基于Patran和MSC Nastran的压电智能桁架结构振动模态分析

用Patran和MSC Nastran分析压电智能桁架结构振动模态,验证基于有限元法建立的智能桁架结构机电耦合动力学模型的正确性和有效性.结果表明：采用Patran和MSC Nastran针对2种典型压电智能桁架结构开展振动模态分析的结果,与采用基于有限元法建立的数学模型计算得到的模态频率及实验测试模态频率近似相等,验证基于有限元法模型的正确性和有效性,为开展主动振动控制器的设计提供模型和技术支持.     

测控技术在自适应桁架结构振动控制中的应用

自适应桁架中的测控技术涉及传感器、驱动器、信号信息处理单元及通信技术等多项关键技术.本研究针对空间柔性自适应桁架结构制作了实验模型,在其上布置压电传感器和作动器,借助测控系统平台,采用改进的二次积分力反馈控制方法研究了空间柔性自适应桁架结构的振动主动控制问题.实验研究表明该控制方法行之有效.     

减振智能结构自寻最优控制系统开发与实现

从振动生主动控制设计思想出发，基于自适应控制策略，对实现结构振动响应应主动控制的自适应控制系统进行了研究与开发，并采用Ｃ语言编制了数据采集与控制软件；在此基础上，应用自寻最优控制方法以一压电机敏桁架结构进行了振动主动控制实验，取得了良好的抵消振动效果，从而表明这一系纺的有效性与可靠性。     

桁架结构振动的主动模糊控制中主动杆数目与位置优化

研究了采用自适应模糊控制器抑制桁架结构振动时的主动杆数目与位置优化问题.通过定义输入能量相关矩阵优化了主动杆的数目.基于主动杆的控制能量配置准则,给出了主动杆优化配置的模型.研究基于整数编码的遗传算法用于大型离散体中的作动器组合优化问题.最后针对挠性空间智能桁架结构的振动控制仿真,使用基于整数编码的遗传算法(GAs)优化主动杆位置.结果表明对于采用自适应模糊控制律的离散体结构振动控制是行之有效的.     

空间桁架结构的优化配置及振动控制研究

为抑制空间柔性桁架结构的低频振动,采用压电杆件进行优化配置实现桁架结构的振动主动控制;建立了空间桁架结构主动压电杆件的机电耦合模型,利用ANSYS前处理功能编制了压电桁架的机电耦合有限元程序;将可控性度量指标与逐步消减法相结合,实现了空间桁架结构主动杆件的优化配置;对结构进行初始位移扰动、正弦激励以及随机激励,并采用最优模态控制算法进行振动抑制仿真分析,对上述方法进行验证且建立振动控制评价指标进行评价;结果表明将可控性度量指标与逐步消减法相结合的方法可有效抑制空间柔性桁架结构的振动。     

智能结构自适应消振控制系统开发与实现

从振动主动控制设计思想出发，基于自适应控制策略，对实现结构振动响应主动控制的自适应控制系统进行了研究与开发；在此基础上，应用自适应滤波前馈控制方法对一压电机敏刚架结构进行了振动主动控制实验，取得了良好的抵消振动效果，从而表明了这一系统的有效性与可靠性。     

基于神经网络的弹性连杆机构振动主动控制

研究基于神经网络的弹性连杆机构振动主动控制方法.介绍了双隐层动态递归神经 网络的数学模型,利用实验数据离线设计了神经网络辨识器与神经网络控制器.采用基于神 经网络的间接自适应控制策略对弹性连杆机构实施了振动主动控制,机构的动力学品质得到 显著改善.实验结果证明了该方法的有效性.     

基于模糊神经网络的冗余度变几何桁架机器人自适应控制

本文提出了一种基于模糊神经网络（FNN）的机器人位置自适应控制方法．利用模糊 神经网络模型来辨识冗余度变几何桁架机器人的逆动力学模型,用常规反馈控制器完成外部 干扰的补偿和闭环控制．并以四重四面体变几何桁架机器人为例进行仿真计算,表明该控制 方法具有良好的轨迹跟踪精度和抗干扰能力．     

柔性卫星系统的振动自适应边界控制

针对受外部干扰和具有结构参数不确定性的柔性卫星系统,为了抑制其振动和避免控制溢出问题,采用Hamilton变分原理和Euler-Bernoulli梁理论建立了结构无穷维偏微分方程模型,随后基于该无穷维模型设计了带有干扰自适应律的自适应边界控制对柔性卫星振动进行主动控制,并证明了闭环柔性卫星控制系统解的存在性、唯一性和收敛性.最后,仿真结果验证了所设计的自适应边界控制算法的有效性.     

