万能试验机神经元自适应控制策略的研究与实现

设计并实现了万能试验机的测控系统,建立了试验机系统各组成部分的数学模型.在测量噪声、负载扰动等非线性因素以及试件材料性能不确定性因素的影响下,高精度万能试验机使用常规PID进行控制难以取得满意的效果.提出了积分分离单神经元自适应PID控制算法,实现了负荷、变形、位移的闭环控制.仿真实验表明,使用该算法可以达到快速响应和高精度的控制效果,具有良好的动态性能、自适应能力和较强的鲁棒性.     

单神经元自适应PID控制在电动油门控制中的应用

利用单神经元模型自学习和自适应特点,在传统PID控制基础上设计出了一种单神经元自适应PID控制器,并将其应用于电动油门控制系统中。实验结果表明:采用单神经元自适应PID控制的电动油门系统能够适应油门手柄在较大范围内的推拉运动,而且控制具有较强的鲁棒性,控制品质优于常规PID控制。     

焦炉集气管压力控制的一种新方法

针对焦炉集气管压力系统的特点,提出一种基于神经元的自适应PID控制算法;将神经元与PID结合,在线调整PID参数;对单神经元自适应PID控制器进行了研究,并利用Matlab/Simulink进行了仿真,结果表明单神经元自适应PID控制算法明显优于常规PID控制算法;用本文提出的单神经元自适应PID控制器控制焦炉集气管压力,使集气管压力系统的抗干扰能力得到提高,取得了良好的控制效果。     

免疫单神经元控制器的研究与应用

针对单神经元PID控制学习速度慢、调节时间长的问题，通过对单神经元自适应PID控制器（SNC）的分析，利用免疫机制，实现了神经元控制器比例系数K的免疫功能选取，从而提出了一种具有免疫功能的单神经元自适应PID控制器（ISNC），构成了自适应型免疫单神经元PID控制器。通过对火电厂锅炉过热蒸汽温度控制的仿真研究，证明了该方法的可行性和有效性。     

改进的单神经元自适应PID控制算法在智能车速度控制系统中的研究与应用

针对单神经元控制算法在电磁导航智能车速度控制中存在加权系数修正时间长、自适应能力差、系统不稳定的缺点,提出了将改进的单神经元自适应PID控制算法应用到智能车的调速系统中。改进的单神经元自适应PID控制算法优化了单神经元自适应PID控制算法中的加权系数学习修正部分,使得权系数在线修正不完全根据神经网络的学习原理,而是参考实际经验制定的,最终自适应地整定PID三个参数来实现智能车的速度控制。Matlab仿真测试表明,与单神经元自适应PID控制算法相比,改进的单神经元自适应PID控制算法在智能车速度控制中具有响应快,超调量小、自适应能力强的优点,大大提高了智能车控制系统的性能。     

单神经元自适应PID控制器及其应用

研究了单神经元自适应PID控制器,阐述了该控制器的特点、控制律、适用对象及工程整定方法,在和利时公司的SmartPro系统平台上开发出单神经元自适应PID控制器,进行了单神经元自适应PID控制器的典型一、二阶对象闭环仿真,最终将单神经元自适应PID控制器应用于制药厂发酵罐温度控制回路中。单神经元控制器具有可调参数少、易于整定、控制输出平稳、鲁棒性强的独特优点,适用于大滞后且要求平稳控制输出的工业过程。     

基于自适应单神经元PID的四旋翼飞行控制研究

针对传统增量式PID控制算法在四旋翼飞行器的姿态控制中自整定参数不足的缺点,提出了一种改进的自适应单神经元PID控制算法,该算法在单神经元加权系数调整的基础上引入PSD自适应控制方法,增加了对比例系数的自适应调整;通过建立四旋翼飞行器的动力学模型和飞行试验平台对该改进算法进行仿真验证;仿真结果表明,采用自适应单神经元PID算法的控制器结构简单且响应速度快,精度高,具有更高的鲁棒性和自适应能力,能有效的实现四旋翼飞行器姿态的稳定控制。     

单神经元自适应PID控制器设计方法研究

本文主要介绍了采用有监督Hebb学习算法的单神经元自适应PID控制器以及采用以输出误差平方为性能指标的单神经元自适应PID控制器的控制算法及其仿真实现,总结出了两种基于单神经元的自适应PID控制器的控制特点及其参数设计规律.     

