基于权重双核模型的宽带功放行为建模方法

针对宽带功放行为建模和畸变补偿问题,提出了一种新的权重双盒模型.该模型采用无记忆子模型和记忆子模型权重级联的方法分别对宽带功放的静态非线性和动态非线性进行级联建模.首先,利用无记忆子模型对输入信号幅值进行压缩,降低静态畸变部分模型的拟合误差,并在子模型输出端进行解权值,保证功放的饱和驱动特性;接着,构建权重记忆子模型,利用信号幅值相关的权值函数动态的调整高低功率动态畸变子模型的权重比例.实验结果表明,在不同信号驱动情况下,本文方法在降低计算复杂度的同时,保持了更好的建模精度和畸变补偿能力.     

Buck型变换器的线性化小信号补偿前馈控制

为减小由输入电源扰动引起的输出电压工频纹波,改善DC/DC变换器动态性能,根据平均变量建模思想,为电压型PWM控制的Buck型变换器建立连续导电工作模式（CCM）下统一的平均变量等效电路。分析等效电路并根据前馈控制的不变性原理提出Buck型变换器针对输入电压扰动的线性化小信号补偿前馈控制原理及实现方法,采用该方法的Buck型变换器可快速补偿输入电压扰动,加快变换器在输入电压扰动时的动态调节过程,显著减小输出电压中包括工频在内的低频纹波,改善变换器的动态性能。仿真研究结果验证了文中线性化小信号补偿前馈控制原理、方法及其分析的正确性。     

基于自适应逆的无人直升机指令控制器设计

使用动态逆和在线神经网络相结合的方法设计了无人直升机的指令控制器。该方法用无人机的似线性模型的动态逆使系统反馈线性化，然后用一神经网络在线消除逆误差的影响。由于在线神经网络的自适应作用，消除了无人机未建模动态和不确定性的影响。仿真结果表明所设计的控制器具有较好的性能。     

动态广义主成分分析及其在故障子空间建模中的应用

针对传统故障子空间建模方法未考虑故障数据中同时包含正常工况信息和故障工况信息的实际情况,或未考虑故障数据中的动态因素而导致的对故障子空间提取不够准确的问题,提出了一种动态广义主成分分析方法。通过将带延迟的输入数据进行空间重组,采用广义主成分分析方法提取正常工况和各故障工况之间的动态特征信息,实现对故障子空间的准确建模,并进一步建立故障库实现故障诊断。仿真结果表明,所提方法能够准确提取动态过程的故障子空间,并可用于动态工业过程的故障诊断。     

一种不依赖状态估计的自适应输出反馈控制方法

研究了一种不依赖状态估计的自适应输出反馈控制方法。首先引入伪控制信号使系统反馈线性化，然后设计一个线性动态补偿器和在线神经网络，自适应消除不确定性和建模所引起的误差。对一含有末建模动态的非线性系统的仿真结果表明，该控制方法能够消除系统的稳态误差，提高系统动态性能，并且具有较好的鲁棒性。     

软件可靠性模型中样点可信度

将未确知有理数理论应用于数据建模是常见的建模方法,为了使其更有效地应用于软件可靠性的建模,结合软件失效数据的特点,从样点及样点的可信度两方面,研究未确知理论应用于软件可靠性模型的方法,提出对样点补偿的观点,并给出用补偿后样点计算可信度的计算方法.为未确知理论应用于软件可靠性的建模提供了新思路和新方法,并结合实例说明这一方法的有效性.     

基于扩张状态观测器的直线倒立摆状态反馈控制研究

针对直线倒立摆的控制问题,利用拉格朗日方法对实际物理模型进行线性化处理建立数学模型,为了提高控制精度,将不确定外扰和未建模动态视为一个综合扰动项,运用扩张状态观测器对系统状态和综合扰动项进行观测,设计了基于扩张状态观测器的直线倒立摆状态反馈稳摆控制器;用能量反馈方法和bang-bang控制器实现倒立摆起摆到稳摆的切换。通过无干扰和有干扰状态直线倒立摆起摆到稳摆过程的实验研究,表明系统能够有效的抑制外界干扰,具有较强的鲁棒性。     

基于动态模型库的多模自适应预测控制

针对非线性复杂系统,提出了一种基于动态模型库的多模自适应预测控制方法.采用最小二乘支持向量机对非线性系统进行建模,对此模型进行线性化,并将此线性化模型加入到固定模型库中,对固定模型库进行动态更新;通过一定的选择机制,从固定模型库中选择出与当前时刻系统工作点较接近的部分模型构成一个动态模型库,并基于此动态模型库重构相应的控制器.仿真结果证明了该方法的有效性.     