基于蚁群算法的车身振动主动控制研究

针对乘用车车身结构振动抑制问题,采用基于蚁群算法的参数自适应PID控制器,以压电元件为测量和控制元件,进行了振动主动控制仿真和实验研究;首先对白车身结构进行实验模态分析,确定了压电元件的布片位置并确定压电控制的传递关系,然后设计基于蚁群算法的PID参数自适应控制器,制定了控制方案,进行了模拟仿真分析,最后搭建试验平台,以某国产乘用车白车身为被控结构,进行了车身振动主动控制实验;系统仿真和实验结果表明,施加控制时车身的振动幅值较未施加控制时大幅减小,在振动幅值较大的低频区域,其振动幅值明显降低;从而验证了应用基于蚁群算法的参数自适应PID控制技术,不仅可以有效降低车身的振动幅度,而且对传统控制方法控制效果不佳的振动低频区域,控制效果明显。     

A fuzzy neural network controller with adaptive learning rates for nonlinear slider-crank mechanism

A fuzzy neural network (FNN) controller with adaptive learning rates is proposed to control a nonlinear mechanism system in this study. First, the network structure and the on-line learning algorithm of the FNN is described. To guarantee the convergence of the tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the adaptive learning rates of the FNN. Next, a slider-crank mechanism, which is driven by a permanent magnet (PM) synchronous motor, is studied as an example to demonstrate the effectiveness of the proposed control technique; the FNN controller is implemented to control the slider position of the motor-slider-crank nonlinear mechanism. The robust control performance and learning ability of the proposed FNN controller with adaptive learning rates is demonstrated by simulation and experimental results.     

含正反馈振动主动控制系统的混合自适应控制

对于存在结构正反馈的振动主动控制系统,传统的基于有限冲击响应的自适应前馈控制器设计方法难以同时保证控制系统稳定与良好的控制性能.本文在分析正反馈对前馈控制系统影响的基础上,基于无限冲击响应控制器设计模式,提出一种结合前馈自适应控制器和反馈自适应控制器的混合自适应振动主动控制方法.其中前馈自适应控制器采用参考传感器采集到的扰动相关信号作为参考信号,反馈自适应控制器通过构建扰动的估计量作为参考信号,控制器参数更新采用Landau参数递推算法.以一典型的具有固有正反馈性质的机械振动系统为控制对象,给出了该混合自适应控制算法的详细推导过程以及稳定性和收敛性分析过程,得到了算法稳定与收敛的严格正实条件以及相应放松严格正实条件的要求.在此基础上,通过构建实时振动主动控制实验平台,针对多种振动扰动开展对比实验分析.相关实验结果验证了本文提出的混合自适应振动主动控制方法的可行性和有效性.     

Direct adaptive fuzzy-neural control with state observer andsupervisory controller for unknown nonlinear dynamical systems

In this paper, an observer-based direct adaptive fuzzy-neural network (FNN) controller with supervisory mode for a certain class of high order unknown nonlinear dynamical system is presented. The direct adaptive control (DAC) has the advantage of less design effort by not using FNN to model the plant. By using an observer-based output feedback control law and adaptive law, the free parameters of the adaptive FNN controller can be tuned on-line based on the Lyapunov synthesis approach. A supervisory controller is appended into the FNN controller to force the state to be within the constraint set. Therefore, if the FNN controller cannot maintain the stability, the supervisory controller starts working to guarantee stability. On the other hand, if the FNN controller works well, the supervisory controller will be de-activated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Simulation results also show that our initial control effort is much less than those in previous works, while preserving the tracking performance     

Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems

A new hybrid direct/indirect adaptive fuzzy neural network (FNN) controller with a state observer and supervisory controller for a class of uncertain nonlinear dynamic systems is developed in this paper. The hybrid adaptive FNN controller, the free parameters of which can be tuned on-line by an observer-based output feedback control law and adaptive law, is a combination of direct and indirect adaptive FNN controllers. A weighting factor, which can be adjusted by the tradeoff between plant knowledge and control knowledge, is adopted to sum together the control efforts from indirect adaptive FNN controller and direct adaptive FNN controller. Furthermore, a supervisory controller is appended into the FNN controller to force the state to be within the constraint set. Therefore, if the FNN controller cannot maintain the stability, the supervisory controller starts working to guarantee stability. On the other hand, if the FNN controller works well, the supervisory controller will be deactivated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Two nonlinear systems, namely, inverted pendulum system and Chua's (1989) chaotic circuit, are fully illustrated to track sinusoidal signals. The resulting hybrid direct/indirect FNN control systems show better performances, i.e., tracking error and control effort can be made smaller and it is more flexible during the design process.     

