改进的单神经元自适应控制系统在MCGS中的应用

对于现代工业控制中的一些具有时变的、大时滞、非线性的复杂系统,常规的PID算法难以整定PID参数,因而难以达到预期的控制效果. 本文在MCGS组态软件的实验平台上实现常规PID算法和单神经元自适应PID算法分别在线性和非线性的系统的控制. 实验结果表明,在线性系统和非线性系统中单神经元自适应PID控制算法都具有更好的稳定性和动态性能.     

基于规则的滞环非线性补偿控制

本文提出了一种基于规则的滞环非线性补偿控制算法，该算法把传统的线性反馈控制器与一个基于规则的滞环补偿器相结合，具有结构简单，易于实现的特点，文中给出了系统的整定方法及步骤。仿真研究证明了该算法对于滞环非线性系统控制的有效性。     

基于模糊模型和神经网络的多时滞不确定非线性系统的鲁棒H∞控制

针对一类具有多时滞的不确定非线性系统,提出了一种基于模糊模型和神经网络的组 合控制方法.利用具有多时滞的模糊T-S模型对系统进行近似建模并给出基于线性矩阵不等式 (LMI)的模糊H∞控制律.提出完全自适应RBF神经网络控制方法,通过在线自适应调整RBF 神经网络的权重、函数中心和宽度,来对消系统的未知不确定性和模糊建模误差的影响,不要求 系统的不确定项和模糊建模误差满足任何匹配条件或约束,并证明了闭环系统的稳定性.最后, 将所提出的方法应用到一具有多时滞的非线性混沌系统,仿真结果表明了该方法的有效性.     

基于P-模糊自适应PID控制的无刷直流电动机调速系统

分析了无刷直流电动机的数学模型,提出了一种新型的P-模糊自适应PID控制方法,并在Matlab/Simulink环境中建立了基于P-模糊自适应PID控制的无刷直流电动机调速系统仿真模型。在该调速系统中,电流控制采用电流滞环,转速控制采用P控制和模糊自适应PID控制相结合的方式,实现了电流滞环和转速模糊控制的双闭环调速控制功能。仿真结果表明,该系统与基于常规PID控制的调速系统相比,系统响应时间缩短一半,且超调减小,具有较强的鲁棒性和自适应能力。     

基于谐波线性化的滞环电流优化控制

滞环控制的PWM变换器是强非线性环节,难以通过优化控制和常规线性化处理来提高电流控制质量.为此,分析了滞环电流控制原理、谐波线性化方法及系统稳定性,得到了滞环电流控制传递函数模型,时滞和环宽与其他参数的一般关系以及系统自振荡频率的计算方法.根据滞环电流控制的特点,提出了将谐波线性化方法与ITAE优化控制律相结合的方法,以实现对PWM变换器的优化控制.Matlab仿真结果表明了所提出方法的有效性.     

采样非线性系统的动态神经网络稳定自适应控制

研究了一类采样数据非线性系统的动态神经网络稳定自适应控制方法.不同于静态 神经网络自适应控制,动态神经网络自适应控制中神经网络用于逼近整个采样数据非线性系 统,而不是动态系统中的非线性分量.系统的控制律由神经网络系统的动态逆、自适应补偿项 和神经变结构鲁棒控制项组成.神经变结构控制用于保证系统的全局稳定性,并加速动态神 经网络系统的适近速度.证明了动态神经网络自适应控制系统的稳定性,并得到了动态神经 网络系统的学习算法.仿真研究表明,基于动态神经网络的非线性系统稳定自适应控制方法 较基于静态神经网络的自适应方法具有更好的性能.     

一类具有输入非线性的线性离散系统的自适应控制方法

本文研究了一类具有控制死区和不对称非线性系统的自适应控制问题。通过把控制死区非线性描述为一个等价的扰动量,提出了一种自适应控制方法,可有效地克服上述非线性的影响。仿真实验的结果表明,无论在确定性场合还是在随机场合,该算法均具有良好特性。     

一种基于滤波参数在线辨识的鲁棒自适应滤波器

针对一类未建模动态和扰动下的非线性随机系统的状态估计问题,提出了一种基于 滤波参数在线辨识的鲁棒自适应滤波器.该算法通过极小化状态估计误差的方差同时正交化 相邻时刻的滤波残差,在线辨识状态预报误差和滤波残差的协方差,实现了对未建模动态和扰 动的自适应动态补偿,因此对未建模扰动具有很强的鲁棒性.仿真中研究了一个非线性随机时 滞系统,其参数存在缓变和突变,时滞会多次跳变,量测噪声发生了均值漂移和方差突变.算法 对时滞和参数的联合估计效果令人满意.     

PID神经网络在电炉控制系统中的应用

常规PID控制器以其算法简单、可靠性高等优点,在工业生产得到了广泛应用。但是,PID控制器存在控制参数不易在线实时整定、难于对复杂对象进行有效控制等不足。利用神经网络自学习、自适应和非线性映射等特点,将神经网络和PID控制相结合,形成一种PID神经网终挖制系统,可对工业中使用的具有大时滞、慢时变、非线性特点的电炉系统进行有效辨识与控制。     

基于RTW的非线性水箱液位自适应控制系统

以HUATEC A3000高级过程控制实验系统中的一类非线性液位水箱系统作为被控对象进行建模,推导了对该非线性对象进行控制的基于梯度法的模型参考自适应控制算法并仿真.应用基于RTW的快速原型化开发工具,实现了算法验证并且进行了实时控制.证明该算法的有效性,并且具有较好的非线性控制性能,可以随工作点的变化自适应控制参数.     

