四辊卷板机侧辊位移跟踪控制

针对四辊卷板机侧辊位移跟踪控制存在负载变化、参数摄动和未建模动态等不确定性问题,提出一种基于非线性扰动观测器的自适应滑模控制策略。采用非线性扰动观测器在线获取并补偿等效扰动;针对引入非线性扰动观测器后的系统,采用反步法设计自适应滑模控制器,利用自适应律动态补偿扰动观测误差,以降低滑模控制器的切换增益。该设计方法放宽了滑模切换增益对系统不确定性上界的先验性要求,降低了外界干扰和系统不确定性对侧辊位移跟踪性能的影响。同时,采用李雅普洛夫方法证明了侧辊位移跟踪闭环控制系统的稳定性。根据工程实际参数进行仿真,结果表明,该控制策略对系统的不确定性,特别是负载变化具有较强的鲁棒性,可以满足侧辊位移快速、精确跟踪的要求。     

神经模糊PI控制在冷连轧机弯辊系统中的应用研究

针对液压弯辊系统的非线性、时变性和不确定性,设计了一种基于神经网络、模糊控制和PI算法的控制器.通过对本钢冷轧厂弯辊控制回路的应用仿真,结果表明神经模糊PI控制可提高弯辊系统对弯辊力给定值的跟踪能力,系统具有更优的动态性能.     

机电伺服系统L_1自适应控制

机电伺服系统存在较多不确定性,制约了系统性能提升。已有非线性控制方法虽可处理系统不确定性,但往往不能满足系统高频跟踪要求,而传统线性频宽控制方法又往往缺乏主动补偿不确定性的手段。针对机电系统参数未知又受时变干扰的情形,设计了一种L_1自适应非线性控制策略,不仅有效补偿了系统不确定性,而且实现了非线性控制器与线性频宽参数的一体化设计。鉴于L_1自适应控制策略依赖全状态反馈,又对测量噪声敏感,进一步搭建了跟踪微分器,快速跟踪速度信号动态特性的同时抑制了噪声,针对机电伺服系统的特点实现了跟踪微分器与L_1自适应控制的有效融合。最后,对比仿真结果验证了算法的有效性。     

数控机床直线伺服系统非线性自适应鲁棒控制的研究

对数控机床永磁直线伺服系统提出非线性自适应鲁棒控制的设计方法.在永磁直线伺服系统非线性数学模型的基础上,为实现对速度和电流的准确跟踪,建立了误差系统的动态模型.将跟踪和干扰抑制归结为非线性自适应鲁棒控制器设计问题,通过构造存储函数得到包含电阻辨识算法的自适应鲁棒控制器的定理,证明定理给出的控制器能满足干扰抑制和系统的渐进稳定.仿真结果表明,用该方法设计的系统能很好地抑制扰动和跟踪给定,满足对数控机床永磁直线伺服系统控制的要求.     

热轧立辊电液伺服系统的自适应模糊控制

以热轧立辊为研究背景,针对其液压伺服系统存在的非线性、参数不确定性以及负载干扰等特点,基于模糊基函数网络提出一种自适应控制方法.首先将非线性系统线性化并将其作为已知系统,利用这部分已知的动态特性设计反馈控制使标称系统稳定.然后利用模糊基函数网络仅学习非线性系统不确定性的上界,将输出作为补偿控制器的参数,并在Lyapunov稳定意义下构造自适应控制器,该自适应控制器不仅确保了闭环系统的鲁棒性而且加快了跟踪误差的收敛速度.将该控制器应用于某热轧立辊电液位置伺服系统中进行仿真研究,结果表明,该控制器优于传统的PID控制器,可以取得较好的控制效果.     

