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基于神经网络的比例伺服阀阀芯液动力补偿鲁棒智能控制

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比例伺服阀广泛应用于智能工程机械、国防装备等高端液压系统中.对于智能比例伺服阀而言,液动力是限制其智能化程度提升最主要的因素.针对上述问题,提出一种基于神经网络的阀芯液动力补偿鲁棒控制器(Flow force compensation neural network-based robust controller,FF-NNRC).首先利用 Fluent 软件,获取在不同阀芯位移、压力边界条件下的液动力信息,用于模拟真实工况下的液动力扰动.设计神经网络学习逼近液动力扰动,从而在模型前馈补偿项构建液动力动态补偿项,针对系统其他扰动及神经网络估计误差设计鲁棒项加以克服.Lyapunov稳定性理论证明提出的控制策略可以实现系统的有界稳定.仿真结果表明,与传统的PID控制器和基于名义值模型补偿的鲁棒控制器(Model compensation robust controller,MC-RC)相比,所提出的控制器具有更高的控制精度和抗干扰能力.
Neural Network-based Robust Intelligent Control of Proportional Servo Valve Center with Flow Force Compensation
Proportional servo valves are widely applied in intelligent engineering machinery,national defence equipment and other high-end hydraulic systems.For the intelligent proportional servo valve,flow force is the most important factor limiting the improvement of its intelligent level.In order to solve the above problems,a flow force compensation neural network-based robust controller(FF-NNRC)of the valve centre is developed.Firstly,Fluent is employed to obtain the flow force information of proportional servo valve under different spool displacements and pressure boundary conditions,which can be used to simulate the flow force disturbance of practical working conditions.Neural network is designed to learn and approximate the flow force disturbance,then handles it in the feedforward model compensation term dynamically,robust term is formulated to deal with other disturbances and neural network estimation error.Lyapunov stability theory proves that the proposed control strategy can achieve the bounded stability of the system.Simulation results show that,compared with traditional PID controller and model compensation robust controller(MC-RC),the proposed controller has higher control accuracy and anti-interference ability.

flow forcecomputational fluid dynamics(CFD)neural networkmodel compensationrobust control

周宁、姚建勇、邓文翔

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南京理工大学机械工程学院 南京 210094

液动力 计算流体动力学 神经网络 模型补偿 鲁棒控制

国家重点研发计划国家自然科学基金国家自然科学基金江苏省研究生科研与实践创新计划

2021YYFB20113005207526252275062KYCX23_0421

2024

机械工程学报
中国机械工程学会

机械工程学报

CSTPCD北大核心
影响因子:1.362
ISSN:0577-6686
年,卷(期):2024.60(4)
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