基于一阶不确定项估计律的机械臂鲁棒控制研究

针对多自由度机械臂控制系统的模型参数误差、关节摩擦力以及外部输入扰动等不确定项,设计了一类一阶误差估计律;结合基于机构动力学名义模型的输入输出反馈线性化控制算法,对六自由度刚性机械臂的时变轨迹跟踪控制进行了研究,理论上证明了设计的鲁棒控制器是全局渐进稳定的.仿真结果表明该控制策略对系统的各类不确定项具有很好的鲁棒性,能够实现高精度的轨迹跟踪控制.     

基于外力估计与位置的阻抗控制算法实现

针对七自由度的协作机械臂,本文介绍了一种基于无力传感器外力估计的基于位置的阻抗控制算法,实现了曲面的恒力跟踪。通过实验证明,该阻抗控制算法不仅可对外力进行精确的估计,而且可以控制机械臂末端平稳跟随给定曲面,力跟踪精度也达到了预期目标,具有较强的实用价值。     

含有迟滞Hammerstein系统的非光滑卡尔曼状态估计

迟滞特性具有非光滑、多值映射等复杂特性.如果迟滞环节的末端还存在一个线性子系统,导致迟滞的输出信号不可测,使得整个系统的状态估计工作成为很大的难题,常规的估计方法无法直接应用到这类系统中.本文提出一种新的非光滑卡尔曼滤波器,描述了Hammerstein系统的状态空间方程.据此构造了能够随系统工作区间变化而自动切换的非光滑滤波器.最后通过仿真和实验,比较了非光滑卡尔曼滤波器和传统的卡尔曼滤波器的状态估计效果,比较结果表明非光滑卡尔曼滤波器对于带迟滞的Hammerstein系统状态变量的估计的准确性要优于传统的卡尔曼滤波器.     

参数不确定系统的H∞估计问题的显式解和中心解

研究在连续时间情形下的具有部分参数不确定性的系统的H∞状态估计问题,它可 以被化简为带有一个自由可调参数对象的H∞状态估计,由此可得到滤波器的简洁通解显 式.并进一步研究了H∞估计的中心解,以及它与卡尔曼滤波器的关系.实例计算表明,对于 参数具有不确定性的系统,H∞滤波器的性能明显地优于卡尔曼滤波器.     

基于改进卡尔曼滤波器的扰动抑制无模型自适应控制方案

针对无模型自适应控制方法在测量扰动作用下控制效果不佳的问题, 本文提出了一种新的扰动抑制无模 型自适应控制方案. 首先基于受控系统的动态线性化数据模型及测量扰动的统计特性, 在最小方差估计准则下推导 了基于系统输入输出数据的改进卡尔曼滤波器. 然后基于此滤波器给出了一种新的扰动抑制无模型自适应控制方 案. 该方案仅需用到受控系统的输入输出数据, 即可实现在强测量扰动作用下系统的无模型自适应控制. 仿真结果 显示, 相比现有的扰动抑制无模型自适应控制方案, 该方案在系统跟踪常值参考信号、时变参考信号时均能有效地 抑制测量扰动, 适用性更好的同时可以获得更小的跟踪误差及更大的数据信噪比.     

改进粒子群算法优化扩展卡尔曼滤波器电机转速估计

针对感应电机扩展卡尔曼滤波器转速估计中难以取得卡尔曼滤波器系统噪声矩阵和测量噪声矩阵最优值的问题, 提出了一种基于改进粒子群算法优化的扩展卡尔曼滤波器转速估计方法。算法通过融合遗传算法和粒子群算法的优点, 采用可调整的算法模型对粒子群算法进行改进, 将改进的粒子群算法对扩展卡尔曼滤波器中的系统噪声矩阵和测量噪声矩阵进行优化处理, 将优化后的卡尔曼滤波器应用于感应电机转速估计。仿真实验表明, 与试探法、标准粒子群算法及遗传算法比较, 改进粒子群算法优化的扩展卡尔曼滤波器能够有效提高转速估计的精度, 从而提高无速度传感器矢量控制系统的控制性能。     

