弯曲型导电聚合物驱动器逆模型控制

针对三层弯曲型导电聚合物驱动器,研究了一种无需外部传感反馈装置的逆模型控制方法。通过实验辨识获得驱动器系统传递函数准确,以驱动器系统的4阶传递函数建立的逆模型控制系统结构简单、易于实现。通过补偿驱动器位移漂移特性提高位移控制精度。实验结果表明：其所提出的具有位移漂移补偿的逆模型控制位移输出能够快速有效地跟踪驱动器的实际位移响应,同时精度符合控制要求。     

基于数据驱动的感应电机多模型逆自适应解耦控制

提出一种基于数据驱动的感应电机多模型逆自适应解耦控制方法. 首先, 利用仿射聚类法(AP) 对电机系统的输入输出数据进行聚类, 再基于聚类结果和隶属度函数建立相应的神经网络多模型逆, 以实现解耦控制. 针对电机系统运行过程中电机参数变化问题, 采用粒子群优化算法(PSO) 在线调节子模型权值, 以改善逆模型失匹造成解耦控制性能下降的问题. 仿真实验表明, 所提出的方法能对电机的转速和磁链实现良好的解耦控制, 且对电机系统工况参数变化具有良好的自适应能力.

     

基于CMAC的双足步行机器人逆运动学控制

针对双足步行机器人(Biped Walking Robot)腿部逆运动学模型求解问题,采用一种基于CMAC神经网络的机器人逆运动学控制方法,设计CMAC神经网络控制系统.控制系统采用2个CMAC神经网络控制器分别用来逼近步行机器人支撑腿与摆动腿的逆模型,跟踪通过三维线性倒立摆模型生成的给定腰部轨迹.建立步行机器人正运动学模型来调整CMAC神经网络权值,实现了步行器人腿部逆运动学映射.仿真结果表明,CMAC神经网络控制系统可以在保证机器人位姿良好的情况下跟踪给定的参考轨迹.三维运动学仿真结果进一步验证了控制算法的有效性.     

无轴承异步电机悬浮系统的非线性滤波器自适应逆控制

讨论了基于非线性自适应滤波器的无轴承异步电机(bearlngless induction motor,BIM)悬浮系统自适应逆解耦控制问题.利用非线性自适应滤波器,建立系统模型和逆模型.复制逆模型,将其串联在悬浮系统之前作为逆控制器,并采用改进的最小均方(least mean square,LMS)算法在线调整权值,从而实现转子的悬浮控制.相比于传统的控制方法,此方法不必依靠转矩系统来传递磁链信息,从而避免了各自的控制策略之间的相互制约问题.仿真结果验证了该方法的有效性,完成了系统模型和逆模型的建立,并且能够实现两自由度径向悬浮力之间解耦.     

基于SVM逆系统控制方法的电液变气门系统的升程控制

针对电液可变气门系统的升程控制,提出了基于支持向量机(SVM)的α阶逆系统控制模型.该方法适合高阶非线性系统的控制问题.根据系统的输入输出,离线建立变气门逆系统的辨识模型,然后将SVM逆系统串接在原系统之前,构成伪线性系统.仿真结果表明:基于SVM的α阶逆系统控制模型,对电液变气门系统的升程,表现出了良好的控制特性.     

微位移系统中压电陶瓷驱动器迟滞建模

针对超精密微位移系统中压电陶瓷驱动器的迟滞非线性问题,提出了一种基于遗传反向传播(BP)神经网络的压电陶瓷迟滞非线性建模方法.通过电涡流位移传感器获取压电陶瓷驱动器不同电压值下所对应的位移值;利用六次多项式拟合获得迟滞的数学模型,从而建立基于遗传BP神经网络的迟滞,模型.实验结果显示:该迟滞模型在神经网络测试下的最大误差为0.082 1 μm,平均绝对误差为0.0158 μm.表明,所建的迟滞模型能够较精确地反映出压电陶瓷驱动器的迟滞特性,同时为微位移控制系统设计提供了一定的理论基础.     

机械手的模糊逆模型鲁棒控制

提出一种基于模糊聚类和滑动模控制的模糊逆模型控制方法,并将其应用于动力学 方程未知的机械手轨迹控制.首先,采用C均值聚类算法构造两关节机械手的高木-关野 (T-S)模糊模型,并由此构造模糊系统的逆模型.然后,在提出的模糊逆模型控制结构中, 离散时间滑动模控制和时延控制(TDC)用于补偿模糊建模误差和外扰动,保证系统的全局 稳定性并改进其动态和稳态性能.系统的稳定性和轨迹误差的收敛性可以通过稳定性定理来 证明.最后,以两关节机械手的轨迹跟随控制为例,揭示了该设计方法的控制性能.     