含特性曲线族器件的规范化分段线性化动态网络的算法

本文改进了L.O.Chua(1977)提出的具有特性曲线族器件的建模方法,得出浮动一维规范化分段线性化模型,它是个较好的模型。文中还详细论述了含有这种器件的规范化分段线性化动态网络的算法。     

基于回声状态网络的功耗曲线特征提取

在模板(Template Attacks,TA)攻击的研究中,如何利用功耗曲线信息,合理选择有效点,增强匹配效果是改进模板攻击的一个重要方向.文中分析了目前有关功耗曲线主要特征提取方法的优缺点,并提出了一种基于回声状态网络(Echo State Network,ESN)的功耗曲线特征提取方法.该方法针对ESN分类方法中的储备池参数选择问题,以时间预测序列精度为标准,采用网格法进行参数空间的优化搜索,并利用神经网络以数据样本形式作为定量知识自行处理的能力,对粗略对齐下的功耗曲线的特征提取能力进行了测试和评估.实验结果表明,基于ESN功耗曲线特征提取方法在功耗曲线数量相同条件下,通过合理选择内核参数,能够降低模板攻击对功耗曲线预处理技术的依赖,提高正确密钥的分类精度.     

基于线性滤波器的四旋翼无人机强化学习控制策略

针对四旋翼无人机(UAVs)系统,该文提出一种基于线性降阶滤波器的深度强化学习(RL)策略,进而设计了一种新型的智能控制方法,有效地提高了旋翼无人机对外界干扰和未建模动态的鲁棒性。首先,基于线性降阶滤波技术,设计了维数更少的滤波器变量作为深度网络的输入,减小了策略的探索空间,提高了策略的探索效率。在此基础上,为了增强策略对稳态误差的感知,该文结合滤波器变量和积分项,设计集总误差作为策略的新输入,提高了旋翼无人机的定位精度。该文的新颖之处在于,首次提出一种基于线性滤波器的深度强化学习策略,有效地消除了未知干扰和未建模动态对四旋翼无人机控制系统的影响,提高了系统的定位精度。对比实验结果表明,该方法能显著地提升旋翼无人机的定位精度和对干扰的鲁棒性。     

Adaptive integral robust control of hydraulic systems with asymptotic tracking

In this paper, an adaptive integral robust controller is developed for high accuracy motion tracking control of a double-rod hydraulic actuator. We take unknown constant parameters including the load and hydraulic parameters, and lumped unmodeled disturbances in inertia load dynamics and pressure dynamics into consideration. A discontinuous projection-based adaptive control law is constructed to handle parametric uncertainties, and an integral of the sign of the extended error based robust feedback term to attenuate unmodeled disturbances. Moreover, the present controller does not require a priori knowledge on the bounds of the lumped disturbances and the gain of the designed robust control law can be tuned itself. The major feature of the proposed full state controller is that it can theoretically guarantee global asymptotic tracking performance with a continuous control input, in the presence of various parametric uncertainties and unmodeled disturbances such as unmodeled dynamics as well as external disturbances via Lyapunov analysis. Comparative experimental results are obtained for motion control of a double-rod hydraulic actuator and verify the high-performance nature of the proposed control strategy.     

Feedback linearization of active magnetic bearings: current-mode implementation

Feedback linearization is a promising approach to the nonlinear control problem posed by active magnetic bearing systems. In this paper, feedback linearization is employed in combination with robust control techniques for the regulation of a single axis test rig actuated by a multiple pole magnetic bearing. To this end, a nonlinear polynomial model of the magnetic actuator was developed based on its experimental calibration. The effect of the amplifier and measurement system dynamics on the feedback linearization performance, was also examined, and compensation filters were developed. Finally, an uncertainty framework was proposed for the linearized plant, and a robust controller was designed via /spl mu/ synthesis. Experimental results demonstrate that the feedback-linearized active magnetic bearing system can achieve stability and the specified performance over the entire range of bearing clearance. The introduction of compensation filters is shown to be essential to this result.     

