周期振动隔离的自适应方法及其实验研究

针对多频、多点隔振问题,将跟踪滤波与带饱和抑制的LMS算法相结合,构造出基于分量控制的自适应隔振方法,将其用于多频率多通道控制,并进行实验验证。该方法是在通用的LMS算法的基础上,引入饱和抑制函数,根据控制饱和程度,优化控制器权系数更新过程,避免控制器的性能恶化。实验结果表明,自适应方法能够在多通道控制中很好地隔离周期振动,不仅具有良好的稳态控制效果,而且可以改善输出饱和情况下的控制性能。     

电液振动台正弦振动自适应控制方法

电液振动台是大型结构正弦振动模拟实验的关键设备。受液压系统频宽及系统中存在的死区、摩擦力等因素影响,正弦振动实验中加速度输出信号存在跟踪精度低、波形失真度大等问题。为提高电液振动台控制精度,提出正弦振动自适应逆控制及谐波抑制复合控制策略。通过带遗忘因子的RLS算法构建自适应逆控制器,提高正弦波形的跟踪精度。基于快速块LMS算法构建双次自适应谐波抑制控制器,减小系统加速度输出信号波形失真度。最后通过振动台实验,验证了控制策略的有效性。     

结构自适应频谱塑形主动控制研究

针对复杂机械系统的靶向振动控制需求,提出不同频率分量幅值相位可控、任意频率可注入的自适应频谱塑形主动控制算法.首先,在传统的滤波自适应控制的结构中引入一个控制器的数字镜像模型,通过自适应算法在线更新该数字镜像模型的系数实现原控制器系数的随动修正,改善了控制系统的闭环特性,提升了控制系统的收敛性.其次,提出了广义塑形滤波...     

多频线谱激励下的混合自适应微振动主动控制

针对多频线谱激励下的结构振动主动控制中的频率失配问题,以并联结构滤波-x最小均方(filtered-x least mean square,简称FxLMS)算法为基础,提出一种混合自适应振动主动控制方法.前馈通道通过多个带通滤波器将多频线谱激励振动的参考信号解耦成为多个单频线谱信号,多个独立自适应滤波器调节权值抑制单一...     

LMS自适应主动控制汽车悬架系统试验研究

以汽车操纵稳定性及行驶平顺性为控制目标，确定汽车系统的簧上质量加速度、车轮动载荷、悬架动挠度为具体评价参数。选择LMS自适应滤波算法，通过调整自适应滤波器的权系数使二次性能指标最小，根据单个样本方差的负梯度来调节权系数，得到控制输出。针对简化的汽车模型，在路面信号作为激励源的仿真研究过程中，算法对悬架系统的振动控制收到了较好的效果。在两自由度的悬架系统试验台架上进行了试验研究，结果进一步证明该算法的有效性，表明LMS自适应控制策略在主动悬架系统中的应用切实可行。     

基于IIR结构的自适应滤波振动主动控制方法

当前自适应滤波前馈控制方法中具有代表性的是滤波-X最小均方(filtered-X least mean square,简称FX-LMS)算法,它通常假定干扰源可测且作为前馈控制器的参考输入,但实际振动控制过程中需要考虑控制输出反馈信号对参考信号的影响,因此滤波-X算法面向实际应用具有较大的局限性。针对这一问题,以机敏压电太阳能帆板结构为模拟试验对象,提出一种基于IIR(infinite impulse response,简称IIR)结构的滤波-U最小均方(filtered-U least mean square,简称FULMS)自适应滤波控制方法,着重分析了控制器结构设计、FULMS算法推理过程、试验模型结构设计、试验平台的构建及其试验验证等环节。经过与FXLMS算法对比仿真试验,笔者所设计的控制算法控制效果良好。将其进行试验验证分析,结果表明,所采用的控制器设计方法与控制算法收敛速度快,控制效果好,为自适应振动控制方法向实际工程应用提供了较好的研究基础。     

关节驱动柔性臂非最小相位系统的全局终端滑模控制

研究关节驱动柔性臂这一非最小相位系统的点对点定位与振动抑制问题。基于闭环动力学原理,将伺服反馈约束理论与Rayleigh-Ritz法相结合,建立耦合关节控制器动力特性的柔性臂振动偏微分方程,由此证明柔性臂实现点对点定位的同时进行振动抑制的可行性。采用微分几何输入/输出线性化方法重新定义观测输出,在新坐标系下将原系统分解为输入/输出子系统和内部子系统,并导出零动力学方程。设计一种全局终端滑模控制器,不仅使输入/输出子系统在有限时间内快速收敛至零,而且避免了常规滑模控制的抖振问题,利用极点配置法设计零动态子系统的控制器参数使整个系统Lyapunov稳定,通过数值仿真验证了所设计控制策略的有效性。设计并建立试验平台,试验结果表明仅以关节处的驱动电机为作动器可同时实现柔性臂系统的点对点定位与快速振动抑制。     

