基于迟滞算子的非平滑三明治系统自适应控制

针对一类具有非平滑的迟滞三明治系统, 提出一种基于神经网络的自适应控制方法. 首先利用神经网络做出了前端动态模块的逆系统实现前端动态模块的近似补偿, 这样将迟滞三明治系统转化成一般的迟滞非线性系统. 然后提出一个迟滞算子将迟滞的多映射转化成一一映射, 基于这个迟滞算子设计了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.     

基于神经网络的迟滞非线性补偿控制

提出了一种基于神经网络的迟滞非线性的补偿方法.首先构造一个Duhem逆算子来描述迟滞逆状态.然后利用神经网络来逼近此状态和输出之间的关系来得到神经网络迟滞逆模型,神经网络权值采用反馈误差学习方法来进行在线调整.系统的前馈控制器和反馈控制器分别为逆模型和PID控制器.该方法不需要建立迟滞的正模型,能够在线构造逆模型来实现迟滞补偿.最后通过仿真验证了该方法的有效性.     

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Duhem前馈逆补偿的压电陶瓷迟滞非线性 自适应滑模控制

针对压电陶瓷的动态迟滞非线性,研究了基于Duhem逆模型前馈补偿的滑模自适应控制策略。首先,利用多项式逼近Duhem模型中的未知分段函数ｆ(.)和ｇ(.),采用递推最小二乘法进行系统辨识,并求取逆模型,将其作为前馈控制器,考虑压电陶瓷迟滞非线性随输入信号频率变化,且难以完全抵消,模型参数存在不确定性等问题,设计一种自适应滑模控制律。利用Lyapunov稳定性定理及仿真实验证明了该控制律可以使系统全局渐进稳定。最后,进行了压电陶瓷迟滞补偿实验和位移跟踪实验。实验结果表明,前馈逆补偿控制下的压电陶瓷位移迟滞量减小了96.1％,与直接控制相比,前馈逆补偿控制下位移跟踪的最大绝对误差减小了27.0％,平均绝对值误差减小了17.9％,具有更好的跟踪精度和动态性能。     

基于RBF神经网络的一类不确定非线性系统自适应H∞控制

基于RBF神经网络提出了一种H∞自适应控制方法.控制器由等效控制器和H∞控制器两部分组成.用RBF神经网络逼近非线性函数,并把逼近误差引入到网络权值的自适应律中用以改善系统的动态性能.H∞控制器用于减弱外部干扰及神经网络的逼近误差对跟踪的影响.所设计的控制器不仅保证了闭环系统的稳定性,而且使外部干扰及神经网络的逼近误差对跟踪的影响减小到给定的性能指标.最后给出的算例验证了该方法的有效性.     

具有零动态的SISO仿射非线性系统的神经网络自适应跟踪控制

针对具有零动态的SISO仿射非线性系统提出了一种神经网络直接自适应跟踪控制方法.采用梯度下降算法最小化未知理想控制器与神经网络控制器的误差代价函数以获得参数自适应律,控制器中无需另加鲁棒控制项.基于Lyapunov稳定性定理证明了在该控制器的作用下能保证输出跟踪误差及相应闭环系统的所有状态最终一致有界及神经网络参数的收敛性.仿真结果验证了该文方法的有效性.     

具有H_∞性能的机械手神经自适应跟踪控制

针对非线性不确定机械手系统的轨迹跟踪控制问题,提出一种具有H∞性能的神经自适应控制算法;该算法为机械手系统分别设计了主控制器和监督控制器;主控制器由神经网络控制为基础,基于滑模控制原理得到神经网络权值的自适应律;基于李亚普诺夫稳定性理论和鲁棒控制设计的监督控制器,补偿自适应神经网络对系统不确定项学习的误差,同时使系统具有H∞性能;通过二自由度机械手模型进行仿真实验,仿真结果验证了方法的有效性。     

基于RBF神经网络的一类不确定非线性系统自适应H控制

基于RBF神经网络提出了一种H_∞ 自适应控制方法.控制器由等效控制器和H_∞ 控制器两部分组成.用RBF神经网络逼近非线性函数,并把逼近误差引入到网络权值的自适应律中用以改善系统的动态性能.H_∞ 控制器用于减弱外部干扰及神经网络的逼近误差对跟踪的影响.所设计的控制器不仅保证了闭环系统的稳定性,而且使外部干扰及神经网络的逼近误差对跟踪的影响减小到给定的性能指标.最后给出的算例验证了该方法的有效性.     

严格反馈非线性系统的自适应神经网络输出反馈控制

基于非线性反馈函数,文章设计神经网络状态观测器,解决一类非线性系统的输出反馈控制问题.非线性反馈神经网络观测器在系统存在不确定性函数的情况下实时估计系统状态.利用所获得的状态信号,设计了自适应神经网络动态面控制器,同时保证了闭环系统的稳定性和所有信号的有界性.通过调节设计参数的取值能够达到期望的闭环跟踪性能.数值仿真表明,所设计的状态观测器不需要对原系统做状态变换,能够克服输出反馈滑模控制器带来的抖震问题.     