循环神经网络前馈补偿的压电驱动器跟踪控制

压电驱动器固有的迟滞特性,以及其他动态特性严重地影响其跟踪性能。循环神经网络能够准确拟合非线性系统,并且具有记忆存储能力,本文设计了一种循环神经网络对压电驱动器的迟滞特性进行建模,进而得到能够准确模拟输出位移和输入电压之间关系的逆模型,并据此对压电驱动器进行前馈补偿。此外,考虑到建模误差以及其他扰动对驱动器跟踪精度的影响,本文设计了一种单神经元自适应比例-积分-微分控制器,对压电驱动器进行跟踪控制,从而实现对期望信号的准确跟踪。实验结果验证了所建立模型的精度以及控制器的跟踪性能。     

基于宽度学习系统的气动波纹管驱动器无模型跟踪控制

针对一款具有波纹管外形的充气伸长型气动软体驱动器(简称“气动波纹管驱动器”),提出一种基于宽度学习系统的无模型跟踪控制方法,使该驱动器有效跟踪期望轨迹.首先,介绍气动波纹管驱动器结构,以及气动波纹管驱动器整体实验平台工作原理.根据驱动器实时位置信息提出一种基于宽度学习系统的跟踪控制方法,受PID跟踪控制方法中积分项作用的启发,所提出控制方法不仅采用系统跟踪误差作为宽度学习系统的输入之一,还将跟踪误差对时间的积分项作为另一输入以消除期望轨迹与实际轨迹间的恒定偏差.然后,采用宽度学习系统计算得到控制气压,同时,利用基于梯度下降法的学习律在线调整宽度学习系统权值,进而减小驱动器跟踪误差.最后,通过实验验证所提出方法的有效性.所提出方法无需建立驱动器模型,能够简化控制器设计步骤,且与深度神经网络控制方法相比,能在避免计算量过大的前提下实现较高的跟踪控制精度.     

基于模糊树模型的自适应直接逆控制

基于模糊树模型, 结合神经网络中的逆向学习和专门化学习, 提出了自适应直接逆控制方法. 首先离线辨识对象的逆模型作为初始的控制器, 然后与对象串联, 用最小均方差 (Least mean square, LMS) 算法在线调节控制器中的线性参数. 本方法辨识得到的逆模型控制器可以减少需要的模糊规则数目, 同时达到较好的跟踪控制效果. 仿真结果表明了方法的有效性.     

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.     

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.     

Robust tracking control for operator-based uncertain micro-hand actuator with Prandtl–Ishlinskii hysteresis

Since the hysteresis property inherently exists in the rubber material, it is necessary to deal with the control issues for the micro-hand by considering the hysteresis property. Therefore, in this paper, the robust tracking control for the micro-hand systems is discussed from the aspect of the Prandtl–Ishlinskii hysteresis property which is more applicable for the real applications. Firstly, a new model is obtained by combining the dynamic model of the micro-hand with Prandtl–Ishlinskii hysteresis property. Secondly, a new stability condition based on bounded input and bounded output stability is proposed for the Prandtl–Ishlinskii hysteresis modeled micro-hand system from two different cases. Thirdly, by designing the robust controllers based on the internal model control method, the tracking performance can be improved by eliminating the effect from the disturbance. Finally, simulation is used to further demonstrate the effectiveness of the proposed design scheme.     

Modeling and inverse adaptive control of asymmetric hysteresis systems with applications to magnetostrictive actuator

When uncertain systems are actuated by smart material based actuators, the systems exhibit hysteresis nonlinearities and corresponding control is becoming a challenging task, especially with magnetostrictive actuators which are dominated by asymmetric hystereses. The common approach for overcoming the hysteresis effect is inverse compensation combining with robust adaptive control. Focusing on the asymmetric hysteresis phenomenon, an asymmetric shifted Prandtl–Ishlinskii (ASPI) model and its inverse are developed and a corresponding analytical expression for the inverse compensation error is derived. Then, a prescribed adaptive control method is applied to mitigate the compensation error and simultaneously guaranteeing global stability of the closed loop system with a prescribed transient and steady-state performance of the tracking error without knowledge of system parameters. The effectiveness of the proposed control scheme is validated on a magnetostrictive actuated platform.     

