七自由度机器人振动抑制的新方法

针对七自由度工业机器人刚性较差,运动过程中容易产生振动且抑制困难的问题,提出了一种控制算法,将时滞整形滤波器与动力学前馈补偿组合在一起,对机器人两个关节采用时滞滤波,并同时采用基于简化动力学模型的前馈补偿来实现机器人振动抑制.     

一种挠性机器人逆动力学控制的迭代算法—动态位姿误差补偿方法

本文分析了挠性机器人逆动力学问题的数学模型,成功地提出了一种逆动力学控制的迭代算法——动态位姿误差补偿方法,可离线或实时计算执行目标位姿所需的驱动力矩和关节变量.本方法具有计算量小、精度高的特点,同时提出了具有动态前馈-伺服补偿和振动抑制结合的阶层控制方案.     

神经外科机器人定位精度研究

针对机器人辅助神经外科手术对高精度的要求，对“黎元”神经外科机器人的定位精度进行了研究．考虑到关节轴的微小偏差，基于修正的DH运动学模型对机器人的实际几何参数进行了辨识．采用基于误差反传（BP）算法的多层前馈神经网络对各关节的传动误差进行了补偿．利用高精度的三坐标测量臂对“黎元”神经外科机器人的定位精度进行了测量．结果表明，该机器人绝对定位精度最大值为1.63 mm，平均值为1.04 mm，较以前有了很大提高，能够满足多数神经外科手术的需要．     

注塑机注射速度的零相位滤波迭代学习控制

针对注射速度控制,设计了一种基于零相位滤波迭代学习的前馈反馈控制方法．该控制算法中的前馈部分采用迭代学习控制,利用以往批次信息实现精确跟踪;反馈部分采用比例控制,用于克服当前批次扰动．为了消除学习算法应用时出现的坏学习瞬态,这里先推导出所提控制算法在频域下的收敛条件,再给出基于零相位滤波的单调递减学习瞬态.经实验验证,这种方法可以使学习控制具有单调递减的学习瞬态曲线,并能很好地满足实际的注射控制要求．     

智能柔性机械臂的建模和振动主动控制研究

针对伺服电动机、谐波齿轮减速器、柔性臂及压电致动器组成的智能柔性机械臂系统,基于假设模态法和Hamilton原理建立系统动力学方程.为了实现系统较高精度的位置控制,同时快速抑制柔性臂的弹性振动,提出了对伺服电动机采用PD(proportional derivative)控制、对压电致动器采用模糊(fuzzy)控制的复合控制策略.在数值仿真分析的基础上,搭建了智能柔性机械臂系统测控平台.数值仿真和实验结果表明所提出的控制策略是可行的:PD控制算法控制伺服电动机以较高的位置精度完成了系统运动控制,模糊控制算法控制压电致动器较快地抑制了柔性臂的弹性振动.实验中柔性臂的振动衰减时间由6.5s缩短为3.5s,提高了柔性臂末端的定位控制精度,改善了系统的操作效率.     

基于遗传算法的直流伺服系统参数辨识及摩擦补偿控制

针对静态摩擦力对数控机床直流伺服系统的干扰问题,提出了一种先利用遗传算法对静态摩擦模型中的参数进行辨识,然后采用基于摩擦模型补偿的伺服控制方法。该方法首先根据直流伺服系统的摩擦特性建立摩擦模型,再将摩擦补偿引入到直流伺服系统的反馈控制结构中,获取伺服电机的位置误差。采用遗传算法对摩擦补偿模型进行参数辨识,使摩擦补偿量在数值上不断逼近实际的摩擦干扰,并利用摩擦补偿量来抵消摩擦给伺服系统带来的影响。为了验证参数辨识的效果,将普通PD控制与基于摩擦补偿的PD控制进行了仿真比较,实验结果表明,后者能够消除由于静摩擦的存在而造成的位置跟踪中出现的平顶现象,能够达到理想的跟踪效果。因此,本文所提出的方法具有较强的摩擦干扰补偿能力,能够实现对直流伺服系统的精确控制。     

基于线特征与内区域特征的视觉伺服解耦控制

针对多自由度机械臂快速趋近任意四边形态目标的视觉伺服控制难题，提出了结合线特征与内区域特征的机器人视觉伺服解耦控制方法.构建了目标内区域特征以指导相机的平移运动速率，利用目标的线特征给出相机的旋转角速率，并通过引入内区域特征的矢量补偿和质心坐标的位置补偿，实现了平移和旋转控制的部分解耦.最后，对机器人视觉伺服控制系统进行了稳定性分析.仿真验证结果表明所提方法能控制相机以较快而平滑的动作收敛到期望位姿，且在相机光轴与目标平面近似垂直的条件下能较好地克服深度估计造成的不确定性问题.     

