基于预设性能转台伺服系统的参数估计和自适应控制
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摘要
针对含参数未知和非线性摩擦动态的转台伺服系统,提出了一种基于预设性能函数的参数估计和自适应控制方法.利用一种连续的摩擦模型表示转台伺服系统的摩擦动态,引入高阶神经网络对其进行逼近.通过构造一种滤波辅助变量获取参数估计误差信息,并将估计误差信息作为参数自适应律的遗漏因子,保证估计值能够快速收敛到真实值.为了提高转台伺服系统的瞬态响应和稳态性能,利用预设性能函数将原始系统的跟踪误差转换为一个新的误差动态,在此基础上设计自适应控制器,实现对期望轨迹的精确跟踪.仿真结果验证了本文所提算法的有效性.
In this paper, a parameter estimate and adaptive control method was proposed based on prescribed performance function(PPF) for the turntable servo mechanisms with unknown parameters and nonlinearity friction dynamics. A continuous friction model was employed to capture the friction dynamics of the turntable servo mechanism, and high-order neural network(HONN) was adopted to approximate the nonlinearity friction dynamics. An auxiliary filter variable was designed to drive the information of the parameter estimation, which was used as a new leakage term of parameter update law to make the estimation values achieve to true values. To improve the transient behavior and steady-state performance of the turntable servo mechanism, the PPF was adopted to translate the error system of the original system into a new error system. Simulation results validate the effectiveness of the proposed control scheme.
In this paper, a parameter estimate and adaptive control method was proposed based on prescribed performance function(PPF) for the turntable servo mechanisms with unknown parameters and nonlinearity friction dynamics. A continuous friction model was employed to capture the friction dynamics of the turntable servo mechanism, and high-order neural network(HONN) was adopted to approximate the nonlinearity friction dynamics. An auxiliary filter variable was designed to drive the information of the parameter estimation, which was used as a new leakage term of parameter update law to make the estimation values achieve to true values. To improve the transient behavior and steady-state performance of the turntable servo mechanism, the PPF was adopted to translate the error system of the original system into a new error system. Simulation results validate the effectiveness of the proposed control scheme.
引文
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[3]Wang S,Ren X,Na J.Adaptive dynamic surface control based on fuzzy disturbance observer for drive system with elastic coupling[J].Journal of the Franklin Institute,2016,353(8):1899-1919.
[4]Wang S,Ren X,Na J,et al.Extended-state-observer-based funnel control for nonlinear servomechanisms with prescribed tracking performance[J].IEEE Transactions on Automation Science & Engineering,2017,14(1):98-108.
[5]Wang S,Ren X,Na J,et al.Robust tracking and vibration suppression for nonlinear two-inertia system via modified dynamic surface control with error constraint[J].Neurocomputing,2016,203:73-85.
[6]Yu J,Shi P,Dong W,et al.Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors[J].IEEE Transactions on Neural Networks & Learning Systems,2015,26(3):640-645.
[7]王树波,任雪梅.基于区间二型模糊神经网络反演控制抑制二惯量系统的机械振动[J].北京理工大学学报,2016,36(6):611-615. Wang Shubo,Ren Xuemei. Vibration suppression for two-inertia system based on backstepping and interval type-2 fuzzy neural networks[J].Transactions of Beijing Institute of Technology,2016,36(6):611-615.(in Chinese)
[8]Dahl P R.A solid friction model[J].Aerospace Corp El Segundo Ca,1968,12(1):12-20.
[9]De Wit C C,Olsson H,Astrom K J,et al.A new model for control of systems with friction[J].IEEE Transactions on Automatic Control,1995,40(3):419-425.
[10]Yao B,Bu F,Reedy J,et al.Adaptive robust motion control of single-rod hydraulic actuators: theory and experiments[J].IEEE/ASME Transactions on Mechatronics,2002,5(1):79-91.
[11]Na J,Mahyuddin M N,Herrmann G,et al.Robust adaptive finite-time parameter estimation and control for robotic systems[J].International Journal of Robust & Nonlinear Control,2015,25(16):3045-3071.
[12]Adetola V,Guay M,Lehrer D.Adaptive estimation for a class of nonlinearly parameterized dynamical systems[J].IEEE Transactions on Automatic Control,2014,59(10):2818-2824.
[13]Adetola V,Guay M.Advances in parameter estimation and performance improvement in adaptive control[M]//Frontiers in Adaptive Control InTech.[S.l.]:Springer,2009.
[14]Na J,Yang J,Ren X,et al.Robust adaptive estimation of nonlinear system with time-varying parameters[J].International Journal of Adaptive Control & Signal Processing,2015,29(8):1055-1072.