噪声统计特性不确定下的卫星姿态估计

姿态确定算法是卫星姿态确定系统的重要组成部分。在姿态确定系统中广泛采用卡尔曼滤波作为姿态确定算法，但是卡尔曼滤波依赖于噪声统计特性的先验知识，采用不精确的噪声统计特性设计卡尔曼滤波器可能会导致较大的估计误差，甚至造成滤波发散。本文针对噪声统计特性的不确定性分别采用了自适应卡尔曼滤波器和预测滤波器估计卫星姿态，通过数学仿真验证在噪声统计特性不确定的情况下。这两种滤波器仍然可以较精确地估计卫星姿态。     

基于时间延时估计和自适应模糊滑模控制器的双机械臂协同阻抗控制

多机械臂的精准协同控制已成为当前机器人领域的研究难点,为实现双机械臂精准控制,通过建立双机械臂动力学模型,采用时间延时估计简化机械臂动力学模型,在保证控制系统稳定性的前提下,引入自适应模糊滑模控制器实现对估计误差的修正和补偿,设计基于时间延时估计和自适应模糊滑模控制的双机械臂协同阻抗控制器,实现双机械臂协同操作的末端轨迹控制以及接触力精准控制.最后,将该控制器应用于两台六自由度机械臂仿真平台,实现双臂夹取和搬运同一目标物体的操作,通过与其他控制器进行对比实验,表明所设计的控制器具有响应快、无抖震、精度高的特点.     

基于干扰观测器的机械臂广义模型预测轨迹跟踪控制

为实现对多自由度机械臂关节运动精确轨迹跟踪,提出一种基于非线性干扰观测器的广义模型预测轨迹跟踪控制方法。针对机械臂轨迹跟踪运动学子系统,采用广义预测控制（Generalized Predictive Control,GPC）方法设计期望的虚拟关节角速度。对于机械臂轨迹跟踪动力学子系统,考虑机械臂的参数不确定性和未知外界扰动,利用GPC方法设计关节力矩控制输入,基于非线性干扰观测器方法实时估计和补偿系统模型中的不确定性。在李雅普诺夫稳定性理论框架下证明了机械臂关节角位置和角速度的跟踪误差最终收敛于零的小邻域。数值仿真验证了所提出控制方法的有效性和优越性。     

3自由度旋翼飞行机械臂系统动力学建模与预测控制方法

旋翼飞行机械臂(rotorcraft aerial manipulator,RAM)系统是安装在飞行机器人上的可操作型机械臂,悬停模式下执行准确的空中操作时旋翼无人机与所加机械臂之间存在相对扰动,通过分离机械臂与飞行机器人进行动力学建模并不能有效消除这种扰动.本文基于对相互扰动力学作用的分析建立整体动力学模型,并在悬停飞行模式下将其简化为线性控制参考模型.进而对旋翼系统控制延时所引起的动力学扰动进行补偿,同时设计预测控制器来消除末端执行器的位置和姿态误差.最后,在存在内部和外部扰动的情况下,设定销钉插入操作任务进行控制方法的对比仿真.末端执行器位姿偏差的仿真结果表明了模型结构与控制方法的有效性.     