电力系统非线性自适应励磁控制设计

考虑了发电机参数的不确定性,根据滤波后的线性参数化模型,通过使参数更新的方向与关于参数的预测误差平方的梯度方向相反,提出了一种在线自适应参数估计非线性励磁控制方法。数字仿真试验表明,设计的非线性自适应控制律具有较强的参数自适应能力,控制器以发电机端电压为输出,保证了系统内动态的稳定。     

输入受限的非完整移动机器人的自适应模糊控制

提出了一种非完整移动机器人饱和自适应模糊轨迹跟踪控制方法,该方法基于反演技术分别设计了系统的运动学控制器和动力学控制器。运动学控制器通过引入分流控制技术解决了初始速度跳变引起的控制量突变问题,动力学控制器利用饱和函数和受限控制参数实现了其有界力矩控制。自适应模糊控制器将模糊逻辑系统与自适应方法相结合,有效消除了常规方法难以解决的系统未知不确定性对系统的影响。通过Lyapunov直接法证明了该系统是收敛且渐进稳定的。仿真结果验证了所设计控制器的良好控制性能和强鲁棒性。     

基于自适应陷波与重复控制的主动磁悬浮轴承系统谐波振动抑制

由转子质量不平衡和传感器跳动引起的谐波振动是磁悬浮转子系统中的主要扰动。为了抑制这些干扰,提出了一种基于重复控制和可变相位自适应陷波反馈的谐波振动抑制的复合控制方法。首先通过建立磁悬浮转子系统模型,分析了不同干扰振动力的产生机理。然后,设计了插入式重复控制器,抑制传感器跳动引起的谐波振动,利用自适应陷波滤波器在线提取同频信号自适应补偿不平衡,通过改变不同频率下的相位角来保持系统的稳定性,并对同频位移刚度进行补偿,使系统在较宽的速度范围内自抑制谐波振动。最后,通过仿真和实验对提出的控制方法进行了验证。实验结果表明,一次、三次和五次谐波振动分别减少94.4%,90.4%和85.9%,采用所提出的复合控制方法可以有效抑制谐波振动。验证了所提控制方法的有效性。     

具有输入饱和的环形永磁力矩电动机自适应鲁棒控制

五轴联动数控机床中复合A/C轴直接驱动环形永磁力矩电动机伺服系统在输入饱和情况时易导致系统性能变差,甚至引起系统不稳定。针对系统中的输入饱和问题,基于反步法设计了饱和自适应鲁棒位置控制器,即设计一个虚拟控制律保证误差信号在每一步都收敛且有界。在控制器设计中构建非降函数处理执行器饱和,将控制输入限制在饱和范围内;对于参数不确定性,采用不连续投影算法进行在线参数估计。仿真结果表明该方法不仅使伺服系统在无输入饱和情况下具有较好的伺服性能,输入饱和情况下也能实现良好的跟踪性能。     

Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation

This paper presents a robust adaptive neural networks control strategy for spacecraft rendezvous and docking with the coupled position and attitude dynamics under input saturation. Backstepping technique is applied to design a relative attitude controller and a relative position controller, respectively. The dynamics uncertainties are approximated by radial basis function neural networks (RBFNNs). A novel switching controller consists of an adaptive neural networks controller dominating in its active region combined with an extra robust controller to avoid invalidation of the RBFNNs destroying stability of the system outside the neural active region. An auxiliary signal is introduced to compensate the input saturation with anti-windup technique, and a command filter is employed to approximate derivative of the virtual control in the backstepping procedure. Globally uniformly ultimately bounded of the relative states is proved via Lyapunov theory. Simulation example demonstrates effectiveness of the proposed control scheme.     

Adaptive integral feedback controller for pitch and yaw channels of an AUV with actuator saturations

Input saturations and uncertain dynamics are among the practical challenges in control of autonomous vehicles. Adaptive control is known as a proper method to deal with the uncertain dynamics of these systems. Therefore, incorporating the ability to confront with input saturation in adaptive controllers can be valuable. In this paper, an adaptive autopilot is presented for the pitch and yaw channels of an autonomous underwater vehicle (AUV) in the presence of input saturations. This will be performed by combination of a model reference adaptive control (MRAC) with integral state feedback with a modern anti-windup (AW) compensator. MRAC with integral state feedback is commonly used in autonomous vehicles. However, some proper modifications need to be taken into account in order to cope with the saturation problem. To this end, a Riccati-based anti-windup (AW) compensator is employed. The presented technique is applied to the non-linear six degrees of freedom (DOF) model of an AUV and the obtained results are compared with that of its baseline method. Several simulation scenarios are executed in the pitch and yaw channels to evaluate the controller performance. Moreover, effectiveness of proposed adaptive controller is comprehensively investigated by implementing Monte Carlo simulations. The obtained results verify the performance of proposed method.     

