压电陶瓷执行器迟滞的滑模逆补偿控制

为了降低迟滞特性对压电陶瓷执行器的影响,研究了基于Preisach逆补偿的滑模控制策略.首先,利用分类排序方法在控制平台上实现了迟滞的Preisach逆模型;然后,将其串联到压电陶瓷执行器前用于抵消迟滞非线性.考虑到迟滞逆补偿的非完全抵消、模型参数的不确定性以及扰动等问题,设计了一种分段边界层滑模控制律.最后,为了验证...     

压电陶瓷驱动器迟滞建模与控制研究

压电陶瓷驱动器存在迟滞非线性,在超精密定位应用中影响定位精度和系统性能。针对这一问题,提出了一种减小迟滞的自适应逆控制算法。基于Prandtl-Ishlinskii迟滞算子建立压电陶瓷迟滞模型,构建自适应逆控制系统并进行实验研究。结果证明,利用该算法,系统输出的非线性误差从17.7%下降到1.43%,系统性能显著提高。     

压电陶瓷微夹钳迟滞环自适应逆控制研究

针对压电陶瓷的迟滞特性可使微夹钳难以获得良好的位移控制的问题，提出自适应逆控制策略。推导了压电悬臂位移特性的理论模型；采用自适应最小均方算法滤波器建立了压电悬臂的基于Backlash算子的迟滞环正逆模型，并以此为基础建立了微夹钳位移的自适应逆控制系统。样机测试和跟踪实验结果验证了所建立的理论模型和迟滞环模型的正确性，以及控制系统良好的自学习能力和控制效果。     

压电陶瓷执行器的动态模型辨识与控制

为了提高精密定位系统中压电陶瓷的控制精度,研究了压电执行器的动态模型及逆模型。根据Weierstrass第一逼近定理,提出了以多项式函数逼近Duhem模型中的分段连续函数f(·)和g(·),并应用递推最小二乘算法辨识Duhem模型的参数α 及f(·)和g(·)的多项式系数,建立了压电陶瓷执行器的非线性参数化动态模型。利用辨识结果建立压电陶瓷执行器的动态逆模型,避免对压电陶瓷执行器进行复杂的模型求逆;介绍了通过逆补偿和PID复合控制对压电陶瓷系统进行的控制。实验结果表明:仅通过逆补偿,可在0~200 μm使得控制绝对误差小于0.8 μm;在前馈逆补偿和PID环控制下,绝对误差可小于40 nm,结果验证了算法的有效性。该算法结构简单,适应性强,便于工程实现。     

压电陶瓷驱动器的滑模神经网络控制

由于压电陶瓷驱动器的迟滞非线性严重影响其定位精度,本文提出了一种滑模神经网络控制方法来改善它的性能.用径向基函数神经网络的输出作滑模控制的等价控制量,由迟滞补偿器估计控射器参数误差、外部扰动和近似计算所造成的不确定量对神经网络的输出控制量进行补偿,从而使驱动器系统状态保持在滑模平面上.基于Lyapunov稳定性理论推导了控制器和补偿器的自适应调节律,分析了控制系统的收敛性和稳定性,以可变幅值的低频三角波为参考位移量对控制系统进行了实验测试与分析,结果表明,只采用神经网络控制时的平均定位误差为0.43 μm,最大误差为0.77 μm,而采用滑模控制方法对神经网络控制量进行补偿后,平均定位误差减小为0.27 μm,最大误差减小为0.49μm,定位精度有了显著的提高.     