基于微分几何的离合器接合过程速度跟踪滑模控制

针对离合器控制系统中存在的非线性、外部干扰和参数不确定问题,提出了基于微分几何的离合器接合过程速度跟踪滑模控制方法。考虑系统参数的不确定性和外界干扰等不确定因素,建立了单个离合器起步动力学模型;基于微分几何的反馈线性化方法,得出系统的控制律;采用基于趋近律的滑模控制方法,设计了存在不确定干扰的离合器控制系统滑模控制器。利用Lyapunov理论对系统的稳定性进行了证明。仿真结果表明该控制器使离合器接合过程的速度跟踪精度高,且鲁棒性好。     

基于干扰观测器的舰船运动模拟器非线性控制

舰船运动模拟器在使用中会受到来自被测设备的干扰力,该干扰力对模拟器的精确控制存在较大影响。针对此问题,根据系统动力学模型,提出一种自适应干扰观测器,在线观测模拟器所受干扰力,并在控制器中进行补偿。分析液压驱动的舰船运动模拟器在精确控制中的困难,使用鲁棒控制器克服参数不确定性和未建模不确定性引起的干扰。利用反步法推导出综合非线性控制器,并通过Lyapunov方法证明了该控制器是渐近稳定的。试验证明,该控制器在存在较大外干扰情况下能够平稳运行,且与普通比例积分微分(Proportional integration differential,PID)算法相比,有效地提高了系统的动态跟踪性能。     

基于干扰观测器的时滞非线性切换系统滑模控制方法

为实现更加精准的时滞非线性切换系统滑模控制,应用干扰观测器设计一种新的系统滑模控制方法。构建时滞非线性切换系统模型,针对系统在发生结构变化时会产生复合干扰变化的情况,设计了一种非线性切换干扰观测器,实施系统不连续干扰的估计。通过 Backstepping 方法结合干扰观测器,设计一种切换滑模控制器,依据标量非线性特性打造一个滑模面,通过滑模控制器算法使时滞非线性切换系统能够满足滑模面的实际可达性条件,完成切换滑模控制器设计,实现系统的滑模控制。对设计的滑模控制方法进行测试,实验中选择的时滞非线性切换系统为一种变后掠翼 NSV 。实验结果表明,该设计方法能够实现较为准确地切入信号跟踪,表现出了很好的切换复合干扰估计性能。     

考虑摩擦力矩扰动的电液位置系统鲁棒自适应控制的研究

对电液摆动位置系统考虑了摩擦力矩干扰，并且模型算法采用基于bristle模型的动态摩擦力模型，线性参数与非线性参数同时在线估计。基于Lyapunov函数设计了鲁棒自适应控制器，和传统的控制性能相比，该系统具有跟踪性好，低速性能优等特点。     

基于修正LuGre模型的自适应鲁棒控制在机电伺服系统中的应用

机电位置伺服系统是典型的非线性系统,且存在诸多不确定性,使得传统方法设计的闭环控制器往往不能满足系统的高性能需求。针对动态摩擦参数和系统负载特性未知的情况,为伺服系统设计一种基于动态面滤波法的自适应鲁棒跟踪策略。构造一个非线性观测器来估计摩擦力矩,利用动态面滤波器简化控制器的设计,设计自适应鲁棒控制器以提高系统的稳态控制精度及鲁棒性。基于Lyapunov稳定性定理证明闭环系统的所有信号半全局一致有界,通过适当选择设计参数及初始化误差变量,跟踪误差可收敛到原点的一个任意小邻域内。仿真和试验结果表明,该控制器能够能有效地抑制摩擦干扰对伺服系统的不利影响,显著提高了系统的控制精度,为提高伺服系统的动态跟踪性能奠定基础。     

热连轧机磁栅尺结构的模型参数辩识

应用系统辨识技术,采用受控自回归(auto egressive exogeneous,简称ARX)模型结构和Matl ab系统 辨识工具箱,辨识了热连轧机辊缝位移传感器的动力学模型参数,建立了该传感器结构的动态 数学模型。系统的输入和输出数据分别为同步记录的轧机相应位置的振动加速度信号,ARX 模型的参数估计采用最小二乘估计法(LSCE),辨识模型的拟合度达到7948％。 辨识结果表明,传感器外壳圆盘结构的刚度和阻尼太小,对轧机垂直振动系统的 影响严重,说明其结构设计不合理,需要进行结构动力学修改。     

Spatial curvilinear path following control of underactuated AUV with multiple uncertainties