Sensorless force estimation for industrial robots using disturbance observer and neural learning of friction approximation

Contact force estimation enables robots to physically interact with unknown environments and to work with human operators in a shared workspace. Most heavy-duty industrial robots without built-in force/torque sensors rely on the inverse dynamics for the sensorless force estimation. However, this scheme suffers from the serious model uncertainty induced by the nonnegligible noise in the estimation process. This paper proposes a sensorless scheme to estimate the unknown contact force induced by the physical interaction with robots. The model-based identification scheme is initially used to obtain dynamic parameters. Then, neural learning of friction approximation is designed to enhance estimation performance for robotic systems subject with the model uncertainty. The external force exerted on the robot is estimated by a disturbance observer which models the external disturbance. A momentum observer is modified to develop a disturbance Kalman filter-based approach for estimating the contact force. The neural network-based model uncertainty and measurement noise level are analysed to guarantee the robustness of the Kalman filter-based force observer. The proposed scheme is verified by the measurement data from a heavy-duty industrial robot with 6 degrees of freedom (KUKA AUGLIS six). The experimental results are used to demonstrate the estimation performance of the proposed approach by the comparison with the existing schemes.     

Application of Kalman filters to the regulation of dead time processes

A single-input single-output control algorithm for a process with dead time and dead time uncertainty is described. The process dynamics consist of first-order mixing and pure delay with the pure delay being dominant. The process is disturbed at the upstream end by a disturbance sequence consisting of white noise passed through a first-order shaping filter. The process output is subject to white measurement noise. A discrete Kalman filter is used to produce state estimates for the disturbance mixing and dead time states which are updated from the process output residual error. In order to handle dead time uncertainty of up to a priori established limits, the residual error is passed through a dynamic dead band whose magnitude is a function of the dead time states of a separate process dynamic model driven by the process input. The dead band eliminates dead time error components from the residual. Control is achieved by state feedback from the upstream Kalman filter state estimate. The algorithm is in use on paper machine and bleach plant control applications and gives near minimum variance performance when properly tuned.     

Adaptive Iterated Extended KALMAN Filter for Relative Spacecraft Attitude and Position Estimation

This paper presents a novel adaptive iterated extended Kalman filter (AIEKF) for relative position and attitude estimation, taking into account the influence of model uncertainty. Considering a nonlinear stochastic discrete‐time system with unknown disturbance, the AIEKF algorithm adopts the Gauss‐Newton iterative optimization steps to implement a maximum a posteriori (MAP) estimation, and the switch‐mode combination technique is used to achieve the adaptive capability. The mean‐square estimation error (MSE) of the state estimate is derived. It is proved that the AIEKF can yield a smaller MSE than that of the traditional extended Kalman filter (EKF) or iterated extended Kalman filter (IEKF). The performance advantage of the AIEKF is illustrated via Monte Carlo simulations on a typical relative position and attitude estimation application. Through comparisons in different scenarios, the presented algorithm is shown to improve adaptability and ensure estimation accuracy.     

COM motion estimation of a biped robot based on kinodynamics and torque equilibrium

A novel technique to estimate motion of the center of mass (COM) for a biped robot is proposed. A Kalman filter is synthesized where the time evolution of COM is predicted from the external force and corrected based on kinematic estimation and torque equilibrium. They complementarily work to compensate the initial estimation offset, the error accumulation, and errors in modeled mass properties. It makes use of the authors’ previous method to estimate the translational and rotational motion of the base body from inertial information and joint angle measurements. The information about torque equilibrium helps to reduce an uncertainty of the height of COM and to improve the estimation accuracy of it by utilizing an interference of the horizontal and vertical motion of COM. The parameters are tuned based on error analyses in mass properties and sensor signals. A comparative study showed a better performance of the proposed method than other methods through dynamics simulations.     

基于间歇测量的状态估计稳定性和精度研究*

针对分布式控制系统状态估计过程出现的测量值丢失问题,本文量化分析测量值丢失对估计稳定性和精度的影响。首先,根据随机测量模型重新推导带测量值丢失的卡尔曼滤波器,得到估计误差协方差迭代式。然后,将随机误差协方差迭代式建模为修正的黎卡提微分方程,提出了估计稳定性和精度分析方法。最后,列举不同的系统实例,证明临界包到达率的存在性,当测量值到达率大于临界值时,平均误差协方差从发散过渡到有界,进而得到估计精度与包达率之间的函数关系。     