Hybrid Adaptive Type‐2 Fuzzy Tracking Control of Chaotic Oscillation Damping of Power Systems

In this paper a novel hybrid direct/indirect adaptive fuzzy neural network (FNN) moving sliding mode tracking controller for chaotic oscillation damping of power systems is developed. The proposed approach is established by providing a tradeoff between the indirect and direct FNN controllers. It is equipped with a novel moving sliding surface (MSS) to enhance the robustness of the controller against the present system uncertainties and unknown disturbances. The major contribution of the paper arises from the new simple tuning idea of the sliding surface slope and intercept of the MSS. This study is novel because the approach adopted tunes the sliding surface slope and intercept of MSS using two simple rules simultaneously. One advantage of the proposed approach is that the restriction of knowing the bounds of uncertainties is also removed due to the adaptive mechanism. Moreover, the stability of the control system is also presented. The proposed controller structure is successfully employed to damp the complicated chaotic oscillations of an interconnected power system, when such oscillations can be made by load perturbation of a power system working on its stability edges. Comparative simulation results are presented, which confirm that the proposed hybrid adaptive type‐2 fuzzy tracking controller shows superior tracking performance.     

薄板系统振动主动控制器的一种设计

根据基于模型的模糊控制策略,提出一种薄板系统振动主动控制器的设计,与传统的方法不同之处在于作者首先采用递归的ＲＡＭＡＸ算法和ＰＥＭ进行模糊参数识别,从而建模;其次,通过在线调速系数矩阵Θ（ａ）的元素和目标函数优化,进行自适应控制器合成,数值仿真和实验结果验证了本文所提方法的有效性和鲁棒性。     

Adaptive fuzzy APSO based inverse tracking-controller with an application to DC motors

This paper introduces the use of the adaptive particle swarm optimization (APSO) for adapting the weights of fuzzy neural networks (FNN) on line. The fuzzy neural network is used for identification of the dynamics of a DC motor with nonlinear load torque. Then the motor speed is controlled using an inverse controller to follow a required speed trajectory. The parameters of the DC motor are assumed unknown as well as the nonlinear load torque characteristics. In the first stage a nonlinear fuzzy neural network (FNN) is used to approximate the motor control voltage as a function of the motor speed samples. In the second stage, the above mentioned approximator is used to calculate the control signal (the motor voltage) as a function of the speed samples and the required reference trajectory. Unlike the conventional back-propagation technique, the adaptation of the weights of the FNN approximator is done on-line using adaptive particle swarm optimization (APSO). The APSO is based on the least squares error minimization with random initial condition and without any off-line pre-training. Simulation results are presented to prove the effectiveness of the proposed control technique in achieving the tracking performance.     

Active vibration control of large space flexible slewing truss using cable actuator with input saturation

This paper proposes a composite approach to implementing attitude tracking and active vibration control of a large space flexible truss system. The system dynamic model is based on Hamilton's principle and discretized using the finite difference method. A nonlinear attitude controller for position tracking is developed based on the input‐output linearization of the discretized system, which can effectively improve system performance compared with a traditional proportional‐differential feedback controller. A taut cable actuator scheme is presented to suppress tip vibration because the mechanical model is a large large‐span spatial structure; furthermore, because the cable has the feature of unilateral input saturation constraint, which can provide only a pulling force, a nonlinear quadratic regulator controller is developed by introducing a piecewise nonquadratic cost function to suppress the vibration of the flexible structure. To investigate the factors that influence the damping effects of the cable, the parametrically excited instability of a cable under 2 supports is analyzed. Simulation results illustrate that the proposed attitude controller can implement the task of position tracking, and the vibration suppression control law is shown to be optimal for functional performance with input saturation.