Landing Posture Control for a Generalized Twin-Body System Using Methods of Input–Output Linearization and Computed Torque

This study aims to examine landing posture control for a generalized twin-body system using methods of input-output linearization and computed torque. The considered system mimics the mechanism of a certain popular cellular phone that is composed of two rigid links and an active joint that not only connects the two links but also offers torques about three orthogonal axes. A standard modeling procedure for robotic dynamics is employed to capture the twin-body dynamics in terms of 312 Euler angles on the basis of the Newton-Euler formulation. Herein, the posture components, i.e., the included angle, the tilt angle, and the first landing body, are employed to represent the landing posture that is particularly designed to prevent the fragile areas of the system from being damaged. Since the three posture components are nonlinear functions of state variables, the standard control strategy cannot be applied. In order to resolve this difficulty, the three posture components are transformed into three control outputs, which are linear functions of state variables. However, the system reveals a nonlinear control problem, and the method of input-output linearization is used to cancel the nonlinear terms. The computed torque method is then utilized to determine the input torques. Finally, the feasibility of the proposed landing posture control is confirmed via MATLAB simulations. This study successfully implemented a standard control strategy to realize landing posture control by establishing transformations from real landing posture components to simulated control outputs, and the controllable regions were defined completely.     

Deep learning-based symbol detection algorithm in IMDD-OOFDM system

In the current research on intensity-modulation and direct-detection optical orthogonal frequency division multiplexing ( IMDD-OOFDM ) system, effective channel compensation is a key factor to improve system performance. In order to improve the efficiency of channel compensation, a deep learning-based symbol detection algorithm is proposed in this paper for IMDD-OOFDM system. Firstly, a high-speed data streams symbol synchronization algorithm based on a training sequence is used to ensure accurate symbol synchronization. Then the traditional channel estimation and channel compensation are replaced by an echo state network (ESN) to restore the transmitted signal. Finally, we collect the data from the system experiment and calculate the signal-to-noise ratio (SNR). The analysis of the SNR optimized by the ESN proves that the ESN-based symbol detection algorithm is effective in compensating nonlinear distortion.     

Design, fabrication, and evaluation of a new haptic device using aparallel mechanism

This paper presents design, fabrication, and evaluation of a new 6-DOF haptic device for interfacing with virtual reality by using a parallel mechanism. The mechanism is composed of three pantograph mechanisms that are driven by ground-fixed servomotors, three spherical joints between the top of the pantograph mechanisms and the connecting bars, and three revolute joints between the connecting bars and a mobile joystick handle. Forward and inverse kinematic analyses are performed and the Jacobian matrix is derived. Performance indexes such as global payload index, global conditioning index, translation and orientation workspaces, and sensitivity are evaluated to find optimal parameters in the design stage. The proposed haptic mechanism has better load capability than those of the pre-existing haptic mechanisms due to the fact that the motors are fixed at the base. It has also a wider orientation workspace mainly due to a RRR-type spherical joint. A control method is presented with gravity compensation and force feedback by a force/torque sensor to compensate for the effects of unmodeled dynamics such as friction and inertia. Also, the dynamic performance is evaluated for force characteristics. Virtual wall simulation with the developed haptic device is demonstrated     

Observer-Based Ripple Force Compensation for Linear Hybrid Stepping Motor Drives

This paper describes our research on a force ripple compensation and closed-loop position control scheme using linear hybrid stepping motors (LHSMs) with significant thrust vibrations. In order to estimate unobservable force ripple components, we propose the Jacobian linearization observer that guarantees the convergence of state estimates into true states. For the precise control of velocity and position, an input-output feedback linearization controller is derived from a nonlinear position-dependent model of the LHSM based on elaborate reluctance network analysis. In addition, we discuss the separation principle used to separate the observer design from the controller design. Common problems associated with the force ripple, such as the positioning error, mechanical stress, and acoustic noise, are efficiently handled using the proposed active damping control scheme. Experimental results show that the positioning accuracy is significantly improved through a closed-loop control while restraining the thrust ripple.     

Real-time implementation of regressor-based sliding mode controlalgorithm for robotic manipulators

A regressor-based variable-structure control scheme has been developed for the trajectory control of robot manipulators in the presence of disturbances, parameter variations, and unmodeled dynamics. The method is based on the regressor structure given by J.J.E. Slotine and W. Li, without parameter adaptation. This avoids the requirement of persistency of excitation, and the convergence of the overall transient exponential. The method is robust against a class of state-dependent uncertainties, which may result, for example, from unmodeled dynamics. The problem of chattering is solved by the smoothing control law. It is with respect to a set around the origin, which can be made arbitrarily small. To illustrate the feasibility of this controller, it was implemented using a Motorola M68000 microprocessor on a two-link revolute joint manipulator subjected to a variable payload. Experimental results confirm the validity of accurate tracking capability and the robust performance