单神经元自适应PID控制器设计方法研究

本文主要介绍了采用有监督Hebb学习算法的单神经元自适应PID控制器以及采用以输出误差平方为性能指标的单神经元自适应PID控制器的控制算法及其仿真实现,总结出了两种基于单神经元的自适应PID控制器的控制特点及其参数设计规律。     

直线电机模糊增量PID控制算法的研究

直线电机在当今的控制设备中应用越来越广泛.深入研究了直线电机原理,建立了电流、位移的双闭环伺服直线电机控制模型.在控制模型的位移控制模块中,采用了模糊增量PID控制算法来实现对位置调节器进行参数自整定、自适应的控制.用Matlab的Simulink工具进行仿真,结果表明,相比传统的PID控制和增量PID控制,在位移控制...     

Adaptive feedforward compensation of force ripples in linear motors

In this paper, an adaptive control scheme is proposed to reduce force ripple effects impeding motion accuracy in Permanent Magnet Linear Motors (PMLMs). The displacement periodicity of the force ripple is first obtained by using a Fast Fourier Transform (FFT) analysis. The control method is based on recursive least squares (RLS) identification of a nonlinear PMLM model which includes a model of the force ripple. Based on this model, the control algorithm can be commissioned which consists of a PID feedback control component, an adaptive feedforward component for compensation of the force ripple and another adaptive feedforward component based on the inverse dominant linear model which will serve to expedite motion tracking response. Simulation and experimental results are presented to verify the effectiveness of the proposed control scheme for high precision motion tracking applications.     

Data-driven nonparametric model adaptive precision control for linear servo systems

Nowadays, high-precision motion controls are needed in modern manufacturing industry. A data-driven nonparametric model adaptive control (NMAC) method is proposed in this paper to control the position of a linear servo system. The controller design requires no information about the structure of linear servo system, and it is based on the estimation and forecasting of the pseudo-partial derivatives (PPD) which are estimated according to the voltage input and position output of the linear motor. The characteristics and operational mechanism of the permanent magnet synchronous linear motor (PMSLM) are introduced, and the proposed nonparametric model control strategy has been compared with the classic proportional-integral-derivative (PID) control algorithm. Several real-time experiments on the motion control system incorporating a permanent magnet synchronous linear motor showed that the nonparametric model adaptive control method improved the system’s response to disturbances and its position-tracking precision, even for a nonlinear system with incompletely known dynamic characteristics.     

模糊PID控制在纳米微动台系统中的应用

针对在纳米级运动控制中,传统PID算法的参数配置在抑制系统运动超调、提高系统定位精度,以及保障系统稳定性等方面存在矛盾的问题,提出了将模糊自适应PID控制器应用于该系统的方案.并基于大量工程整定实验,给出了针对纳米量级控制特点的模糊集设置和模糊整定规则,选取了合理的PID参数论域取值.实验结果表明,所设计的模糊控制器通...     

Adaptive Kalman filter and dynamic recurrent neural networks-based control design of macro-micro manipulator

In this paper,a composite control scheme for macro-micro dual-drive positioning stage with high acceleration and high precision is proposed.The objective of control is to improve the precision by reducing the influence of system vibration and external noise.The positioning stage is composed of voice coil motor(VCM) as macro driver and piezoelectric actuator(PEA) as micro driver.The precision of the macro drive positioning stage is improved by the combined PID control with adaptive Kalman filter(AKF).AKF is used to compensate VCM vibration(as the virtual noise) and the external noise.The control scheme of the micro drive positioning stage is presented as the integrated one with PID and intelligent adaptive inverse control approach to compensate the positioning error caused by macro drive positioning stage.A dynamic recurrent neural networks(DRNN) based inverse control approach is proposed to offset the hysteresis nonlinearity of PEA.Simulations show the positioning precision of macro-micro dual-drive stage is clearly improved via the proposed control scheme.     