具有不确定性重力补偿的无标定视觉伺服

文章首先叙述了视觉伺服的分类。给出了机器人动力学模型及其主要性质、自适应复合雅可比矩阵的具体推导过程。摄像机位于机器人末端的情况被考虑,给出了具有不确定性重力补偿的无标定PD视觉伺服控制器,而后运用李雅普诺夫方法对其进行了稳定性分析,结果证明系统能够渐近稳定。为了验证算法的有效性,针对一个二连杆eye-in-hand视觉伺服定位系统进行了仿真,仿真结果说明了算法的有效性。     

基于模糊补偿的主从式上肢外骨骼康复机器人训练控制方法

针对主从式上肢外骨骼康复机器人主臂信息获取、从臂快速响应等问题,提出了基于关节位姿、速度和力/力矩等信息的运动意图建模方法及基于模糊补偿的康复训练控制策略.根据人体工程学原理,提出了一种同构同型的主从式双臂康复机器人新型结构;利用D-H算法给出了笛卡儿空间的主从臂运动学模型,建立了患者健肢运动意图信息和从臂各关节动作的人机协作映射关系;以患者运动意图力矩作为输入,基于模糊补偿算法提出了患者-主臂-从臂协作控制策略,并利用李亚普诺夫定理证明了该控制系统的稳定性.仿真结果表明,康复机器人从臂可以根据患者运动意图跟随主臂运动,能有效地防止抖动误动,可避免对患肢的二次伤害.实验结果表明从臂运动轨迹平滑,无剧烈波动,控制轨迹跟踪主臂效果好.     

异型曲面加工机器人自适应NDO控制

针对异型曲面打磨机器人中摩擦导致的加工精度降低的问题，提出了一种改进的非线性干扰观测器对其进行观测和补偿。建立了高精度工业机械臂的动力学模型，基于该模型设计非线性干扰观测器并应用李雅普诺夫函数稳定性理论给出了系统的稳定性分析。引入典型摩擦模型，利用观测器估计不可测的内部摩擦状态，并将估计值用于PD控制器中摩擦补偿部分。经过仿真以及实验验证，对比实验结果表明该观测器可以使系统的控制精度大幅提高，降低了仿真实验的跟踪误差，实验平台的控制精度提高了30%以上，能很好地补偿双关节机械手的摩擦力，更好地跟踪关节位置。     

PD Controller for Manipulator with Kinetic Energy Term

A kinetic energy approach to PD control in joint space of a manipulator in terms of the eigenfactor quasi-coordinate velocity (EQV) vector is considered in this paper. The modified PD controller which contains quantities resulting from decomposition of a manipulator mass matrix is proposed. The obtained equations of motion are based on the eigenvalues and eigenvectors of the mass matrix (Junkins, J. L. and Schaub, H. [7]. It is shown that utilizing the EQV vector one can determine directly the kinetic energy of the manipulator and at the same time realize PD control in its joint space. This energy-based strategy gives an interesting insight into position control. The controller presented here was tested in simulation on a 3 d.o.f. direct drive arm manipulator and via experiment on a 2 d.o.f. manipulator. The results confirmed that one can directly determine the kinetic energy for the total manipulator as well for its each joint. Additionally, time response of the system under EQV controller is faster than for the classical controller if mechanical coupling are strong.     

Robust nonlinear control of a planar 2-DOF parallel manipulator with redundant actuation

A new robust nonlinear controller is presented and applied to a planar 2-DOF parallel manipulator with redundant actuation. The robust nonlinear controller is designed by combining the nonlinear PD (NPD) control with the robust dynamics compensation. The NPD control is used to eliminate the trajectory disturbances, unmodeled dynamics and nonlinear friction, and the robust control is used to restrain the model uncertainties of the parallel manipulator. The proposed controller is proven to guarantee the uniform ultimate boundedness of the closed-loop system by the Lyapunov theory. The trajectory tracking experiment with the robust nonlinear controller is implemented on an actual planar 2-DOF parallel manipulator with redundant actuation. The experimental results are compared with the augmented PD (APD) controller, and the proposed controller shows much better trajectory tracking accuracy.     

Augmented Nonlinear PD Controller for a Redundantly Actuated Parallel Manipulator

In order to improve trajectory tracking accuracy for a redundantly actuated parallel manipulator, a so-called augmented nonlinear PD (ANPD) controller based on the conventional dynamic controller is proposed in this paper. The ANPD controller is designed by replacing the linear PD in the augmented PD controller with a nonlinear PD algorithm. The stability of the parallel manipulator system with the proposed ANPD controller is proven using the Lyapunov stability theorem, and the ANPD controller is further proven to guarantee asymptotic convergence to zero of both the tracking error and error rate. The superiority of the ANPD controller is verified through trajectory tracking experiments of an actual 2-d.o.f. redundantly actuated parallel manipulator.     

