三轴电磁-压电混合驱动快速刀具伺服的轨迹跟踪控制

三轴快速刀具伺服（Fast Tool Servo, FTS）具有更高的刀具空间运动柔性，逐渐用于复杂光学曲面和微纳结构表面的切削加工。针对所研制电磁-压电混合驱动三轴FTS存在的轴间耦合、高频谐振和迟滞非线性等因素对轨迹跟踪性能的影响，研究综合补偿策略实现三轴空间轨迹的高性能跟踪控制。以陷波滤波器抑制系统高频谐振，以前馈解耦补偿弱化平面轴间耦合；针对法应力电磁驱动和压电驱动的迟滞非线性，提出以线性动力学模型级联Prandtl-Ishlinskii模型描述各轴的动态迟滞特性，并构建无需直接求逆的迟滞前馈补偿模型，实现系统的迟滞非线性补偿。谐波扫频测试结果表明：所采用的陷波滤波器可以很好地消除高频谐振，前馈解耦补偿可将平面XY轴间的耦合幅值降低约14 dB。宽频域内迟滞建模结果表明：平面XY轴和Z轴的动态迟滞建模误差分别小于±2.2%和±1.8%。以PID为主控制器，对宽频谐波（10～100 Hz）的跟踪结果表明：采用综合补偿策略获得各轴的最大跟踪误差约为仅采用逆动力学前馈补偿的25%～50%，进一步对空间螺旋球面轨迹进行了跟踪测试，证明了所构建的综合补偿控制策略的有效性。     

SDH模型与神经网络串联的谐波减速器混合迟滞建模研究

针对柔性环节含有的谐波减速器所表现出的特殊非线性迟滞特性,构建了由SDH模型与神经网络串联的谐波减速器的混合迟滞模型。以输入与输出信号之间具有与谐波减速器迟滞曲线相似迟滞特性的SDH模型为前置模型,以补偿前置模型在描述迟滞特性时存在的误差的非线性动态RBF神经网络作为后置模型,构成了混合迟滞模型,描述谐波减速器迟滞非线性特性。根据所搭建的实验平台,对不同频率输入信号、不同负载状态下获得的数据进行建模,与经典RBF神经网络模型和SDH模型相对比,实验表明,所构造的混合迟滞模型精度高、适应性强。     

非对称泵缸系统模型跟踪控制研究

用神经网络逼近非对称泵缸系统的非线性逆模型，通过神经网络模型参考控制实现电液斜盘位置和流量的控制，使系统的不确定性和非线性等得到补偿。仿真和实验表明，所开发的神经网络控制器能够较好地实现模型跟踪控制，有较好的自适应性和鲁棒性，跟踪性能有较大改善。     

混合驱动平面六杆机构动力学分析

混合驱动平面六杆机构具有两个自由度，其动力学模型是一个高度非线性强耦合系统。通过分析平面六杆的动力学特性，建立了基于拉格朗日方程的混合驱动平面六杆机构系统动力学模型，并对动力学模型的特点进行分析。     

高压带电作业机器人升降系统振动分析及控制补偿策略

介绍了高压带电作业机器人的结构和工作原理；建立了高压带电机器人升降系统的非线性动力学模型，用非线性动力学理论对上述模型进行理论分析，求出其二阶非线性近似解，对系统的运动稳定性进行了研究，得出升降平台在受导线交变作用力时在水平平面的轨迹；最后给出了控制补偿策略。     

仿生双目异向运动控制系统

介绍一种用于超小型无人旋翼机自主着陆的仿生双目异向运动系统的总体设计,研制,工作过程以及系统设计中的技术问题和解决方案。     

基于神经网络的复杂结构映射建模和优化方法

运用结构力学方法建立易于解析的大型结构平面模型；采用商业有限元软件建立能精确反映结构真实受力的空间模型。求解得到2种模型各单元内力，将平面模型单元内力作为输入，空间模型的内力作为输出而构造样本集。利用神经网络的非线性映射能力，通过样本训练提取其特征和本质联系，以获得具有内插和泛化能力的映射模型，为后续优化设计奠定了基础。     

基于神经网络的复杂曲面加工误差控制

在复杂曲面的切削过程中，加工系统表现显著的多输入多输出及非线性特征，传统的误差补偿方法不能有效地保证加工精度，因此提出一种加工误差控制方法，引入神经网络对加工系统的逆模型进行辨识，运用该模型前置校正加工系统以改善加工效果，充分考虑到机械系统的非线性特征，且网络模型可连续辨识，因而系统的静态性能和动态特性均能有效补偿，在中凸变椭圆活塞裙面加工中的成功应用，证明其合理性及先进性。     

