地震激励下参数不确定结构重叠分散保性能控制

对参数不确定结构振动控制方法进行了研究。基于包含原理,采用重叠分散控制策略与保性能控制算法相结合的方法,将高层建筑结构划分成一组子结构,对每一个子结构采用保性能控制方法设计控制器,提出了参数不确定结构重叠分散保性能控制方法,较好地解决了地震激励下参数不确定建筑结构的振动控制问题。以20层Benchmark结构模型为研究对象,采用重叠分散保性能控制策略和集中保性能控制策略进行数值模拟。结果表明,对于结构系统建模存在误差或系统本身存在不确定性的情况,本文提出的重叠分散保性能控制方法均能有效降低结构的地震响应,且能够保证控制系统的可靠性和稳定性。     

参数不确定结构动态输出反馈重叠分散保性能控制方法

针对参数不确定建筑结构在地震激励下控制方法,采用重叠分散控制策略,提出了动态输出反馈的重叠分散保性能控制算法。基于包含原理,一个大尺度结构分为一组具有共同部分的成对子系统,使用保性能算法和线性矩阵不等式设计了子系统的控制器,然后利用收缩原理,形成原系统的控制器。利用提出的动态输出反馈重叠分散保性能控制方法,对20层Benchmark结构模型进行数值模拟分析,并与采用重叠分散LQG、状态反馈重叠分散、输出反馈集中保性能控制方法进行了分析对比。结果表明,提出控制算法能够降低地震激励下结构动力响应且保证了控制系统在结构参数摄动下的鲁棒性。     

地震激励下参数不确定的相邻建筑结构重叠分散保性能混合控制方法

研究阐述了具有不确定结构参数的两个相邻建筑物在地震激励下的混合控制方法。利用包含原理和具有保证性能控制算法的重叠分散控制策略,提出了一种不确定参数结构系统的重叠分散保性能混合控制算法。基于包含原理,将一个大尺度结构分为一组具有共同部分的成对子结构,其共同部分即重叠的部分。使用保性能算法设计每个成对子结构的控制器,然后利用收缩原理,形成原系统的控制器。将重叠分散保性能混合控制算法应用于具有不确定参数的12层和9层两个相邻建筑物的振动控制问题,并与集中的保性能振动控制方法进行了比较,两种方法下结构层间位移的最大控制率分别为50%~60%和60%~70%。数值结果表明:对于具有不确定结构参数的两个相邻建筑物,该方法可以有效地抑制结构的动力响应,确保控制系统稳定性,提高了控制器设计的灵活性和可靠性。     

地震作用下高层建筑重叠分散控制子结构划分机理研究

针对地震作用下高层建筑振动分散控制问题,引入信息共享的重叠分散策略,研究高层建筑振动重叠分散控制子结构划分机理。基于线型二次型(LQR)最优控制的最优权矩阵和H∞鲁棒控制的最优输出评价矩阵,分析评价高层建筑重叠分散控制子结构不同划分策略时的控制效果。对某20层Benchmark结构模型进行数值模拟与分析,结果表明,本文提出的两种重叠分散控制方法的性能评价方法,可指导任意层数高层建筑振动重叠分散控制子结构的合理划分,既保证控制系统良好的控制效果,又保证控制力在合理的范围内。     

高层建筑结构的离散时间重叠分散H_∞控制方法

采用分散控制策略可有效解决多自由度的建筑结构振动控制问题。本文对地震作用下的高层建筑结构的振动控制方法进行研究,将重叠分散控制策略、离散时间H∞控制算法、线性矩阵不等式(LMI)方法和静态输出反馈控制方法相结合,提出了离散时间重叠分散H∞控制方法。基于包含原理和分解原理,将整个建筑结构控制系统划分为一系列子系统,对各个子系统采用离散时间重叠分散H∞控制方法进行控制。采用驱动器饱和控制策略,对20层抗震钢结构Benchmark模型进行数值模拟与计算。结果表明:全状态集中控制(理想驱动控制)、全状态集中控制(驱动器饱和控制策略)、部分输出反馈控制(驱动器饱和控制策略)、重叠静态输出反馈控制(驱动器饱和控制策略)、多重叠静态输出反馈控制(驱动器饱和控制策略)的平均控制率分别达到了63.4%、53.4%、50.7%、48.2%、32.6%,采用这些控制方法都获得了较好的控制效果;重叠分散控制策略降低了计算成本并且增加了控制器设计的灵活性。     