基于遗传算法的模糊迭代学习控制算法

为了提高被控系统的控制精度及加快迭代域内的收敛速度,提出一种基于遗传算法的模糊PD型迭代学习控制算法。该算法通过模糊TSK模型设计迭代学习控制器,TSK模型中THEN部分的未知参数由遗传算法离线计算确定,进而产生合理的迭代学习律。针对被控系统,设计相应的迭代学习控制算法进行仿真分析,并与传统PD型迭代学习控制算法、模糊PID迭代学习控制算法相比较,进而将该算法用于双关节机械手进行仿真研究,仿真结果表明该算法的有效性。     

柔性空间机械臂的闭环方波序列控制

将“输入成形”方法加以推广,提出一种方波序列控制,并利用幅值调制的思想,提出了一种闭环方波序列控制方法,在保证无余振的前提下,实现了柔性空间机械臂的高精度机动控制,并对系统惯量不确定性具有较强的鲁棒性,理论和仿真结果表明,该方法是有效的。     

基于遗传算法的柔性机械臂的同时优化设计

针对单连杆柔性机械臂系统,采用同时设计的方法,对包含柔性机械臂结构参数、传感器参数和控制器参数的系统模型进行整体优化设计,改进的遗传算法用于参数的全局寻优.仿真结果显示,优化设计后的单连杆柔性机械臂为变截面梁,可以仅采用简单的控制器(PD控制器)达到减小梁末端振动的效果.     

Active vibration control of a flexible beam,based on flow source control

This paper presents a new method of actively controlling the vibration of a flexible beam by using a rigid body motion actuator based on flow source control. The proposed flow source controller generates a control input of a rotating angle instead of a torque that acts as an effort source control input. It is shown that the proposed flow source control improves the vibration suppression performance when disturbance forces such as friction forces are involved in the rigid body motion dynamics. The stability and the robustness to disturbance of the flow source controller are compared with an effort source controller. An optimal control theory is used to design the flow source vibration controller and a conventional PD controller is used for the motor position controller. Computer simulations and experimental results on a rotating beam system show that the vibration control performance achieved by the proposed flow source control method is superior to that of an effort source control method.     

Dynamic Modeling and Inverse Optimal PID with Feed-forward Control in <Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> Framework for a Novel 3D Pantograph Manipulator

This paper affords dynamic modeling and control for a new 3D pantograph manipulator. The new manipulator possesses pure decoupled translational motions and it is characterized by large workspace to size ratio, high speed, rigidity, and accuracy. Euler-Lagrange first type method is used to get the dynamic model. However, the resulted dynamic model is too complex to be used in model-based control techniques. Therefore, a simplified nominal plant is proposed. It allows the inverse dynamic solution efficiently. However, an explicit form of the nominal Coriolis and centrifugal matrix cannot be obtained due to the complicated kinematic terms. Considering these dynamic characteristics as well as the required robust trajectory tracking performance of the manipulator, a new controller is proposed. The new controller is called inverse optimal PID with Feed-Forward Control which is designed in H_∞ framework. The new controller has the following merits; robustness, optimality, simple implementation, and efficient execution without the need of explicit forms of dynamic matrices. The extended disturbance in the proposed controller is smaller than that in the inverse optimal PID control (IPID) and contains one type of error contrary to the nonlinear robust motion controller (NRIC). The performance of the proposed controller is compared with those of IPID and NRIC controllers for different trajectories and payloads. The dynamic simulation results via co-simulation of MSC-ADAMS® and MATLAB®/Simulink software prove the robustness of the proposed controller against speed/payload variations. The proposed controller is found to have higher performance compared with IPID and NRIC controllers. These results assure the feasibility of the 3D pantograph manipulator with the proposed controller for pure translational tracking applications.     

移动机械手的T-S模糊控制系统

对移动机械手进行动力学分析,给出了统一的移动机械手整体动力学建模方法;针对移动机械手的非线性特点,提出T-S模糊控制算法,将复杂的非线性强耦合系统转化为若干线性问题的组合;利用线性矩阵不等式方法为该系统设计了状态反馈控制器,并给出了整体系统的稳定性证明。仿真实验证明该控制器具有良好的控制效果和稳定性。     

Compound control of a compound cosine function neural network and PD for manipulators

In this paper, a compound cosine function neural network controller for manipulators is presented based on the combination of a cosine function and a unipolar sigmoid function. The compound control scheme based on a proportional-differential (PD) feedback control plus the cosine function neural network feedforward control is used for the tracking control of manipulators. The advantages of the compound control are that the system model does not need to be identified beforehand in the manipulator control system and it can achieve better adaptive control in an on-line continuous learning manner. The simulation results for the two-link manipulator show that the proposed compound control has higher tracking accuracy and better robustness than the conventional PD controllers in the position trajectory tracking control for the manipulator. Therefore, the compound cosine function neural network controller provides a novel approach for the manipulator control with uncertain nonlinear problems.