压电工作台的神经网络建模与控制

建立了压电工作台的神经网络在线辨识模型并设计了相应的自适应控制器以抑制压电工作台迟滞特性、蠕变特性及动态特性对其微定位精度的影响.采用双Sigmoid激活函数对神经网络激活函数进行了改进,同时分析了改进激活函数的神经网络模型与PI迟滞模型在迟滞建模上的异同.设计了基于改进激活函数的3层BP神经网络作为压电工作台的在线辨识模型,推导了网络权值、阈值及激活函数阈值修正公式.最后,基于神经网络模型设计了压电工作台的自适应控制方案,该控制方案利用另外一个神经网络来完成对PID控制器参数的自适应调整.实验结果表明:提出的神经网络在线辨识模型平均误差为0.095 μm,最大误差为0.32 μm;自适应控制方案跟踪三角波的平均误差为0.070 μm,最大误差为0.100 μm;跟踪复频波的平均误差为0.80 μm,最大误差为0.105 μm.实验数据显示压电工作台的定位精度得到了有效提高.     

压电陶瓷执行器迟滞补偿及复合控制

快速倾斜镜是星间激光通信终端精瞄系统的核心部件,其驱动装置为压电陶瓷执行器,而压电陶瓷具有迟滞特性,其严重影响了快速倾斜镜的定位精度,进而对星间通信链路的稳定性造成不利影响。为解决这一问题,本文设计了一种改进Prandtl-Ishlinskii(P-I)模型对压电陶瓷执行器进行建模。在此基础上,提出了压电陶瓷执行器前馈线性化方法,以对迟滞特性进行前馈逆补偿。接着,提出了一种结合改进的P-I模型与增量式PID算法的复合控制算法,并在DSP中实现了该复合控制算法。最后,在试验平台上对该算法进行了验证。结果显示:当分别对系统输入10Hz和100Hz减幅正弦、等幅正弦曲线时,模型误差在0.59%以内,在输入同频100Hz以下的减幅正弦曲线时,传统PID算法的最大误差为59.31μrad,而该复合算法的最大误差为14.22μrad。实验数据表明,本文复合控制方法的动态跟踪性能明显优于传统PID方法,改进Prandtl-Ishlinskii(P-I)模型可以精确描述压电陶瓷的迟滞特性。本文设计的复合控制方法满足实际应用对快速倾斜镜的要求。     

具有迟滞和蠕变特性压电作动器的模糊自适应逆控制方法

针对具有迟滞和蠕变特性的压电作动器非线性模型,提出了一种前馈控制和反馈控制相结合的自适应模糊逆控制方案。在前馈控制器中压电作动器的迟滞和蠕变非线性特性的逆模型由自适应模糊逻辑系统近似;在反馈控制器中比例控制器用来调节压电作动器的输出误差。该方法可以实时补偿压电作动器的迟滞和蠕变特性,减少作动器跟踪误差。仿真计算结果表明了该方法的有效性。     

基于Preisach模型的压电陶瓷执行器线性插值法迟滞位移研究

论文介绍了基于Preisach模型的迟滞特性建模方法.通过一系列实验数据建立了Preisach模型,研究了如何应用线性插值法和BP神经网络方法进行任意Preisach函数X（α,β）值的求解,从而计算得到相应电压序列的位移,分析比较了两种方法的预测结果.实验表明,基于Preisach模型的线性插值方法方法可以更好地预测压电陶瓷执行器的迟滞位移.     

压电陶瓷微位移器件控制模型的研究

从不同角度介绍了压电陶瓷微位移器件的两种控制模型.首先,借助于统计物理学分析,结合数学建模方法,建立了一个简单实用的压电陶瓷的迟滞数学模型.其次,借助于弹性体变形理论,介绍了压电/电致伸缩陶瓷的归一化控制模型,从理论上说明了采用电极化强度的方法可以有效减小迟滞的观点.并设计了两种实验系统,对两种控制模型进行了实验验证,实验结果表明,所建立的两种模型可有效减小压电陶瓷的迟滞非线性误差,提高压电陶瓷微位移的控制精度,有助于实现压电陶瓷驱动器的高精度开环微位移控制.     