大型柔性构件振动控制的特征结构配置

根据大型柔性构件存在着建立运动方程自由度多,整体结构参数不宜测量等特点,文中研究了基于子结构的振动特征结构配置．为克服以往在拼装子结构时由于界面耦合作用,造成振动能量相互传递,破坏原有各子控制器设计性能的缺陷,作者提出了通过在界面上安装控制执行器,以割断子结构间能量传递的分散解耦控制法,保证了子控制器的设计特性在装配后不会改变     

建筑结构多级分散稳定控制

建立大尺度建筑结构线性时不变分散控制模型。采用局部控制器和全局控制器的多级控制方法使闭环大系统分散稳定,并用H∞控制方法得到了建筑结构各独立子结构的局部控制器。通过构造特定的正定矩阵,将各独立子结构控制系统集结为整体结构的Lypunov函数;根据全局稳定条件,得到了满足建筑结构整体稳定性条件的全局控制器。进一步将全局控制器和局部控制器统一为一组线性矩阵不等式的优化设计,分析了由全局状态和部分状态设计全局控制器的方法。以20层钢结构基准模型在地震激励下的控制为例,验证了该多级分散控制的有效性。研究表明:该多级分散控制方法可以对大尺度建筑结构的位移、加速度等地震响应进行有效控制;采用部分结构状态设计的全局控制器可以协调子结构之间的振动影响,实现整体结构的稳定。     

基于输出的建筑结构分散滑模控制

大尺度结构分解为多个低阶子结构,子结构间的相互作用视为作用于子结构的有界扰动。本文提出了一种基于部分位移、速度输出反馈的建筑结构分散控制方法。分析了输出坐标下子结构稳定滑模的存在条件,得到输出坐标下子结构稳定滑模面方程。建立了基于输出的稳定分散控制格式,其中控制力非线性部分保证结构全局状态收敛至设计的滑模面,线性部分使建筑结构闭环子结构系统渐进稳定。以一20层钢结构基准模型在地震激励下的控制为例,验证了该分散控制的有效性。研究表明,使用部分位移、速度输出信号可以设计基于全局稳定的建筑结构分散滑模控制。所提控制方法有效抑制了大尺度建筑结构振动响应,避免了滑模设计所需的复杂坐标变换,简化了基于输出的稳定滑模设计。     

地震作用下高层建筑结构的重叠分散控制研究

针对地震作用下高层建筑结构大系统控制问题,引入分散控制的策略,导出重叠分散最优控制(Overlapping Decentralized Optimal Control)算法。该方法将包含原理与线性二次型(Linear Quadratic Regulator)最优控制原理相结合,通过扩展分解控制系统及协调收缩获得重叠分散控制器。对某20层钢结构Benchmark结构模型进行数值计算与分析,结果表明,导出的重叠分散最优控制方法可以有效地抑制结构地震反应,控制效果不仅接近传统的集中控制方法,而且提高了高层结构大系统控制的鲁棒性和可靠性。本文方法可应用于大尺度高层结构振动控制中,实现高层建筑结构地震反应的可靠控制。     

磁流变阻尼建筑结构多态控制策略研究

针对双态控制易引起结构加速度局部放大和三态控制中刚度不易确定的问题,提出以速度响应作为状态切换参数的多态控制策略.以磁流变阻尼结构为例对采用不同的速度切换参数大小、输入地震动强度、场地类别和结构周期等相关因素对结构地震反应控制效果进行对比分析,发现存在一个对加速度响应的最优速度控制参数.并利用遗传算法对状态切换参数的取值进行优化计算,得到其简化计算公式.通过对一装有磁流变阻尼器的三层钢筋混凝土框架-剪力墙结构的振动台试验,对比分析多态控制策略与passive-off、passive-on和双态控制的控制效果.研究结果表明,多态控制策略比其它控制策略能更有效地控制结构反应,实用性较强;提出的速度切换参数估算公式是合理的.     