具有间隙非线性系统的控制加权自适应控制

本文对具有间隙的非线性系统提出一种可控制开环不稳定及非最小相位系统的控制加权自适应算法，并给出了稳定性收敛性定理及其证明。仿真结果表明，该算法可明显地改善系统的控制性能。     

BACKLASH COMPENSATION WITH FILTERED PREDICTION IN DISCRETE TIME NONLINEAR SYSTEMS BY DYNAMIC INVERSION USING NEURAL NETWORKS

A dynamics inversion compensation scheme is designed for control of nonlinear discrete‐time systems with input backlash. This paper extends the dynamic inversion technique to discrete‐time systems by using a filtered prediction, and shows how to use a neural network (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamics preinverse of an invertible discrete time dynamical system. A discrete‐time tuning algorithm is given for the NN weights so that the backlash compensation scheme guarantees bounded tracking and backlash errors, and also bounded parameter estimates. A rigorous proof of stability and performance is given and a simulation example verifies performance. Unlike standard discrete‐time adaptive control techniques, no certainty equivalence (CE) or linear‐in‐the‐parameters (LIP) assumptions are needed.     

死区非线性系统的全局收敛自适应控制

本文通过引入一类新的目标函数，对死区非线性系统建立了全局收敛的自适应控制算法，该算法可用于最小相应和非最小相位系统，并具有渐近最优的控制效果。仿真实验表明本文算法具有良好的动态性能。     

Combined frequency-prediction error identification approach for Wiener systems with backlash and backlash-inverse operators

Wiener systems identification is studied in the presence of possibly infinite-order linear dynamics and memory nonlinear operators of backlash and backlash-inverse types. The latter is laterally bordered with polynomial lines of arbitrary-shape. It turns out that the borders are allowed to be noninvertible and crossing making possible to account, within a unified theoretical framework, for memory and memoryless nonlinearities. Moreover, the prior knowledge of the nonlinearity type, being backlash or backlash-inverse or memoryless, is not required. Using sine excitations, and getting benefit from model plurality, the initial complex identification problem is made equivalent to two tractable (though still nonlinear) prediction-error problems. These are coped with using linear and nonlinear least squares estimators which all are shown to be consistent.     

A nonsmooth IMC method for mechanical systems with backlash

This paper proposes a discrete-time nonsmooth internal model control （NSIMC） approach for mechanical transmission systems described by so-called sandwich system with backlash. In this method, a dynamic compensator is introduced to compensate for the effect of the input linear subsystem. Thus, the sandwich systems with backlash can be simplified as a pseudo-Hammerstein system with backlash. The corresponding NSIMC strategy is designed to control this system. The design procedure of the controller is presented based on the analysis on the robust stability by considering the model errors involved with the effect of backlash as well as the compensated error of the input linear subsystem. Moreover, as the model is switched among the different operating zones, the robust filters are proposed to guarantee the robust stability and satisfactory control performance of the system.     

Adaptive Tracking Control for Nonlinear Systems with a Class of Input Nonlinearities

An adaptive tracking control approach is presented for nonlinear systems with a class of input nonlinearities. A generalized model has been developed for a class of non‐smooth nonlinearities that include dead‐zone, backlash and ‘backlash‐like’ hysteresis. By using the developed model and Nussbaum‐gain technique, the problem of input nonlinearity is solved perfectly. The proposed method is available even when the designer is uncertain about the type of input nonlinearities mentioned above, and the knowledge on the bounds of these nonlinearity parameters is not required. Furthermore, it is proved that all closed‐loop signals are bounded and the tracking error converges to a small residual set asymptotically. Two simulation examples are provided to demonstrate the effectiveness of the proposed method.     

Robust Adaptive Output Control of Uncertain Nonlinear Plants With Unknown Backlash Nonlinearity

In this note, we consider a class of uncertain dynamic nonlinear systems preceded by unknown backlash nonlinearity. The control design is achieved by introducing a smooth inverse function of the backlash and using it in the controller design with backstepping technique. For the design and implementation of the controller, no knowledge is assumed on the unknown system parameters. It is shown that the proposed controller not only can guarantee stability, but also transient performance     

具有间隙非线性系统的自适应控制*

本文对具有间隙非线性系统提出了自适应控制算法,在确定和随机情况下,控制算法都具有大范围渐近收敛性质,仿真结果表明该算法能有效地改善系统的控制性能。     

间隙引发的变载非线性及其闭环辨识

阐明了齿轮传动位置伺服系统间隙除产生熟知的前向迟滞非线性之外,还将因系统往复运动中加卸载相交替的反向调制致使驱动电机动力学参数同步跳变所出现的变载非线性现象.推导建立了这类非线性在一阶谐波意义下的串联式等效频域模型及其近似计算公式,分析了相应的幅相特性.进一步面向服役中系统提出了直接通过闭环弱自激振荡控制所实现的间隙辨识新方法,给出了控制器的结构及其参数整定策略.此外,文中还给出了含高斯白噪声测量的仿真算例,充分验证了闭环间隙辨识方法的有效性和便捷性.