Distributed adaptive neural networks leader-following formation control for quadrotors with directed switching topologies

The leader-following formation problem is discussed for a team of quadrotors under directed switching topologies. To obtain a more general dynamic model, we describe the quadrotor system in a non-affine pure-feedback form with mismatched unknown nonlinearities. By employing an adaptive neural networks state observer to approximate the unknown nonlinear functions and to reconstruct the immeasurable inner states, we propose a novel distributed output feedback formation control protocol with the backstepping method combining with the dynamic surface control technique. From the Lyapunov stability theorem, all signals in the closed-loop formation system are proven to be cooperatively semiglobally uniformly ultimately bounded for any given bounded initial conditions. Finally, we proved that we verify the performance of the proposed formation control approach by a simulation study.     

Nonlinear model identification and adaptive model predictive control using neural networks

This paper presents two new adaptive model predictive control algorithms, both consisting of an on-line process identification part and a predictive control part. Both parts are executed at each sampling instant. The predictive control part of the first algorithm is the Nonlinear Model Predictive Control strategy and the control part of the second algorithm is the Generalized Predictive Control strategy. In the identification parts of both algorithms the process model is approximated by a series-parallel neural network structure which is trained by a recursive least squares (ARLS) method. The two control algorithms have been applied to: 1) the temperature control of a fluidized bed furnace reactor (FBFR) of a pilot plant and 2) the auto-pilot control of an F-16 aircraft. The training and validation data of the neural network are obtained from the open-loop simulation of the FBFR and the nonlinear F-16 aircraft models. The identification and control simulation results show that the first algorithm outperforms the second one at the expense of extra computation time.     

Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks

This paper describes the development of a neural network (NN) based adaptive flight control system for a high performance aircraft. The main contribution of this work is that the proposed control system is able to compensate the system uncertainties, adapt to the changes in flight conditions, and accommodate the system failures. The underlying study can be considered in two phases. The objective of the first phase is to model the dynamic behavior of a nonlinear F-16 model using NNs. Therefore a NN-based adaptive identification model is developed for three angular rates of the aircraft. An on-line training procedure is developed to adapt the changes in the system dynamics and improve the identification accuracy. In this procedure, a first-in first-out stack is used to store a certain history of the input-output data. The training is performed over the whole data in the stack at every stage. To speed up the convergence rate and enhance the accuracy for achieving the on-line learning, the Levenberg-Marquardt optimization method with a trust region approach is adapted to train the NNs. The objective of the second phase is to develop intelligent flight controllers. A NN-based adaptive PID control scheme that is composed of an emulator NN, an estimator NN, and a discrete time PID controller is developed. The emulator NN is used to calculate the system Jacobian required to train the estimator NN. The estimator NN, which is trained on-line by propagating the output error through the emulator, is used to adjust the PID gains. The NN-based adaptive PID control system is applied to control three angular rates of the nonlinear F-16 model. The body-axis pitch, roll, and yaw rates are fed back via the PID controllers to the elevator, aileron, and rudder actuators, respectively. The resulting control system has learning, adaptation, and fault-tolerant abilities. It avoids the storage and interpolation requirements for the too many controller parameters of a typical flight control system. Performance of the control system is successfully tested by performing several six-degrees-of-freedom nonlinear simulations.     

自适应光学系统中倾斜镜的自适应逆控制

为了克服自适应光学系统中倾斜镜的迟滞响应,提高响应的线性度,改善倾斜镜的控制精度,研究了倾斜镜的迟滞非线性效应。提出了一个基于频率相关的Mutified-Prandtl-Ishlinskii(MPI)模型的补偿方法来在线自适应逆补偿倾斜镜的迟滞非线性。结合反馈PID控制构成了自适应逆前馈复合控制方案,其中自适应逆前馈克服了由于频率等因素引起的迟滞曲线变化,反馈PID则改善了整体的控制性能。建立了倾斜镜二阶系统模型来估计倾斜镜系统的输出,解决了MPI模型参考信号的问题,避免了增加额外前馈传感器,保证了光能量的利用率。实验结果表明,倾斜镜系统15 Hz非线性迟滞率由原来的24.28%降为1.17%,线性度提高了约95%,控制精度较传统PID方法提高了约60%。该方法能够有效补偿倾斜镜的迟滞非线性,提高了自适应光学系统中倾斜镜的校正精度。     