Non-probabilistic reliability method and reliability-based optimal LQR design for vibration control of structures with uncertain-but-bounded parameters

Uncertainty is inherent and unavoidable in almost all engineering systems. It is of essential significance to deal with uncertainties by means of reliability approach and to achieve a reasonable balance between reliability against uncertainties and system performance in the control design of uncertain systems. Nevertheless, reliability methods which can be used directly for analysis and synthesis of active control of structures in the presence of uncertainties remain to be developed, especially in non-probabilistic uncertainty situations. In the present paper, the issue of vibration con- trol of uncertain structures using linear quadratic regulator （LQR） approach is studied from the viewpoint of reliabil- ity. An efficient non-probabilistic robust reliability method for LQR-based static output feedback robust control of un- certain structures is presented by treating bounded uncertain parameters as interval variables. The optimal vibration con- troller design for uncertain structures is carried out by solv- ing a robust reliability-based optimization problem with the objective to minimize the quadratic performance index. The controller obtained may possess optimum performance un- der the condition that the controlled structure is robustly re- liable with respect to admissible uncertainties. The proposed method provides an essential basis for achieving a balance between robustness and performance in controller design ot uncertain structures. The presented formulations are in the framework of linear matrix inequality and can be carried out conveniently. Two numerical examples are provided to illustrate the effectiveness and feasibility of the present method.     

基于神经网络算法的建筑结构振动分散控制研究

针对地震作用下建筑结构振动分散控制问题,引入神经网络算法,研究结构振动分散神经网络控制策略,来解决分散控制中各子系统的耦合问题和神经网络算法的训练成本问题.利用径向基函数RBF(Radical Basis Function)神经网络模型并基于newrb函数构建了RBF神经网络控制器,对某20层Benchmark结构模型分别进行集中控制和多工况子系统划分分散控制的数值模拟分析,结果表明,提出的各子系统耦合的分散RBF神经网络振动控制策略考虑了子系统间的信息共享,可有效控制结构的振动响应,且子系统达到理想训练结果所需的训练次数与BP网络相比显著降低.     

Decentralized sliding mode control of fractional-order large-scale nonlinear systems

This paper presents some novel discussions on fully decentralized and semi-decentralized control of fractional-order large-scale nonlinear systems with two distinctive fractional derivative dynamics. First, two decentralized fractional-order sliding mode controllers with different sliding surfaces are designed. Stability of the closed-loop systems is attained under the assumption that the uncertainties and interconnections among the subsystems are bounded, and the upper bound is known. However, determining the interconnections and uncertainties bound in a large-scale system is troublesome. Therefore in the second step, two different fuzzy systems with adaptive tuning structures are utilized to approximate the interconnections and uncertainties. Since the fuzzy system uses the adjacent subsystem variables as its own input, this strategy is known as semi-decentralized fractional-order sliding mode control. For both fully decentralized and semi-decentralized control schemes, the stability of closed-loop systems has been analyzed depend on the sliding surface dynamics by integer-order or fractional-order stability theorems. Eventually, simulation results are presented to illustrate the effectiveness of the suggested robust controllers.     

Dynamic behavior and vibration control of a tensegrity structure

Tensegrities are lightweight space reticulated structures composed of cables and struts. Stability is provided by the self-stress state between tensioned and compressed elements. Tensegrity systems have in general low structural damping, leading to challenges with respect to dynamic loading. This paper describes dynamic behavior and vibration control of a full-scale active tensegrity structure. Laboratory testing and numerical simulations confirmed that control of the self-stress influences the dynamic behavior. A multi-objective vibration control strategy is proposed. Vibration control is carried out by modifying the self-stress level of the structure through small movement of active struts in order to shift the natural frequencies away from excitation. The PGSL stochastic search algorithm successfully identifies good control commands enabling reduction of structural response to acceptable levels at minimum control cost.     

