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Friction-compensation-based adaptive neural network control method and device

A neural network control and friction compensation technology, which is applied in adaptive control, general control system, control/regulation system, etc., can solve the problems that system analysis based on model compensation is difficult to realize and not easy

Active Publication Date: 2019-05-03
中科南京移动通信与计算创新研究院
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Problems solved by technology

However, the friction model is a highly nonlinear and complex model, and it is not easy to establish an accurate friction model, and even if a relatively complete friction model is obtained, it will be difficult to realize system analysis and model-based compensation due to its complex expressions

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  • Friction-compensation-based adaptive neural network control method and device
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  • Friction-compensation-based adaptive neural network control method and device

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Embodiment Construction

[0092] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and 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.

[0093] It should be noted that, if there is no conflict, various features in the embodiments of the present invention may be combined with each other, and all of them are within the protection scope of the present invention. In addition, although the functional modules are divided in the schematic diagram of the device, and the logical order is shown in the flowchart, in some cases, the division of modules in the device or the sequence shown in the flowchart can be performed in different ways. or the steps described.

[0094] Currently, many techniques have been studied to address the effects of f...

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Abstract

The invention discloses a friction-compensation-based adaptive neural network control method and device. The method comprises: establishing a motor position servo system model; designing an adaptive neural network controller based on friction compensation; according to the designed friction-compensation-based adaptive neural network controller, stability identification is carried out on the motorservo system based on a Lyapunov stability theory; and with the Barbalat lemma, a global asymptotic stability result of the system is obtained. According to the invention, modeling of the friction characteristics in the motor servo system is carried out by using a continuous friction model and the friction characteristics and other nonlinear disturbances in the motor servo system are compensated well by combining the self-learning ability of the neural network and the estimation ability of the parameter estimator, so that the system's stable tracking accuracy is improved substantially.

Description

technical field [0001] The invention relates to the field of motor servo system control, in particular to an adaptive neural network control method and device based on friction compensation. Background technique [0002] Friction is a complex phenomenon that exists in the relative motion of all mechanical structures, and it depends on the physical properties of the contact surfaces, relative speed and lubrication conditions, etc. In practical applications, friction not only seriously affects the control accuracy of the servo system, but also causes undesired stick-slip motion or limit cycle oscillation to occur. [0003] Currently, many techniques have been studied to address the effects of friction in motor servo systems. For example, PID control has a simple structure, does not depend on the mathematical model of the system, and has strong engineering practicability, but its control effect on nonlinear friction and strong disturbance systems is not good. With the continu...

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Application Information

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IPC IPC(8): G05B13/04
Inventor 刘雷石晶林胡金龙
Owner 中科南京移动通信与计算创新研究院
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