对迟滞三明治系统基于Duhem算子的自适应控制

对具有迟滞非线性的三明治系统,设计了基于Duhem算子的神经网络自适应控制器.首先对前端动态子系统进行近似补偿;然后用Duhem算子描述所提出的迟滞状态,用神经网络逼近迟滞状态与迟滞输出的关系,实现对迟滞非线性的建模.基于该迟滞模型并采用伪控制技术设计神经网络自适应控制器,通过Lyapunov方法证明了系统的稳定性,并推导出神经网络的权值自适应调整律和控制律.最后通过仿真验证了该方案的有效性.     

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.     

基于状态全反馈的动态迟滞非线性系统

该文针对不平滑、多映射动态迟滞非线性系统,提出了一种基于神经网络自适应控制方案.在该方案中,通过利用神经网络来逼近模型误差,避免了目前常用逆模型补偿方案中,需求取复杂逆模型的问题.应用Lyapnov稳定定理,证明了整个闭环系统的跟踪误差及神经网络权值将收敛到零点一个有界邻域内.仿真结果表明,所提出的控制方案能够有效补偿迟滞非线性对系统的影响.     

基于输入空间扩张的动态迟滞神经网络模型

针对神经网络不能直接用于辨识具有多值映射特征的迟滞非线性的不足, 利用输入空间扩张的方法, 引入动态迟滞算子来反映动态迟滞的速率依赖性, 由迟滞的输入、输入变化率和算子输出构造神经网络的扩张输入空间, 将输出空间的迟滞多值映射转换为在新的扩张输入空间上的一一映射, 从而将神经网络应用到动态迟滞非线性的辨识中. 所建立模型结构简单, 易于实现在线调整. 最后, 使用该方法对压电陶瓷执行器中的动态迟滞进行了辨识.     

基于状态全反馈的动态迟滞非线性系统自适应控制

该文针对不平滑、多映射动态迟滞非线性系统，提出了一种基于神经网络自适应控制方案。在该方案中，通过利用神经网络来逼近模型误差，避免了目前常用逆模型补偿方案中，需求取复杂逆模型的问题。应用Lyapnov稳定定理，证明了整个闭环系统的跟踪误差及神经网络权值将收敛到零点一个有界邻域内。仿真结果表明，所提出的控制方案能够有效补偿迟滞非线性对系统的影响。     

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。     

行波型超声电机基于神经网络的逆模型辨识

行波型超声电机的动态特性受定子压电陶瓷迟滞和接触层非线性摩擦力的影响,表现出复杂的多值映射特征.通过引入动态迟滞逆算子,将存在于超声波电机逆系统中的多值映射在新的扩张输入空间上,转换为一一映射;然后使用神经网络建立超声波电机的逆模型,对迟滞和非线性摩擦力的影响进行补偿.所建立的模型结构简单,可以在线调整适应电机参数的非线性变化.实验仿真结果验证了该方法的有效性.     

基于神经网络的迟滞逆模型

一个新的基于神经网络的迟滞逆模型被提出.采用连续坐标变换的方法,建立基本迟滞逆算子(EIHO),EIHO为神经网络提供了基本的迟滞逆信息,并与迟滞逆的输入一起作为神经网络的输入,使迟滞逆由多值映射关系转化为一对一映射关系,从而达到用神经网络逼近迟滞逆的目的.一组实测数据被用来检验模型有效性,实验结果表明,这种建模方法是有效的.     

基于退化激励信号的含有迟滞的三明治系统辨识

含有迟滞的三明治系统不仅具有非光滑、多值映射等特性, 而且迟滞环节的输入输出信号还是不能直接测量的, 常规方法难以进行有效的辨识. 本文提出了一种基于退化激励信号的两步辨识方案: 第一步, 设计一个特殊的退化激励信号将迟滞环节退化为一条静态曲线, 从而可以将两端的线性动态环节辨识出来, 解决中间信号不可测的问题; 第二步, 利用已辨识的线性模型重构迟滞环节的输入输出信号, 再采用“扩展输入空间法”建立迟滞环节的神经网络模型. 最后, 在压电超精密运动系统的实验结果表明所提出的建模方法取得了令人满意的结果.     

压电陶瓷执行器的非光滑三明治模型辨识与内模控制

压电陶瓷执行器中含有非光滑、多值映射、频率依赖的非线性迟滞特性,然而在实际应用中,压电器件的输入输出信号无法直接测量,常规方法难以进行有效的辨识和控制.本文采用三明治模型来精确描述实际对象,并提出一种基于退化激励信号的两步辨识法解决三明治迟滞模型的辨识问题.最后,基于已辨识的三明治模型,设计一个内模控制器,解决压电陶瓷执行器的精密轨迹控制问题.实验结果表明所提出的辨识和控制方案取得了令人满意的结果.     