A Delay‐Dependent Approach to Robust Fast Adaptive Fault Estimation Design for Uncertain Neutral Systems with Time‐Varying Interval Delay

This paper studies the problem of robust fault estimation for neutral systems, which are subjected to uncertainties, actuator fault, time‐varying interval delay, and norm‐bounded external disturbance. Based on the fast adaptive fault estimation (FAFE) algorithm, we focus on the design of a fault estimation filter that guarantees stability in the filtering error system with a prescribed H_∞ performance. A novel Lyapunov‐Krasovskii functional is employed, which includes time delay information. A delay‐dependent criterion of robust fault estimation design is obtained by employing the free‐weighting matrices technique, and the proposed result has advantages over some existing results, in that it is less conservative and it enlarges the application scope. An improved sufficient condition for the existence of such a filter is proposed in terms of the linear matrix inequality (LMI) by the Schur complements and the cone complementary linearization algorithm. Finally, illustrative examples are provided to show the effectiveness of the proposed method.     

鲁棒卡尔曼滤波下的图像雅可比矩阵带时延补偿的估计

传统的图像雅可比矩阵估计的方法没有考虑时延因素,因此具有较大的估计误差.为补偿时延带来的影响,提出一种鲁棒卡尔曼滤波的方法,实现时延情况下当前时刻特征点在图像空间中位置和速度的估计,进而得到时延情况下较为准确的图像雅可比矩阵的估值.具体说,特征点在图像空间中当前时刻位置和速度是首先用卡尔曼滤波的方法估计的,但观测噪声的描述却采用了马尔科夫链模型,由此产生了过程噪声和观测噪声的互相关,传统卡尔曼滤波受限.为此,我们引入滤波修正向量并重新定义过程方程及观测方程,结合卡尔曼滤波中噪声的数学特性,得到滤波修正向量消除互相关性,从而构建出鲁棒卡尔曼滤波模型;其次,针对鲁棒卡尔曼滤波模型中存在的无法获得时延期间的观测向量的问题,提出利用多项式拟合出这部分观测向量,该多项式的选取综合考虑了特征点的位置、速度、加速度、加速度的变化率对于特征点轨迹的影响,与实际情况相符;最后,由预测出的当前时刻特征点在图像空间中的位置和速度,实现时延情况下图像雅可比矩阵较为准确的估计.仿真和实验结果验证了本文方法的可行性和优越性.     

基于旋转矩阵KF的低成本MEMS姿态解算

针对低成本MEMS器件组合的姿态检测系统在运动加速度干扰下姿态估计精度较差等问题,提出了一种基于旋转矩阵卡尔曼滤波器(KF)的姿态解算方法.为了克服四元数法观测方程为非线性的缺点,该方法以旋转矩阵部分元素建立状态方程,并对量测加速度采用状态反馈估计的运动加速度进行补偿,减小了外部加速度的干扰,然后通过构造水平观测向量降低了计算复杂度,并给出了量测噪声协方差的推导.最后设计了卡尔曼滤波器对量测信息实现融合.动静态测试表明,该方法消除了累计误差,与无迹卡尔曼滤波(UKF)相比,提高了在运动加速度干扰下的姿态估计精度.     

双目视觉基于修正卡尔曼滤波器的运动估计

研究了目标物体的远程运动估计.首先,建立了一种双目视觉系统的基于卡尔曼滤波器的目标物体运动估计的运动学模型,并且证明了双目视觉系统同步的各自连续两帧图像中至少三个对应图像点能完全确定刚性物体的运动参数和空间位置;然后,通过对状态向量中的速度分量进行再估计,提出了一种修正卡尔曼滤波器对目标物体远程运动估计的算法,与直接卡尔曼滤波器的远程运动估计相比,提高了估计的精度.将该方法运用到一种实时预测的实验中,其结果证明了该算法的有效性.