Decentralized sliding mode quantized feedback control for a class of uncertain large-scale systems with dead-zone input

This paper is concerned with the robust quantized feedback stabilization problem for a class of uncertain nonlinear large-scale systems with dead-zone nonlinearity in actuator devices. It is assumed that state signals of each subsystem are quantized and the quantized state signals are transmitted over a digital channel to the controller side. Combined with a proposed discrete on-line adjustment policy of quantization parameters, a decentralized sliding mode quantized feedback control scheme is developed to tackle parameter uncertainties and dead-zone input nonlinearity simultaneously, and ensure that the system trajectory of each subsystem converges to the corresponding desired sliding manifold. Finally, an example is given to verify the validity of the theoretical result.     

Active disturbance rejection adaptive control of uncertain nonlinear systems: theory and application

This paper proposes an active disturbance rejection adaptive controller for tracking control of a class of uncertain nonlinear systems with consideration of both parametric uncertainties and uncertain nonlinearities by effectively integrating adaptive control with extended state observer via backstepping method. Parametric uncertainties are handled by the synthesized adaptive law and the remaining uncertainties are estimated by extended state observer and then compensated in a feedforward way. Moreover, both matched uncertainties and unmatched uncertainties can be estimated by constructing an extended state observer for each channel of the considered nonlinear plant. Since parametric uncertainties can be reduced by parameter adaptation, the learning burden of extended state observer is much reduced. Consequently, high-gain feedback is avoided and improved tracking performance can be expected. The proposed controller theoretically guarantees a prescribed transient tracking performance and final tracking accuracy in general while achieving asymptotic tracking when the uncertain nonlinearities are not time-variant. The motion control of a motor-driven robot manipulator is investigated as an application example with some suitable modifications and improvements, and comparative simulation results are obtained to verify the high tracking performance nature of the proposed control strategy.     

Decentralized adaptive fuzzy control of large-scale nonaffine nonlinear systems by state and output feedback

This paper develops two novel decentralized adaptive fuzzy control methods of large-scale nonaffine uncertain nonlinear systems. By using a fuzzy inference system and implicit function theorem, a decentralized direct adaptive state feedback fuzzy control algorithm is firstly presented for a class of large-scale nonaffine continuous-time systems. By using a high-gain observer to reconstruct the system states, an extension is made to a decentralized output feedback control of unmeasurable interactive nonaffine systems. The decentralized adaptive fuzzy control schemes via state and output feedback guarantee the stability of the closed-loop large-scale systems. The effectiveness of the developed approaches is demonstrated through simulation results of a platoon of vehicles within an automated highway system.     

Decentralized direct adaptive fuzzy control of robots using voltage control strategy

Decentralized control is the most favorite control of robot manipulators due to computational simplicity and ease of implementation. Beside that, adaptive fuzzy control efficiently controls uncertain nonlinear systems. These motivate us to design a decentralized fuzzy controller. However, there are some challenging problems to guarantee stability. The state-space model of the robotic system including the robot manipulator and motors is in a noncompanion form, multivariable, highly nonlinear, and heavily coupled with a variable input gain matrix. For this purpose, adaptive fuzzy control may use all variable states. As a result, it suffers from computational burden. To overcome the problems, we present a novel decentralized Direct Adaptive Fuzzy Control (DAFC) of electrically driven robot manipulators using the voltage control strategy. The proposed DAFC is simple, in a decentralized structure with high-accuracy response, robust tracking performance, and guaranteed stability. Instead of all state variables, only the tracking error of every joint and its derivative are given as the inputs of the controller. The proposed DAFC is simulated on a SCARA robot driven by permanent magnet dc motors. Simulation results verify superiority of the decentralized DAFC to a decentralized PD-fuzzy controller.