基于深度学习方法的含间隙铰链多刚体系统的动力学建模与分析

机械系统中,铰接处接触力受铰间间隙量大小、运动副各部件的材料属性、运动过程中的接触状态等因素的影响,表现出很强的非线性。传统的间隙铰摩擦理论模型主要关注对摩擦现象描述的普适性,而难以精确地描述摩擦过程中摩擦力的非线性特征。基于物理样机试验获得的数据,使用深度学习方法建立了间隙铰非线性接触力神经网络模型,通过摩擦试验生成接触摩擦力数据集,结合旋转铰间隙接触碰撞力混合模型生成接触碰撞力数据集,对模型进行训练和测试,得到了旋转间隙铰的神经网络动力学模型。在此基础上,结合拉格朗日方程对含间隙铰的曲柄滑块机构进行建模,建立了“多刚体系统-间隙铰-多刚体系统”的动力学模型,通过仿真分析得到系统关键参数的动力学响应,并与物理试验结果进行了对比,验证了基于深度学习方法获得的间隙铰模型的正确性,为深度学习方法在非线性系统动力学建模方向上的应用提供了一个可行的思路。     

迟滞非线性系统辨识与补偿控制研究

针对智能材料中存在的迟滞问题,对其开展了迟滞非线性特性分析,建立了迟滞系统。该迟滞系统由两个部分串联构成:一部分是滤去传递函数影响的Preisach模型;另一部分是不考虑迟滞影响的系统传递函数。将离线和在线辨识方法应用到辨识迟滞系统中,应用最小二乘法离线辨识得到了辨识传递函数参数,再用此辨识传递函数参数作为神经网络辨识的初始权值,得到了神经网络在线辨识的辨识模型;建立了辨识传递函数的逆模型控制系统和前馈逆模型PID控制系统,并对辨识系统进行了迟滞非线性补偿。研究结果表明,模型辨识方法的可行性和补偿控制的有效性在仿真中得到了验证。     

Algorithm research and realization of the turning control system for heavy transportation vehicle

The dynamics of turning system which is a nonlinear system normally has great impact on the transportation speed of the vehicle having heavy load and large size.The dynamics of turning system depends on control algorithm and its implementation,but the existing control algorithms which having high dynamics in the application of heavy transportation vehicle are complex for realization and high hardware requirement.So,the nonlinear turning system is analyzed for improving its dynamics by researching new efficient control algorithm.The models of electromagnetic valve,hydraulic cylinder and turning mechanical part are built individually to get the open-loop model of the turning system following characteristics analyzed.According to the model,a new control algorithm for heavy transportation vehicle which combined PID with Bang-Bang control is presented.Then the close-loop model of turning system is obtained under Matlab/Simulink environment.By comparing the step response of different control algorithms in the same conditions,the new algorithm’s validity is verified.On the basis of the analysis results,the algorithm is adopted to implement the turning control system by using CAN field bus and PLC controllers.Furthermore,the turning control system has been applied in one type of heavy transportation vehicle.It reduces the response time of turning system from seconds level to 250 ms,and the speed of heavy transportation vehicle increases from 5 km/h to 30 km/h.The application result shows that the algorithm and turning control system have met all the turning requirements.This new type of turning control algorithm proposed is simple in implementation for fast response of nonlinear and large-scale turning system of heavy transportation vehicle.     

正独立式机械双流变速箱的动力学仿真研究

采用多体动力学仿真软件ADMAS,建立了某正独立式机械双流变速传动箱在直驶、转向和中心转向时的动力学仿真分析模型,利用在各种任务剖面下,行驶仿真实验获得的负载转矩和由发动机的外特性曲线构成的输入转矩对其进行动力学仿真分析,获得了其关键部件的动态载荷谱,为变速箱的动态优化设计和剩余寿命预测提供理论依据.     

平面可控七杆机构的键合图动力学分析

介绍了基于键合图理论的混合驱动七杆机构的动力学分析方法，给出了机构的键合图模型，为确定混合驱动七杆机构的主辅驱动力、力矩大小提供了一种规则化、适合于计算机自动生成的动力学方程式，并给出了其动力学分析的一个具体实例。     

