Self-adaption control method and device based on deep learning

A technology of deep learning and control methods, applied in the directions of adaptive control, two-dimensional position/course control, vehicle position/route/altitude control, etc., which can solve the problems of high experience cost and time cost, great difficulty, and difficult optimization of parameters And other issues

Inactive Publication Date: 2017-07-28
李德毅
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  • Application Information

AI Technical Summary

Problems solved by technology

However, different types of vehicles have different dynamic characteristics. In order to ensure safe and reliable autonomous driving control, people often use PID (Proportion Integration Differentiation) control to ensure the stability of vehicle automatic control after the chassis is modified by wire control. and robustness, or through model predictive control (Model PredictiveControl, MPC) to adjust PID parameters, it is difficult, experience cost and time cost are high, parameter variables are difficult to optimize, and it is often a dilemma

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  • Self-adaption control method and device based on deep learning
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Embodiment Construction

[0017] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0018] It should be noted that the "connection" mentioned in this application and the words used to express "connection", such as "connected", "connected", etc., include not only a direct connection between a certain component and another component, but also a certain One part is connected to another part through other parts.

[0019] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0020] The success of the century-old automobile industry is the success of ergonomics. It is the decoupling of the longitudinal and lateral movements of the car. It is the fact that the d...

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Abstract

The invention discloses a self-adaption control method and a device based on deep learning. According to the invention, a deep neural network is utilized to learn to realize the online control of people on a back route through the obtaining of feedback of the body of an experienced driver in various driving conditions and to generate a self-adaption controller. The method and the device are applied to self-driving and are suitable for various types of vehicles. In the invention, the cognitive behaviors of the experienced driver are materialized; the self-driving control is decoupled; and the circular neural network architecture in deep learning is used to make a self-adaption controller of a self-driving vehicle. In the application, the self-adaption controller receives the command of the cognitive arrows formed by the driving brain decision so as to deal with the road, weather and load uncertainties to control the driving of the vehicle and to ensure the self-driving becomes safe, stable and energy conserving.

Description

technical field [0001] The invention belongs to the technical field of unmanned driving, and in particular relates to an adaptive control method and device based on deep learning. Background technique [0002] Unmanned driving technology involves computer science, communication science, cognitive science, vehicle engineering, electrical and electronic engineering, control science and engineering, system science and technology, ergonomics, artificial intelligence and many other disciplines. A self-driving car is a wheeled mobile robot. It is the product of the development of unmanned driving to an advanced stage. It is one of the important symbols to measure a country's scientific research strength and industrial level. [0003] The development of self-driving cars involves both software and hardware. In terms of software, dozens or even hundreds of software modules need to work together to complete tasks such as environment perception, driving cognition, intelligent decisio...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04G05D1/02
CPCG05B13/027G05D1/0221
Inventor 李德毅薛崇郑思仪贾鹏
Owner 李德毅
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