Adaptive nonlinear control using input normalized neural networks

An adaptive feedback linearization technique combined with the neural network is addressed to control uncertain nonlinear systems. The neural network-based adaptive control theory has been widely studied. However, the stability analysis of the closed-loop system with the neural network is rather complicated and difficult to understand, and sometimes unnecessary assumptions are involved. As a result, unnecessary assumptions for stability analysis are avoided by using the neural network with input normalization technique. The ultimate boundedness of the tracking error is simply proved by the Lyapunov stability theory. A new simple update law as an adaptive nonlinear control is derived by the simplification of the input normalized neural network assuming the variation of the uncertain term is sufficiently small.     

Adaptive control of nonlinear system using online error minimum neural networks

In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly.     

基于递归型小波神经网络的感应电动机伺服驱动系统自适应控制

针对感应电动机伺服驱动系统具有的多变、强耦合、慢时变等非线性特性和不确定性扰动,传统的位置速 度PID控制策略不能保证轨迹跟踪的精度和良好的动态品质的问题;保证系统对系统内部参数波动和外界不确定 性扰动具有较好的鲁棒性,在矢量控制策略的基础上,提出了基于递归型小波神经网络的自适应控制方案。神经 网络参数的在线学习机制采用delta自适应律并结合了BP算法和梯度下降法,算法简单,计算量大大减少。仿真 的结果验证了方案的有效性。     

自适应逆控制原理应用于高精度恒流源

将自适应逆控制理论应用于高精度恒流源的设计,系统结构简单,易于实现。实验结果表明,采用自适应逆控制的方法能够使输出电流具有很高的精度和稳定性,可实际应用于仪器仪表、家用电器、传感技术和通信设备等领域。     

Event-triggered decentralized adaptive fault-tolerant control of uncertain interconnected nonlinear systems with actuator failures

This paper investigates the event-triggered decentralized adaptive tracking problem of a class of uncertain interconnected nonlinear systems with unexpected actuator failures. It is assumed that local control signals are transmitted to local actuators with time-varying faults whenever predefined conditions for triggering events are satisfied. Compared with the existing control-input-based event-triggering strategy for adaptive control of uncertain nonlinear systems, the aim of this paper is to propose a tracking-error-based event-triggering strategy in the decentralized adaptive fault-tolerant tracking framework. The proposed approach can relax drastic changes in control inputs caused by actuator faults in the existing triggering strategy. The stability of the proposed event-triggering control system is analyzed in the Lyapunov sense. Finally, simulation comparisons of the proposed and existing approaches are provided to show the effectiveness of the proposed theoretical result in the presence of actuator faults.     

RBFNDOB-based neural network inverse control for non-minimum phase MIMO system with disturbances

An adaptive control strategy combining neural network inverse controller (NNIC) with RBFN disturbance observer (RBFNDOB) is developed for a multi-input-multi-output (MIMO) system with non-minimum phase, internal and external disturbances in this paper. Since the inverse model of system is unstable due to the non-minimum phase, a pseudo-plant is constructed, then the RBFN is used to identify the inverse model of pseudo-plant, which can track the parameter variations of system. By copying the structure and parameters of the identifier, the NNIC is obtained. Cascading the NNIC with the original plant, the MIMO system can be decoupled and linearized into independent SISO systems. For the independent decoupled system, the RBFNDOB employs a RBFN to observe the external disturbances and this estimate value is used as a feed-forward compensation term in controller. The case study on ball mill grinding circuit is presented. The effectiveness of the proposed method is demonstrated by simulation results and comparisons.