Adaptive positioning control of an ultrasonic linear motor system

A 3PRR parallel precision positioning system, driven by three ultrasonic linear motors, was designed for use as the object stage of a scanning electron microscope (SEM). To improve the tracking accuracy of the parallel platform, the positioning control algorithms for the drive joints needed to be studied. The dead-zone phenomenon caused by static friction reduces the trajectory tracking accuracy significantly. Linear control algorithms such as PID (Proportion Integration Differentiation) are unable to compensate effectively for the dead-zone nonlinearity. To address this problem, two types of feedforward compensation control algorithms have been investigated. One is constant feedforward with the integral separation PID; the other is adaptive feedback and feedforward based on the model reference adaptive control (MRAC). Simulations and experiments were conducted using these two control algorithms. The results demonstrated that the constant feedforward with integral separation PID algorithm can compensate for the time-invariant system after identifying the dead-zone depth, while the adaptive feedback and feedforward algorithm is more suitable for the time-varying system. The experimental results show good agreement with the simulation results for these two control algorithms. For the dead-zone nonlinearity caused by the static friction, the adaptive feedback and feedforward algorithm can effectively improve the trajectory tracking accuracy.     

ROBUST ADAPTIVE CONTROL OF PIEZOELECTRIC ACTUATORS WITH AN APPLICATION TO INTRACYTOPLASMIC SPERM INJECTION

This paper describes a robust adaptive control method for positioning piezoelectric actuators (ultrasonic motor) to achieve highly precise motion. The model employed to describe the motor is a second‐order linear model plus a nonlinear part comprising predominantly of a dynamical hysteresis. Based on the model, the overall control algorithm uses a PID component and an adaptive robust component for estimating the parameters of the piezo motor model. The adaptive component is continuously refined based on just prevailing input and output signals. Real‐time experimental results are provided to verify the effectiveness of the proposed scheme when applied to high precision motion trajectory tracking such as Intracytoplasmic Sperm Injection (ICSI).     

柔性支撑Stewart平台自适应交互PID隔振控制

为了实现500 m口径球面射电望远镜(five hundred meter aperture spherical radio telescope, FAST)二级精调稳定平台对馈源舱隔振控制的定位和指向精度, 首先提出了基于并联机构学原理的3维机动目标跟踪预测算法, 对柔性支撑Stewart平台的基座运动进行跟踪预测. 进而,在Stewart平台关节空间设计了自适应交互PID控制器, 引入自适应交互算法解决PID参数的实时调整, 以适应柔性支撑Stewart平台的参数变化对不同控制参数的需求. 采用现代机电系统仿真策略, 对柔性支撑Stewart平台隔振系统的动力学与控制问题进行了仿真, 结果表明: 与传统的PID控制器相比, 自适应交互PID控制器大大改善了隔振效果, 完全满足隔振目标的要求.     

高精度随动跟踪系统的实现

高精度跟踪随动系统分为跟踪运动系统和随动运动系统,其特点是跟踪系统工作模式多样,定位精度高,动态响应频率高,随动系统延时误差小.系统采用新型PCC控制器和直流伺服驱动器,配合直流大型力矩电机实现无级调速驱动,利用双通道高精度旋转变压器完成高精度的定位.跟踪系统和干扰系统之间利用高效的通讯方式尽量缩短通讯延时避免由于通讯...     

LED 晶圆贴片过程中压力的二阶段模糊决策与控制

针对LED 晶圆贴片过程, 设计一种利用阀门的开关和开度控制气缸内气压的压力控制系统, 提出一种多目标分阶段决策和控制的方法, 实现活塞运动和加压控制. 根据活塞的位移和压力, 采用逻辑决策方法, 选择需要控制的阀门. 根据规划压力与实测压力的差值及其差值变化率, 利用模糊自适应PID 控制方法控制阀门的开度. 采用半张量积的方法将逻辑决策和模糊控制中的推理转变成矩阵形式, 以简化运算量, 提高运算速度和控制精确度.

     

共轴双驱型导轨精密定位与同步性的研究

为了满足高精度测量系统对高精密运动导轨的需求,通过结合通用运动自动控制器(UMAC)和直线电机的驱动方式,实现了"PID+速度/;加速度前馈+陷波滤波器"控制算法对共轴双驱型导轨精密定位和同步的精确控制.在实验检测的基础上,验证了导轨的同步性在无摩擦负载和有摩擦负载的情况下,分别可以控制在0.7"和5"以内.