This paper investigates the problem of spatial curvilinear path following control of underactuated autonomous underwater vehicles (AUVs) with multiple uncertainties. Firstly, in order to design the appropriate controller, path following error dynamics model is constructed in a moving Serret–Frenet frame, and the five degrees of freedom (DOFs) dynamic model with multiple uncertainties is established. Secondly, the proposed control law is separated into kinematic controller and dynamic controller via back-stepping technique. In the case of kinematic controller, to overcome the drawback of dependence on the accurate vehicle model that are present in a number of path following control strategies described in the literature, the unknown side-slip angular velocity and attack angular velocity are treated as uncertainties. Whereas in the case of dynamic controller, the model parameters perturbations, unknown external environmental disturbances and the nonlinear hydrodynamic damping terms are treated as lumped uncertainties. Both kinematic and dynamic uncertainties are estimated and compensated by designed reduced-order linear extended state observes (LESOs). Thirdly, feedback linearization (FL) based control law is implemented for the control model using the estimates generated by reduced-order LESOs. For handling the problem of computational complexity inherent in the conventional back-stepping method, nonlinear tracking differentiators (NTDs) are applied to construct derivatives of the virtual control commands. Finally, the closed loop stability for the overall system is established. Simulation and comparative analysis demonstrate that the proposed controller exhibits enhanced performance in the presence of internal parameter variations, external unknown disturbances, unmodeled nonlinear damping terms, and measurement noises.     

Disturbance observer based fault estimation and dynamic output feedback fault tolerant control for fuzzy systems with local nonlinear models

This paper addresses the problems of fault estimation (FE) and fault tolerant control (FTC) for fuzzy systems with local nonlinear models, external disturbances, sensor and actuator faults, simultaneously. Disturbance observer (DO) and FE observer are designed, simultaneously. Compared with the existing results, the proposed observer is with a wider application range. Using the estimation information, a novel fuzzy dynamic output feedback fault tolerant controller (DOFFTC) is designed. The controller can be used for the fuzzy systems with unmeasurable local nonlinear models, mismatched input disturbances, and measurement output affecting by sensor faults and disturbances. At last, the simulation shows the effectiveness of the proposed methods.     

液压弯辊系统的优化神经网络内模控制

针对轧机液压弯辊系统存在非线性、时变性等特点,采用基于前馈神经网络的内模控制方法,将优化网络用于神经网络辨识器和内模控制器的离线训练,采用学习率自适应调整的改进BP算法在线调整网络权值。仿真研究表明,将优化网络用于液压弯辊系统控制,提高了液压弯辊系统的动态响应速度和稳态跟踪精度,具有较强的快速性和鲁棒性,能够取得理想的控制效果。     

双足欠驱动机器人能量成型控制

研究欠驱动双足机器人在3D空间稳定行走控制器。建立双足机器人的3D动力学模型,通过构建概循环拉格朗日函数,把欠驱动双足机器人的3D动态系统解耦成前向和侧向部分。针对前向2D欠驱动部分设计势能成型和动能成型控制器,为了求解能量成型控制器,将匹配方程分解成分别与角度和角速度相关的两个子条件,再将非线性偏微分方程变为线性偏微分方程,求解出能量成型控制器对前向行走进行控制,使前向行走获得稳定且具有仿生特点。对侧向部分采用零动态控制,在保证侧向稳定同时,还满足系统的动态解耦条件。对不同步长行走进行仿真试验,仿真结果表明,动态步行收敛于稳定的极限环,步态符合仿生规律,验证了所提出理论的可行性和有效性。     

Controller synthesis for automatic control systems: State and prospects

This paper considers the state and prospects for studies of the central problem of the theory and practice of automatic control—the problem of controller synthesis for systems with nonlinear plants. A crucial factor in the performance of controllers for nonlinear plants is the use of deep feedback for derivatives of the output variable, which provides localization of disturbances in fast subprocesses (against the background of the main processes) and allows the generation of the required dynamic properties     

Multivariable robust adaptive sliding mode control of an industrial boiler–turbine in the presence of modeling imprecisions and external disturbances: A comparison with type-I servo controller