Hybrid neural network bushing model for vehicle dynamics simulation

Although the linear model was widely used for the bushing model in vehicle suspension systems, it could not express the nonlinear characteristics of bushing in terms of the amplitude and the frequency. An artificial neural network model was suggested to consider the hysteretic responses of bushings. This model, however, often diverges due to the uncertainties of the neural network under the unexpected excitation inputs. In this paper, a hybrid neural network bushing model combining linear and neural network is suggested. A linear model was employed to represent linear stiffness and damping effects, and the artificial neural network algorithm was adopted to take into account the hysteretic responses. A rubber test was performed to capture bushing characteristics, where sine excitation with different frequencies and amplitudes is applied. Random test results were used to update the weighting factors of the neural network model. It is proven that the proposed model has more robust characteristics than a simple neural network model under step excitation input. A full car simulation was carried out to verify the proposed bushing models. It was shown that the hybrid model results are almost identical to the linear model under several maneuvers. This paper was recommended for publication in revised form by Associate Editor Hong Hee Yoo Dr. Wan-Suk Yoo was born in 1954, and received B.S. degree from Seoul National University (1976), and got M.S. degree from KAIST (1978) and Ph.D. from the University of Iowa (1985). He is currently a full professor at the Pusan National University in Korea, where he joined since 1978. His major area is vehicle dynamics and flexible multibody dynamics. He became an ASME Fellow (2004), and currently serving as an associate editor for the ASME, J. of computational and nonlinear dynamics. He is also serving a contributing editor for the multibody system dynamics journal. He is serving as ISC chair for the ACMD2008, and a member at IFToMM TC for multibody dynamics. He is currently a vicepresident of the KSME (Korean Society of Mechanical Engineers).     

A partial feedback linearization based control design and simulation for three phase shunt active power filter

This paper exploited partial feedback linearization technique to control design of a three phase shunt active power filter (APF) by considering it as a Multiple Input Multiple Output (MIMO) system. The averaged dynamic model of the three phase APF has been derived considering the single phase equivalent circuit of the system. This averaged dynamic model is used to partially feedback linearize the MIMO nonlinear system dynamics. New control input to the linearized system is obtained considering the stability of the complete APF system. After that, control input to APF is derived by nonlinear transformation. Stability of the internal dynamics of the system is analyzed considering zero dynamics of the system. MATLAB/Simulink based simulation results are provided to validate the performance of the controller.     

含间隙和时变刚度的齿轮机构振动特性分析及实验研究

以齿轮系统动力学和非线性动力学理论为基础,针对齿轮机构时变啮合刚度和齿侧间隙耦合作用的具体特点,并考虑油膜挤压刚度,提出了基于轮齿弹性振动及单、双齿啮合区变化的齿轮机构多体弹性非线性动力学模型,用GEAR方法对其在某些参数域中进行了非线性振动研究,得出不同的参数对齿轮机构动力学特性的影响,并提出了实验方案。     

含间隙机构无质量杆-弹簧阻尼组合模型的近似解法

提出了一种新的无质量杆一弹簧阻尼组合(MKD)模型非线性动力学运动微分方程的近似解法。它通过求解无间隙机构来得到相应的含间隙机构的动力响应，无须求解系统的非线性微分方程组，使解决问题的难度大为降低，且具有较高的精度。     

非线性变形对大范围平面运动梁动力学特性的影响

根据柔性梁的几何非线性变形理论,从连续介质的力学原理出发,针对大范围运动的平面柔性梁,考虑了弯曲的非线性因素对横向变形的影响,得到了较为精确的变形模式。利用Lagrange方程建立了非线性变形模式下的动力学方程。仿真算例说明横向变形的非线性因素会对柔性梁的变形位移产生影响。     

Nonlinear micro-dynamic behavior of a ball-screw driven precision slide system

An essential ingredient in diamond turning is a slide system that can respond quickly and precisely to very small input signals. In this paper, a study is carried out to characterize the micro-dynamic (or small motion) behavior of a ball-screw driven precision slide. First, it is observed that the dynamics of the slide system change significantly from a nominal linear approximation as the commanded input signal decreases. This apparently nonlinear phenomenon can be attributed mainly to the presence of the nonlinear friction and local elastic deformation plus other residual effects due to a higher noise-to-signal ratio for a smaller input signal, the digitization of the servo error, etc. Specifically, for small input signals, a local elastic deformation at the ball-screw interface prior to stiction breakaway resulted in a secondary resonant peak in the frequency response. As the magnitude of input signal increased, the peak showed a decrease in its frequency as well as the magnitude, and finally disappeared completely beyond a certain input signal magnitude. The presence of the peak is a strong indication that the micro-dynamics of the system are nonlinear, and the gradual shift and eventual disappearance of the peak indicate that the behavior is characterized by varying spring and damping constants. A nonlinear model has been developed based on this observation, and simulated time response matched accurately the actual micro-dynamic response of the system.