Self-adaptive inverse control method for unmanned rotorcraft based on neural networks

A rotor unmanned, self-adaptive inverse technology, applied in the field of unmanned aerial vehicles, can solve the problems of mismatching system models, the controller cannot achieve the control effect, etc., and achieve good robustness, good consistency, and good control effect. Effect

Active Publication Date: 2015-09-02
成都优艾维智能科技有限责任公司
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Problems solved by technology

However, simplifying the dynamic model for controller design will inevitably lead to the problem of system mod

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  • Self-adaptive inverse control method for unmanned rotorcraft based on neural networks
  • Self-adaptive inverse control method for unmanned rotorcraft based on neural networks
  • Self-adaptive inverse control method for unmanned rotorcraft based on neural networks

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Embodiment Construction

[0013] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0014] The system identifier and the inverse system controller in the case of the ideal system model of the rotor UAV are established, so that the system identifier reflects the input-output relationship of the ideal system model, and the inverse system controller reflects the input-output relationship of the ideal inverse system model. When the target signal value is input to such as figure 1 In the control system shown in the figure, due to external disturbances, load changes, etc., the system model output and the target output have errors, and the feedback correction of the system identifier and inverse system controller through the adaptive algorithm has made the control system more sensitive to external disturbances and loads. Variations are robust. In the present invention, the function of the system identifier is to establish a mappin...

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Abstract

The invention discloses a self-adaptive inverse control method for an unmanned rotorcraft based on neural networks. The self-adaptive inverse control method is characterized by controlling in accordance with the following formula as shown in the description, where <Eta> is the learning rate, two items as shown in the description represent values of the p-th nerve cell in a hidden layer and the t-th nerve cell in an output layer after training for the n+1-th time and the n-th time respectively, another two items as shown in the description represent values of the m-th nerve cell in an input layer and the p-th nerve cell in the hidden layer after training for the n+1-th time and the n-th time respectively, and the last two items as shown in the description represent negative gradient directions of a capability function EC with variables Wtp and Vpm. Two neural networks are used for respectively establishing forward and reverse models of an unmanned rotorcraft system model respectively so as to achieve the fully control and identification of the unmanned rotorcraft.

Description

technical field [0001] The invention belongs to the technical field of unmanned aerial vehicles, and relates to an adaptive inverse control method of a rotor unmanned aerial vehicle based on a neural network. Background technique [0002] A rotor UAV is an aircraft that can take off and land vertically, uses several rotors as a power plant, and does not carry an operator. With its flexible control methods such as vertical take-off and landing, arbitrary hovering, and simple structure with low noise, it has gradually become a research hotspot in recent years. Due to the characteristics of strong coupling, underactuation, and strong nonlinearity in the dynamic model of the rotor UAV, it is very difficult to design an effective controller for the rotor UAV, especially when the parameters of the object model change and there are external disturbances. . In the existing technology, the general practice is to simplify the dynamic model of the rotor UAV to reduce the complexity o...

Claims

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Application Information

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IPC IPC(8): G05B13/04
Inventor 张洪斌谢荣强王刚张红雨
Owner 成都优艾维智能科技有限责任公司
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