对Preisach类的迟滞非线性神经网络建模

为了消除迟滞非线性对系统的不良影响,本文利用神经网络对Preisach类的迟滞非线性进行建模．通过引入一个特殊的迟滞因子,将多映射的迟滞非线性转换成一一映射,然后建立了基于神经网络的迟滞非线性模型．该模型结构简单,简化了辨识过程,可以调整神经网络权值以适应不同条件下的迟滞辨识．最后．应用该方法对压电执行器中的迟滞非线性建模,并与KP模型进行了比较．     

Adaptive Consensus Quantized Control for a Class of High-Order Nonlinear Multi-Agent Systems With Input Hysteresis and Full State Constraints

For a class of high-order nonlinear multi-agent systems with input hysteresis, an adaptive consensus output-feedback quantized control scheme with full state constraints is investigated. The major properties of the proposed control scheme are: 1) According to the different hysteresis input characteristics of each agent in the multi-agent system, a hysteresis quantization inverse compensator is designed to eliminate the influence of hysteresis characteristics on the system while ensuring that the quantized signal maintains the desired value. 2) A barrier Lyapunov function is introduced for the first time in the hysteretic multi-agent system. By constructing state constraint control strategy for the hysteretic multi-agent system, it ensures that all the states of the system are always maintained within a predetermined range. 3) The designed adaptive consensus output-feedback quantization control scheme allows the hysteretic system to have unknown parameters and unknown disturbance, and ensures that the input signal transmitted between agents is the quantization value, and the introduced quantizer is implemented under the condition that only its sector bound property is required. The stability analysis has proved that all signals of the closed-loop are semi-globally uniformly bounded. The StarSim hardware-in-the-loop simulation certificates the effectiveness of the proposed adaptive quantized control scheme.      

A hybrid model for rate-dependent hysteresis in piezoelectric actuators

A hybrid model is proposed in this paper to describe the static nonlinear and dynamic characteristics of rate-dependent hysteresis in piezoelectric actuators. In this model, a neural network based submodel is implemented to approximate the static nonlinear characteristic of the hysteresis while a submodel with the first-order differential operators is constructed to describe the dynamic behavior of the hysteresis. In this paper, a special hysteretic operator is proposed to extract the hysteretic feature of the hysteresis. Then, an expanded input space with such special hysteretic operator is constructed. Based on the constructed expanded input space, the neural network can be implemented to approximate the hysteresis phenomenon. The submodel to describe the dynamics is a sum of the weighted first-order differential operators. Finally, the experimental results of applying the proposed method to the modeling of hysteresis in a piezoelectric actuator are also presented.     

Observability and Controllability Analysis for Micro‐Positioning Stage Described by Sandwich Model with Hysteresis

In this paper, an approach for analyzing the observability and controllability of micro‐positioning stage with piezoelectric actuator described by sandwich model with hysteresis is proposed. As hysteresis inherent in piezoelectric actuator is a non‐smooth nonlinear function with multi‐valued mapping, the positioning system is also a non‐smooth dynamic system. The Prandtl‐Ishlinksii (PI) submodel is employed to describe the characteristic of hysteresis embedded in the sandwich system. A linearization method based on non‐smooth optimization is proposed to derive a generalized linearized state‐space function to approximate the non‐smooth sandwich systems within a bounded region around the equilibrium points the system works at. Then, both observability and controllability matrices are constructed and the methods to analyze the observability as well as the controllability of sandwich system with hysteresis are derived. Finally, a simulation example and an application of the proposed method to a micro‐positioning stage with piezoactuator are presented to validate the proposed method.     

Modeling hysteresis in piezoelectric actuators using NARMAX models

This paper presents a non-linear moving average model with exogenous inputs (NMAX) and a non-linear auto-regressive moving average model with exogenous inputs (NARMAX) respectively to model static and dynamic hysteresis inherent in piezoelectric actuators. The modeling approach is based on the expanded input space that transforms the multi-valued mapping of hysteresis into a one-to-one mapping. In the expanded input space, a simple hysteretic operator is proposed to be used as one of the coordinates to specify the moving feature of hysteresis. Both the modified Akaike's information criterion (MAIC) and the recursive least squares (RLS) algorithm are employed to estimate the appropriate orders and coefficients of the models. The advantage of the proposed approach is in the systematic design procedure which can on-line update the model parameters so as to accommodate to the change of operation environment compared with the classical Preisach model. Moreover, the obtained model is non-linear in variables but linear in parameters so that it can avoid the problem of sticking in local minima which the neural network based models usually have. The results of the experiments have shown that the proposed models can accurately describe static and dynamic behavior of hysteresis in piezoelectric actuators.