Adaptive Stabilization for a Class of Stochastic Nonlinear Systems with Prandtl‐Ishlinskii Hysteresis

This paper deals with a class of stochastic nonlinear systems with unknown hysteresis. A stochastic Lyapunov method is applied for systems in strict‐feedback form driven by unknown Prandtl‐Ishlinskii hysteresis and Wiener noises of unknown covariance. An adaptive controller is obtained which guarantees the global asymptotic stabilization in probability. Simulation results are provided to illustrate the effectiveness of the proposed approach.     

Accounting for hysteresis in repetitive control design: Nanopositioning example

This paper deals with designing a repetitive controller (RC) for tracking periodic reference trajectories for systems that exhibit hysteresis, such as piezoelectric actuators used in nanopositioning systems. Hysteresis can drastically limit the performance of an RC designed around a linear dynamics model, and thus the effect of hysteresis on the closed-loop stability of RC is analyzed and the allowable size of the hysteresis nonlinearity for a stable RC is quantified. But when the hysteresis effect exceeds the maximum bound, an inverse-hysteresis feedforward controller based on the Prandtl–Ishlinskii hysteresis model is used to compensate for the nonlinearity. The control method is implemented on a custom-designed nanopositioning stage. Experimental results show that by incorporating hysteresis compensation the stability margin and the rate of error reduction improve. Likewise, the maximum tracking error reduces by 71%, from 13.7% (using industry-standard integral control) to 3.9% (using RC with hysteresis compensation), underscoring the benefits of RC with hysteresis compensation.     

Disturbance observer–based prescribed adaptive control for rate‐dependent hysteretic systems

In this paper, a disturbance observer–based prescribed adaptive control approach is proposed for ultra‐high‐precision tracking of a class of hysteretic systems with both high‐order matched and mismatched disturbances. Considering the adverse effects of asymmetric and rate‐dependent hysteresis nonlinearities, a polynomial‐based rate‐dependent Prandtl‐Ishlinskii model is first developed to characterize their behaviors, and inverse model based compensation is also constructed. Furthermore, the resulting inverse compensation error is analytically given, and a novel disturbance observer with adaptive control techniques is designed to handle the bounded disturbances, including the inverse compensation error and the high‐order matched and mismatched disturbances. Comparative experiments on a multiaxis nano servo stage are finally conducted to demonstrate the effectiveness of the proposed control architecture, where substantial performance improvement over existing results are achieved on various tracking scenarios.     

Operator-based robust control for nonlinear systems with Prandtl–Ishlinskii hysteresis

This article presents an operator-based robust control method for nonlinear systems with Prandtl–Ishlinskii (PI) hysteresis. On the existence of the hysteresis, the system usually exhibits undesirable oscillations and even instability. While addressing the hysteresis, PI model is adopted to describe it. Especially, the PI model is decomposed into two terms: an invertible part and a disturbance part. In this way, the invertible part could be considered as a part of the nonlinear system. Based on the concept of Lipschitz's operator and the robust right coprime factorisation condition, a robust control design scheme is given to guarantee the bounded input bounded output stability of the obtained system. Further, a tracking operator design method is given to ensure the control system output-tracking performance under the existence of the disturbance part. Numerical simulation results are presented to validate the effectiveness of the proposed method.     

A hysteresis compensation method of piezoelectric actuator: Model,identification and control

A major deficiency of piezoelectric actuators is that their open-loop control accuracy is seriously limited by hysteresis. In this paper, a novel mathematical model is proposed to describe hysteresis precisely. Based on the hysteresis model, an adaptive inverse control approach is presented for reducing hysteresis. The weights of the main hysteresis loop are identified by using least mean square (LMS) algorithm. The realization of an inverse feedforward controller for the linearization of a piezoelectric actuator is formulated. Experiments were performed on a micro-positioning system driven by piezoelectric actuators. The experimental results demonstrate that the positioning precision is noticeably improved in open-loop operation compared to the conventional open-loop control without any compensation.     

Adaptive tracking of stochastic nonlinear systems with Prandtl–Ishlinskii hysteresis

This paper deals with adaptive tracking problems for a class of stochastic nonlinear systems with unknown hysteresis nonlinearities. The system considered is in a strict‐feedback form driven by unknown Prandtl–Ishlinskii hysteresis and Wiener noises of unknown covariance. By employing backstepping design techniques and stochastic Lyapunov design method, parameter adaptive laws and control laws are obtained, which ensure that the tracking error can converge to a small residual set around the origin in the sense of mean quartic value. Copyright © 2011 John Wiley & Sons, Ltd.