基于数据融合的自适应神经网络模糊控制系统设计

基于数据融合的思想，提出一种非线性系统的自适应神经网络模糊控制器的设计方法。该方法利用数据融合技术降低了模糊控制器的输入维数，简化了模糊控制器的设计。用自适应神经模糊推理系统的神经网络自学习功能完成模糊控制器的设计。仿真结果表明，自适应神经网络模糊控制系统性能优于采用普通的模糊控制器的情况，为数据融合与智能系统技术在非线性系统中的应用作了有益的探索.     

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.     

Absolute stability analysis of linear systems with Duhem hysteresis operator

In this paper, we investigate the stability of positive and negative feedback interconnections of a linear system and a Duhem hysteresis operator. We provide sufficient conditions on the linear plant and on the Duhem operator which are based on the counterclockwise (CCW) or clockwise (CW) input–output property of the plant and hysteresis operator. We show the application of our main result in the design of a linear controller to stabilize a simple mechanical system driven by a hysteretic actuator, such as, piezo-actuator or smart material-based actuator.     

MLP-based adaptive neural control of nonlinear time-delay systems with the unknown hysteresis

In this note, the authors study the tracking problem for uncertain nonlinear time-delay systems with unknown non-smooth hysteresis described by the generalised Prandtl–Ishlinskii (P-I) model. A minimal learning parameters (MLP)-based adaptive neural algorithm is developed by fusion of the Lyapunov–Krasovskii functional, dynamic surface control technique and MLP approach without constructing a hysteresis inverse. Unlike the existing results, the main innovation can be summarised as that the proposed algorithm requires less knowledge of the plant and independent of the P-I hysteresis operator, i.e. the hysteresis effect is unknown for the control design. Thus, the outstanding advantage of the corresponding scheme is that the control law is with a concise form and easy to implement in practice due to less computational burden. The proposed controller guarantees that the tracking error converges to a small neighbourhood of zero and all states of the closed-loop system are stabilised. A simulation example demonstrates the effectiveness of the proposed scheme.     

基于状态观测器的迟滞非线性系统输出反馈控制

利用Preisach模型与其边界线之间的映射关系建立了容易在线更新的迟滞模型．将模型和径向基网络相结合，针对一类动态多映射迟滞非线性系统设计了输出反馈控制器．应用LyaPunov定理得到系统控制律和神经网络权值更新律，从而保证了闭环系统的跟踪误差及网络权值偏差收敛到原点的某个有界邻域内．     

Adaptive output feedback control of systems preceded by the Preisach-type hysteresis.

An adaptive output feedback controller is presented for a class of single-input-single-output (SISO) nonlinear systems preceded by an unknown hysteresis nonlinearity represented by the Preisach model. First, a novel model is developed to represent the hysteresis characteristic in order to handle the case where the hysteresis output is not directly measured. The model is motivated by the Preisach model but implemented by the neural networks (NN). Therefore, it is easily used for controller design. Then, a radius-basis-functional-neural-networks (RBF NN) adaptive controller based on the model estimation is presented by combining the high-gain state observer. The updated laws and control laws of the controller are derived from Lyapunov stability theorem, so that the ultimate boundedness of the closed-loop system is guaranteed. At last, an example is used to verify the effectiveness of the controller.     

Adaptive wavelet neural network control with hysteresis estimation for piezo-positioning mechanism

An adaptive wavelet neural network (AWNN) control with hysteresis estimation is proposed in this study to improve the control performance of a piezo-positioning mechanism, which is always severely deteriorated due to hysteresis effect. First, the control system configuration of the piezo-positioning mechanism is introduced. Then, a new hysteretic model by integrating a modified hysteresis friction force function is proposed to represent the dynamics of the overall piezo-positioning mechanism. According to this developed dynamics, an AWNN controller with hysteresis estimation is proposed. In the proposed AWNN controller, a wavelet neural network (WNN) with accurate approximation capability is employed to approximate the part of the unknown function in the proposed dynamics of the piezo-positioning mechanism, and a robust compensator is proposed to confront the lumped uncertainty that comprises the inevitable approximation errors due to finite number of wavelet basis functions and disturbances, optimal parameter vectors, and higher order terms in Taylor series. Moreover, adaptive learning algorithms for the online learning of the parameters of the WNN are derived based on the Lyapunov stability theorem. Finally, the command tracking performance and the robustness to external load disturbance of the proposed AWNN control system are illustrated by some experimental results.     

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.     

非完整移动机器人的神经网络鲁棒自适应控制

在非完整移动机器人轨迹跟踪问题中,针对机器人运动学与动力学模型的参数和非参数不确定性,提出了一种混合神经网络鲁棒自适应轨迹跟踪控制器,该控制器由运动学控制器和动力学控制器两部分组成;其中,采用了参数自适应的径向基神经网络对运动学模型的未知部分进行了建模,并采用权值在线调整的单层神经网络和自适应鲁棒控制项构成了动力学控制器;基于Lyapunov方法的设计过程保证了系统的稳定性和收敛性,仿真结果证明了算法的有效性。