首页|基于滑模观测器与径向基网络的电液位置伺服系统反步控制

基于滑模观测器与径向基网络的电液位置伺服系统反步控制

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针对传统电液伺服系统中存在的未知扰动、非线性动态以及参数不确定性等共性问题,提出了一种基于滑模观测器和径向基函数(RBF)神经网络的反步控制(BC)方法.首先,构建了阀控缸系统三阶严反馈状态空间模型,并采用具有有限时间收敛特性的滑模观测器,估计系统的未知速度状态和非匹配项集总扰动;然后,基于系统的严反馈模型和指令滤波技术,设计了反步控制器,利用径向基神经网络(RBFNN)补偿系统速度/扰动的估计误差、滤波偏差以及系统的匹配项扰动,并根据Lyapunov稳定性定理证明了闭环系统跟踪误差的渐进有界收敛;最后,搭建了阀控缸电液控制系统实验平台,通过仿真和实验验证了该控制方法对多频率参考信号的跟踪性能.研究结果表明:该控制方法在传统PI控制器的基础上进一步改善了电液伺服系统的轨迹跟踪精度,实验跟踪误差最大值Me、跟踪误差平均值μ和跟踪误差均方值σ分别降低了66.7%、80%、83.3%;该滑模干扰观测器和RBF补偿器能够有效降低不确定性扰动的影响.
Sliding mode observer and RBF based backstepping control for electro-hydraulic position servo system
Aiming at the common problems of unknown disturbance,nonlinear dynamics and parametric uncertainty in the electro-hydraulic servo system,a backstepping control(BC)method based on sliding mode observer and radial basis function(RBF)neural network was proposed.Firstly,a three-order strict feedback state space model of the valve-controlled cylinder system was constructed,and the sliding mode observer with finite time convergence characteristics was used to estimate the unknown speed state of the closed-loop system and the uncertain lumped disturbance of mismatched items.Then,the backstepping controller was designed based on the strict feedback model of the valve-controlled cylinder system and command filtered technology.The classic recursive backstepping methodology was utilized to design the nonlinear tracking controller according to the strictly feedback state-space mathematical model.The radial basis function neural network(RBFNN)was used to compensate the estimation error of the system speed and disturbance states,the filtering deviation and the matching term disturbance of the system,etc.According to Lyapunov stability theorem,the asymptotic bounded convergence of the tracking error for the closed-loop system was proved.Finally,valve-controlled cylinder electro-hydraulic control system test platform was established and the tracking performance of the control method on multi frequency reference signals was verified by comparative simulation and experiment.The research results demonstrate that the control method can further improve the trajectory tracking accuracy and robustness.Maximum experimental tracking error Me,average tracking error μ and mean square value of tracking error σ are respectively decreased by 66.7%,80%,and83.3%.Therefore,the proposed sliding mode disturbance observer and RBF compensator can effectively reduce the impact of various disturbances.

electro-hydraulic servo systemposition tracking performanceradial basis function(RBF)radial basis function neural network(RBFNN)backstepping control(BC)sliding mode observerLyapunov

卢彬、陈明和

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南京机电职业技术学院 机械工程系,江苏 南京 211300

南京航空航天大学 机电学院,江苏 南京 210016

电液伺服系统 位置跟踪性能 径向基函数 径向基神经网络 反步控制 滑模观测器 Lyapunov

江苏省高职院校教师专业带头人高端研修项目南京市教学创新团队建设项目(十四五)

2021GRFX0390201202

2024

10.3969/j.issn.1001-4551.2024.03.011

机电工程
浙江大学　浙江省机电集团有限公司

机电工程

CSTPCD北大核心
影响因子：0.785
ISSN：1001-4551
年,卷(期)：2024.41(3)
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