类反斜线回滞系统的模型参考滑模控制

回滞现象广泛地存在于许多控制研究领域,可能引起系统的振荡,甚至造成系统的不稳定,因此有必要对回滞系统的有效控制进行研究.针对一类重要的类反斜线回滞系统,通过对类反斜线回滞特性的分析,将其分解为线性部分和已知上限的非线性不确定部分,给定期望的不存在回滞特性的光滑轨线,得到了模型参考滑模控制律,通过跟踪给定期望的不存在回滞特性的光滑轨线,削弱甚至消除回滞特性.仿真结果表明此控制律是有效的.     

基于NBPID的回滞系统控制

回滞现象广泛存在于许多领域，其不可积的非线性特性给控制设计带来了困难。提出的NBPID控制器是基于神经网络逆模型前馈控制(N)，加上改进的Bang—Bang控制(B)的PID控制器。首先通过神经网络逆模型的前馈控制来削弱回滞带来的影响，然后在PID控制器的基础上，为了进一步控制误差，加上改进的Bang—Bang控制。通过设计出的NBPID对回滞系统进行控制，仿真结果表明控制方法是有效的。     

基于非奇异终端滑模的一类回滞系统跟踪控制与仿真

该文研究一类重要的类反斜线回滞系统的鲁棒控制问题。首先通过对类反斜线回滞特性的分析，把回滞系统看作输人具有确定上限的不确定干扰的一般非线性系统；然后利用非奇异终端滑模控制方法设计控制律，使得系统在有限时间内跟踪期望轨迹，并保证系统的跟踪精度，从而削弱甚至消除回滞带来的影响。在数值仿真实验中，解决了非线性系统仿真时状态初始值赋值的问题，仿真结果表明了该方法的有效性。     

基于虚拟仪器的铁电薄膜电滞回线测量系统设计

针对传统Sawyer-Tower电路测量薄膜电滞回线的不足，提出了电荷积分改进方案。并在改进Sawyer-Tower电路基础上,结合虚拟仪器测量思想，组建以Lab VIEW和NI公司数据采集卡为核心的软硬件平台测量系统，并结合Matlab对采集数据进行分析处理，实现薄膜本征电滞回线的精确测量。经实验验证本测试系统可以快速准确完成薄膜电滞回线测量。     

滞环细胞神经网络实现多涡卷混沌发生器

提出利用2个具有滞环输出的CNN细胞实现了多涡卷混沌发生器．首先用标准CNN细胞的状态方程来表示多涡卷混沌发生器的状态方程，通过对比来确定CNN模板的参数，用运算放大器来实现CNN细胞的状态方程及不同形式的滞环输出方程，分别采用不同形式的滞环输出函数实现双涡卷和三涡卷混沌吸引子．本系统由运算放大器，电阻及电容构成，易于实现集成．最后，应用Orcad Pspice进行了电路仿真，验证了结论的正确性．     

环形弹簧阻尼器的滞回性能试验研究

环形弹簧是一种在机械工程领域中应用广泛的减振部件.将环形弹簧用于土木工程结构的减震控制,设计了一种具有自复位功能的环形弹簧阻尼器(Ring spring damper,RD),通过往复荷载作用下的性能试验,研究了RD的滞回性能.首先,提出RD的构造设计和工作原理.进而,设计加工了RD试件,对其进行了滞回性能试验,研究了位移幅值、加载频率对RD试件滞回特性的影响规律.最后,利用ABAQUS软件,对RD试件的滞回性能进行了数值模拟.研究结果表明,RD在往复荷载作用下的滞回性能稳定,且具有良好的耗能与复位特性；RD的有限元模拟滞回曲线与试验曲线较为接近,表明所建立的有限元模型可较为准确地描述RD的滞回行为.     

基于Prandtl-Ishlinskii模型的一类回滞非线性系统自适应控制

对带回滞驱动的一类单输入单输出的非线性不确定系统,本文采用Prandtl-Ishlinskii模型描述回滞特性,采用反步递推设计方法,实现自适应控制器的设计.仿真结果说明控制方法的有效性.     

考虑回滞现象的可变元胞传输模型与仿真设计

交通流测量中的一个典型测量是流量．密度关系图,该关系图可用来展示交通流量和车流密度之间的函数关系。元胞传输模型中的流量．密度服从三角形形式的函数关系,然而实测的流量密度关系往往像是希腊字母入的镜像,这是因为存在回滞现象的原因。文中在基于CTM路段模型的基础上,提出了考虑回滞现象的CTM路段模型,从而弥补了原始模型在描述拥挤路段上的车流传播所引起的较大误差,在一定程度上提高yCTM的真实性和可靠性。仿真结果表明：在车流处于亚稳态区时,自由流到拥挤流相变时的车流密度往往高于拥挤流到自由流相变的车流密度。     

ANSYS二次开发技术及其在土木工程中的应用

提出一种基于ANSYS二次开发功能的数值模拟技术,用来模拟用户自定义的非线性应力应变关系,给出基于ANSYS的用户自定义滞回模型开发方法的操作流程.同时,利用这项技术实现EDR阻尼器和拟线性变摩擦滞回阻尼器的滞回特性,在ANSYS中实现阻尼器单元的负刚度特性,数值分析验证了这项技术的正确性,具有极高的实用性.     