Finite-time decentralized adaptive neural constrained control for interconnected nonlinear time-delay systems with dynamics couplings among subsystems

The problem of finite-time decentralized neural adaptive constrained control is studied for large-scale nonlinear time-delay systems in the non-affine form. The main features of the considered system are that 1) unknown unmatched time-delay interactions are considered, 2) the couplings among the nested subsystems are involved in uncertain nonlinear systems, 3) based on finite-time stability approach, asymmetric saturation actuators and output constraints are studied in large-scale systems. First, the smooth asymmetric saturation nonlinearity and barrier Lyapunov functions are used to achieve the input and output constraints. Second, the appropriately designed Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions, and the neural networks are employed to approximate the unknown nonlinearities. Note that, due to unknown time-delay interactions and the couplings among subsystems, the controller design is more meaningful and challenging. At last, based on finite-time stability theory and Lyapunov stability theory, a decentralized adaptive controller is proposed, which decreases the number of learning parameters. It is shown that the designed controller can ensure that all closed-loop signals are bounded and the tracking error converges to a small neighborhood of the origin. The simulation studies are presented to show the effectiveness of the proposed method.     

Self-tuning adaptive control for an industrial weigh belt feeder

An industrial weigh belt feeder is used to transport solid materials into a manufacturing process at a constant feedrate. It exhibits nonlinear behavior because of motor friction, saturation, and quantization noise in the measurement sensors. To overcome the nonlinearities, a simple yet effective method of controller autotuning, an indirect self-tuning regulator, was designed and implemented for an industrial weigh belt feeder. Implementation issues are discussed and experimental results show the effectiveness of the adaptive controller for several different reference inputs. Also, the performance of the indirect self-tuning regulator is compared with that of a fuzzy logic controller for the same application.     

电液伺服系统的神经网络在线学习补偿自适应控制

基于反馈误差学习法、小波分析理论并结合面向控制的辨识思想，提出了一种神经网络在线学习补偿自适应控制结构。基于被控过程的小波变换结果信息利用反馈误差学习法调整控制参数。利用“参征器”实行监督控制，避免控制器的输出产生振荡或进入饱和状态。工程应用表明，该方法将过程辨识和“参征器”引入神经网络的学习和控制中，可有效地提高了系统的控制品质。     

Adaptive control for spacecraft rendezvous subject to actuator faults and saturations

An adaptive saturated fault-tolerant controller is proposed for a spacecraft rendezvous maneuver with a cooperative target spacecraft. The six-degree-of-freedom (6-DOF) relative dynamics subject to unknown inertial parameters, external disturbances, actuator faults and saturations are formulated in the pursuer's body-fixed frame. To design controller satisfying asymmetric magnitude constraints, a modified smooth hyperbolic tangent function is applied to approximate the non-differentiable saturation function. Based on the augmented system technique, an adaptive fault-tolerant saturated controller is designed for the pursuer by using a Nussbaum function matrix compensating for the nonlinear term arising from the input saturations. In addition, a Levant differentiator is introduced to obtain the derivative of the virtual control in finite time that avoids the complicated calculation. It is proved via Lyapunov stability theory that all the signals in the closed-loop augmented system are bounded and the relative errors asymptotically converge to zero. Numerical simulations are performed to illustrate effectiveness of the proposed controller.     

基于模型参考自适应控制的直线电机位置控制

针对直线电机的强非线性和时变特性,采用模型参考自适应控制(MRAC)方法对SISO直线电机闭环位置控制器进行了研究。利用偶极子对消建立了简化的永磁直线电机二阶数学模型,提出了基于局部参数最优化MIT(梯度)方案和全局稳定性理论的Lyapunov方案下的二阶直线电机位置模型参考自适应控制器,并对自适应控制器下的直线电机闭环系统稳定性进行了分析研究。在相同的前馈加反馈的控制器下,对这两种方案下的实验结果进行了对比分析。研究结果表明,基于Lyapunov第二方法设计的二阶控制器比MIT方法下的二阶控制器更能实现对三阶点到点轨迹输入信号的快速响应和跟踪,证实了直线电机位置自适应控制的有效性。     

非线性液压伺服系统高性能自适应控制器的仿真研究和实时分析

本文研究了一种新的自适应控制规律,即比例加积分控制规律和继电控制规律的综合控制。还研究了一些目前国际上自适应控制领域中没有很好解决的问题：如非线性自适应控制系统;应用滤波输入和输出以避免测量对象高阶导数问题;高阶对象和低阶模型的跟踪问题;非最小相位系统的稳定控制;利用前馈传递函数以改善系统动态特性问题。本文给出了若干数字仿真和数摸混合实时分析的结果数据和图形。