of the 3-DOF helicopter, the positive model and the inverse model are researched. The 3-DOF helicopter system which is reversible is proved by the reversible decision theorem. The positive model and the inverse model of the controlled object is established by the neural network.Thirdly, using model reference adaptive inverse based on fuzzy neural network method study the control problems of the 3-DOF helicopter system. The method can identify the helicopter model parameters and can determine the inverse model of controlled object whose parameters are adjusted by fuzzy neural network algorithm to make the bias of the system output and the one input to a minimum. The MATLAB simulation results show that the system control is better in the smaller external noise. However, the control effect of the larger external noise is not very satisfactory.Fourthly, for the problem described above, fuzzy neural network model reference adaptive inverse control method with disturbance eliminator is proposed. The 3-DOF helicopter system dynamic response and the disturbance eliminate is separately controlled.The signal of the same size and the opposite direction with the object disturbance is added to the helicopter input. And the inverse model is added into the feedback loop. Therefore disturbances are eliminated through a positive model and an inverse model. The MATLAB simulation result show that the method has fast convergence and can quickly and accurately eliminate the disturbance, the actual output can quickly track the settings input.