基于形状记忆合金驱动器的微纳定位系统鲁棒自适应控制

针对基于智能材料驱动器串联驱动的微纳定位系统,本文主要探讨了此类高精定位系统的控制设计策略.其控制设计的主要任务是消除驱动器中未知回滞特性对系统性能所造成的负面影响.本文重点以形状记忆合金驱动器为例,采用基于广义play算子的广义Prandtl-Ishlinskii回滞模型来表征形状记忆合金驱动器中的未知饱和回滞非线性,并在此基础上提出了一种鲁棒自适应控制设计方法来消除前置回滞存在的影响.设计的控制器在保证全局稳定性的基础上能实现理想的跟踪精度,仿真结果验证了控制策略的有效性和正确性.     

Robust adaptive control of a class of nonlinear systems including actuator hysteresis with Prandtl-Ishlinskii presentations

This paper deals with robust adaptive control of a class of nonlinear systems preceded by unknown hysteresis nonlinearities. By using a Prandtl-Ishlinskii model with play and stop operators, we attempt to fuse the model of hysteresis with the available control techniques without necessarily constructing a hysteresis inverse. A robust adaptive control scheme is therefore proposed. The global stability of the adaptive system and tracking a desired trajectory to a certain precision are achieved. Simulation results attained for a nonlinear system are presented to illustrate and further validate the effectiveness of the proposed approach.     

Hybrid systems in automotive control

This paper deals with robust adaptive control for a class of perturbed strict-feedback non-linear systems preceded by unknown hysteresis non-linearities. By using the Prandtl-Ishlinskii model with play and stop operators and the properties of this model, a robust adaptive control scheme is proposed to mitigate effects of the preceded hysteresis. The global uniform ultimate boundedness of the closed-loop system is achieved, and the effectiveness of the proposed control approach is demonstrated through simulation examples.     

Neural model-based adaptive control for systems with unknown Preisach-type hysteresis

An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firstly developed to describe the characteristic of hysteresis. This model is motivated by Preisach model but implemented by using neural networks (NN). The main advantage is that it is easily used for controller design. Then, the adaptive controller based on the proposed model is presented for a class of SISO nonlinear systems preceded by unknown hysteresis, which is estimated by the proposed model. The hws for model updating and the control hws for the neural adaptive controller are derived from Lyaptmov stability theorem, therefore the semi - global stability of the closed-loop system is guaranteed. At last, the simulation results are illuswated.     

Zero-error tracking control of uncertain nonlinear systems in the presence of actuator hysteresis

In this paper, the problem of adaptive tracking control is addressed for a class of nonlinear systems with unknown constant parameters and unknown actuator nonlinearity. The actuator nonlinearity is modelled as the backlash-like hysteresis, which is described by a differential model. The prior knowledge on the control gain sign is not required, and only the assumption on the reference signal is made. By combining the adaptive backstepping technique with the Nussbaum gain approach, an adaptive compensation controller design approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are bounded, and the tracking error can converge to zero asymptotically despite the presence of the actuator hysteresis. Two simulation examples are included to illustrate the effectiveness of the proposed approach.     

迟滞非线性动态系统神经网络自适应控制

为了减轻非线性动态系统中未知迟滞(Hysteresis)的不良影响，该文提出了一类Backlash型迟滞模型。将有限数量不同宽度的Backlash(Matlab／Simulink)算子进行叠加，来仿真执行器中的迟滞非线性动态。用此模型，提出了基于径向基函数神经网络的自适应控制方案，以控制伴有未知迟滞的非线性动态系统。该方案采用了动态逆的思想及伪控制的概念。利用Lyapunov稳定理论，设计了两个鲁棒控制项，保证动态系统的稳定性、系统中所有信号有界和误差收敛到起点的领域内。通过Matlab／Simulink仿真实验，证明了所提出方案的有效性。     

未知控制方向的迟滞非线性系统预设自适应控制

针对一类含有迟滞特性的未知控制方向严反馈非线性系统,设计了基于误差变换的反步自适应控制器.首先提出动态迟滞算子来扩展输入空间建立神经网络迟滞模型.然后利用径向基函数(RBF)神经网络逼近未知函数,并引入Nussbaum型函数来解决系统未知控制方向问题.最后采用误差变换将误差限定在预设的范围内,并利用反步法设计自适应控制器.该控制方案不仅能够保证跟踪精度,还可以提高系统暂态和稳态性能.仿真结果表明了控制方案的可行性.     

Pseudo-inverse-based adaptive control for uncertain discrete time systems preceded by hysteresis

This paper discusses the adaptive control for the uncertain discrete time linear systems preceded by hysteresis nonlinearity described by the Prandtl-Ishlinskii (PI) model. The contribution of the paper is the development of an adaptive algorithm in which a pseudo-inversion is introduced to avoid difficulties of the directly inverse construction for complex hysteresis models, especially for the unknown hysteresis case. In the developed approach, only those parameters in the formulation of the sliding mode controller are adaptively estimated. The stability in the sense that all signals in the loop remain bounded is analyzed. Simulation results show the effectiveness of the proposed algorithm.     

Adaptive control of system involving complex hysteretic nonlinearities: a generalised Prandtl–Ishlinskii modelling approach

In this article an adaptive control approach is proposed for a class of nonlinear systems preceded by unknown hysteretic nonlinearities, which is described by a generalised Prandtl–Ishlinskii (P-I) model. The main feature is that the generalised P-I hysteresis model is counted in the controller design without constructing a hysteresis inverse. The developed controller guarantees the global stability of the system and tracking a desired trajectory to a certain precision is achieved. The effectiveness of the proposed control approach is demonstrated through simulation example.