To guarantee the safety and efficient performance of the power plant, a robust controller for the boiler–turbine unit is needed. In this paper, a robust adaptive sliding mode controller (RASMC) is proposed to control a nonlinear multi-input multi-output (MIMO) model of industrial boiler–turbine unit, in the presence of unknown bounded uncertainties and external disturbances. To overcome the coupled nonlinearities and investigate the zero dynamics, input–output linearization is performed, and then the new decoupled inputs are derived. To tackle the uncertainties and external disturbances, appropriate adaption laws are introduced. For constructing the RASMC, suitable sliding surface is considered. To guarantee the sliding motion occurrence, appropriate control laws are constructed. Then the robustness and stability of the proposed RASMC is proved via Lyapunov stability theory. To compare the performance of the purposed RASMC with traditional control schemes, a type-I servo controller is designed. To evaluate the performance of the proposed control schemes, simulation studies on nonlinear MIMO dynamic system in the presence of high frequency bounded uncertainties and external disturbances are conducted and compared. Comparison of the results reveals the superiority of proposed RASMC over the traditional control schemes. RAMSC acts efficiently in disturbance rejection and keeping the system behavior in desirable tracking objectives, without the existence of unstable quasi-periodic solutions.     

Clad height control in laser solid freeform fabrication using a feedforward PID controller

In this paper, a feedforward proportional-integral-derivative (PID) controller is developed to effectively control the clad height in laser solid freeform fabrication (LSFF). The scanning velocity is selected as the input control variable and the clad height is chosen as the output. A novel knowledge-based Hammerstein model, including a linear dynamic and a nonlinear memoryless block, is developed, and its parameters are identified offline using experimental data. The architecture of the controller consists of a PID and a feedforward module, which is the inverse of the identified model. The advantage of adding a feedforward path to the PID controller is evaluated experimentally, in which the results show a lower overshoot and faster response times. Also, the performance of the controller is verified in the presence of geometrical disturbances, as well as in the fabrication of a nonplanar part.     

Quasi-minimal active disturbance rejection control of MIMO perturbed linear systems based on differential neural networks and the attractive ellipsoid method

This study addresses the problem of designing an output-based controller to stabilize multi-input multi-output (MIMO) systems in the presence of parametric disturbances as well as uncertainties in the state model and output noise measurements. The controller design includes a linear state transformation which separates uncertainties matched to the control input and the unmatched ones. A differential neural network (DNN) observer produces a nonlinear approximation of the matched perturbation and the unknown states simultaneously in the transformed coordinates. This study proposes the use of the Attractive Ellipsoid Method (AEM) to optimize the gains of the controller and the gain observer in the DNN structure. As a consequence, the obtained control input minimizes the convergence zone for the estimation error. Moreover, the control design uses the estimated disturbance provided by the DNN to obtain a better performance in the stabilization task in comparison with a quasi-minimal output feedback controller based on a Luenberger observer and a sliding mode controller. Numerical results pointed out the advantages obtained by the nonlinear control based on the DNN observer. The first example deals with the stabilization of an academic linear MIMO perturbed system and the second example stabilizes the trajectories of a DC-motor into a predefined operation point.     

多种群遗传优化的客车防侧翻鲁棒控制方法

为改善客车主动防侧翻能力,提出多种群遗传优化的防侧翻鲁棒控制方法。考虑车轮侧倾外倾、侧倾转向、悬架变形外倾和变形转向对轮胎侧偏特性影响,以及客车垂向与侧倾运动的耦合特性,建立6自由度客车侧翻动力学模型;针对客车的实际干扰及参数不确定性,以最大横向载荷转移率为控制目标,融合差动制动原理设计客车主动防侧翻的鲁棒控制方法;应用多种群遗传理论对控制器的权函数进行动态优化,增强控制系统的抗干扰能力;选取J-Turn及Worst-Case典型侧翻工况进行数值仿真,分析防侧翻控制方法对不同行驶工况的适用性、前轮转向干扰及路面干扰下的抗干扰稳定性以及簧载质量和车速变化时参数摄动鲁棒性。结果表明该方法能将客车侧翻危险速度提高75%以上,有效改善客车主动防侧翻能力;且对不同行驶工况、不同类型干扰及参数变化均有强鲁棒性。