Unmanned aerial vehicle attitude fuzzy adaptive predication control method based on nonlinear model and system thereof

Abstract

The invention discloses an unmanned aerial vehicle attitude fuzzy adaptive predication control method based on nonlinear model and a system thereof. A Cubic-RBF-ARX nonlinear model of the system is established by means of an offline data identification method. Then a fuzzy adaptive prediction controller is designed based on the established nonlinear model. The prediction controller performs online adjustment to the weight coefficient of a target function in the prediction controller according to the real-time state in controlling the attitude of the unmanned aerial vehicle. The fuzzy adaptive prediction controller can ensure a fact that the selected target function accords with a dynamic and stable rule and trend for attitude adjustment in the attitude control process of the unmanned aerial vehicle. Compared with a common unmanned aerial vehicle prediction controller, the whole dynamic and stable process for controlling is considered in setting the parameters of the target function, thereby performing a function of improving an unmanned aerial vehicle attitude control dynamic-and-static response index, and realizing relatively high practical value and good application prospect.

Description

technical field [0001] The invention relates to the technical field of automatic control of aerial robots, and relates to a fuzzy self-adaptive predictive control method and system for attitude of an unmanned aerial vehicle based on a nonlinear model. Background technique [0002] Quadrotor UAV has the advantages of simple structure, high power plant efficiency, and low cost. Its advantages can be embodied in military and civilian applications such as search and rescue, monitoring, and aerial photography. In view of its wide use and value, quadrotor drones are playing an increasingly important role in many fields. UAV flight control refers to the control system that can stabilize the flight attitude of the UAV and control the autonomous or semi-autonomous flight of the UAV. It is the brain of the UAV. Therefore, the quality of the flight control directly determines the quality of the UAV. Performance, and the core of the flight control is the control algorithm that controls...

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Problems solved by technology

But the shortcomings of this method are also obvious, because the optimal objective function of the system is different under different control states, it is difficult to find a suitable set of Q, R 1 , R 2 Ensure that the system can achieve very good control effects in both dynamic and steady-state processes, and maximize the effect of predictive control

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