Robust exponential stabilization of uncertain nonholonomic chained systems based on visual feedback

The visual serving stabilization for a kind of nonholonomic mobile robots with uncalibrated camera parameters is investigated based on the visual feedback and the state and input transformations. The authors obtain a new uncertain model of the nonholonomic kinematic system in the image plane,which is a chained form with uncalibrated visual parameters,from the camera robotic system. A new time varying feedback controller is proposed for the exponential stabilization of the nonholonomic chained system with unknown parameters by using state-scaling and switching technique.The exponential stability of the closed loop system is rigorously proved.Simulation results demonstrate the effectiveness of the proposed methods.     

Autotuning algorithm of particle swarm PID parameter based on D-Tent chaotic model

An improved particle swarm algorithm based on the D-Tent chaotic model is put forward aiming at the standard particle swarm algorithm. The convergence rate of the late of proposed algorithm is improved by revising the inertia weight of global optimal particles and the introduction of D-Tent chaotic sequence. Through the test of typical function and the autotuning test of proportionalintegral-derivative （PID） parameter, finally a simulation is made to the servo control system of a permanent magnet synchronous motor （PMSM） under double-loop control of rotating speed and current by utilizing the chaotic particle swarm algorithm. Studies show that the proposed algorithm can reduce the iterative times and improve the convergence rate under the condition that the global optimal solution can be got.     

New judging model of fuzzy cluster optimal dividing based on rough sets theory

To investigate the judging problem of optimal dividing matrix among several fuzzy dividing matrices in fuzzy dividing space, correspondingly, which is determined by the various choices of cluster samples in the totality sample space, two algorithms are proposed on the basis of the data analysis method in rough sets theory: information system discrete algorithm (algorithm 1) and samples representatives judging algorithm (algorithm 2). On the principle of the farthest distance, algorithm i transforms continuous data into discrete form which could be transacted by rough sets theory. Taking the approximate precision as a criterion, algorithm 2 chooses the sample space with a good representative. Hence, the clustering sample set in inducing and computing optimal dividing matrix can be achieved. Several theorems are proposed to provide strict theoretic foundations for the execution of the algorithm model. An applied example based on the new algorithm model is given, whose result verifies the feasibility of this new algorithm model.     

Model algorithm control using neural networks for input delayed nonlinear control system

The performance of the model algorithm control method is partially based on the accuracy of the system’s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to "learn"the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.     

Disturbance observer based time-varying sliding mode control for uncertain mechanical system

It is now well known that the time-varying sliding mode control (TVSMC) is characterized by its global robustness against matched model uncertainties and disturbances. The accurate tracking problem of the mechanical system in the presence of the parametric uncertainty and external disturbance is addressed in the TVSMC framework. Firstly, an exponential TVSMC algorithm is designed and the main features are analyzed. Especially, the control parameter is obtained by solving an optimal problem. Subsequently, the global chattering problem in TVSMC is considered. To reduce the static error resulting from the continuous TVSMC algorithm, a disturbance observer based time-varying sliding mode control (DOTVSMC) algorithm is presented. The detailed design principle and the stability of the closed-loop system under the composite controller are provided. Simulation results verify the effectiveness of the proposed algorithm.     

Reliability and Numerical Analysis of a Robot Safety System

This paper discusses a model for a repairable robot safety system composed of a safety component and two redundant robots according to a semi-group approach. The development of the model is divided into three phases:(Ⅰ) The use of pure analysis to prove the uniqueness of a classical solution to the model;(Ⅱ) The verification of this uniqueness by providing a non-negative solution to the model; and(Ⅲ) the formulation of reliability indices for the model, along with numerical examples to explain the results. The existence, uniqueness, and exponential stability of the solution of the repairable robot safety system is studied using Volterra-type integral-differential-equation theory, and the spectral distribution of the ACP operator is discussed. Moreover, the authors propose a method to investigate the steady-state indicator of the robot safety system with the safety component working, and with or without the component revolving, to make use of the correspondence of the eigenvector to the eigenvalue zero. The authors show that the robot's operation, with or without the safety component revolving and with fractional motion, is superior to that with the safety component working. The novelty of the approach lies in its examination of a unique solution to the system and its exponential stability based on C_0-semi-group theory, the co-final relative theory, and the functional analysis method.     

Degradation algorithm of compressive sensing

The compressive sensing(CS) theory allows people to obtain signal in the frequency much lower than the requested one of sampling theorem.Because the theory is based on the assumption of that the location of sparse values is unknown,it has many constraints in practical applications.In fact,in many cases such as image processing,the location of sparse values is knowable,and CS can degrade to a linear process.In order to take full advantage of the visual information of images,this paper proposes the concept of dimensionality reduction transform matrix and then selects sparse values by constructing an accuracy control matrix,so on this basis,a degradation algorithm is designed that the signal can be obtained by the measurements as many as sparse values and reconstructed through a linear process.In comparison with similar methods,the degradation algorithm is effective in reducing the number of sensors and improving operational efficiency.The algorithm is also used to achieve the CS process with the same amount of data as joint photographic exports group(JPEG) compression and acquires the same display effect.     

Algorithm of sky-ground-wave signal separation in CDMA system

To solve the problem of the sky-wave interference in radio positioning system operating in CDMA mode, an algorithm of sky-ground-wave separation is provided. Based on the MLE （maximum likelihood estimate）, and by estimating the amplitude and the phase of the sky-wave signal, the provided algorithm for separating skyground-wave is implemented. The mathematics model used for signal processing is established, and the possible solutions are provided. The structure and signal processing flow implementing the presented algorithm in the receiver are presented. A multi-channels signal searching idea is adopted, some of which process the sky-wave signal, and some of which process the ground-wave signal. Numerical analysis and simulation show that the proposed algorithm has higher accuracy, more rapid processing speed, and simpler implementation for the estimation of the sky-wave signal parameter, and can separate the sky-wave signal and ground-wave signal from the arrival combination signal effectively.     

图像反馈机器人视觉伺服系统仿真

机器人视觉伺服系统的研究是机器人领域中的重要内容之一，其研究成果可以直接用于机器人自动避障、轨线跟踪和运动目标跟踪等问题中。分析了基于图像雅克比矩阵的机器人视觉伺服方法的基本原理，通过创建子系统(sub-system)使得Marlab和Simulink有机结合，基于机器人Matlab仿真工具箱(Robotics Toolbox for Matlab)实现了六自由度Motoman-SV3工业机器人图像反馈视觉伺服系统的Simulink模型。采用该模型进行了机器人跟踪三维空间螺旋运动目标的仿真实验，结果验证了该方法的有效性。     

基于图像的直接视觉伺服控制器设计与仿真

对于手眼机器人视觉伺服,考虑机器人的运动学及动力学特性,在物体的深度未知及机器人的基坐标系与物体的坐标系之间的相似变换矩阵未定标的情况下,设计了直接视觉伺服控制器,利用自适应算法对深度及上述相似变换矩阵的参数进行在线估计,实现了基于图像误差的定位控制,在系统的工作空间范围内,闭环系统是渐近可稳定的。该方法不需要物体的几何模型及深度的精确值,仿真结果验证了其有效性。     

具有一种新的图像雅可比矩阵估计方法的无定标视觉伺服

对于基于图像的具有手眼或固定摄像机模式的视觉伺服,考虑机器人的运动学及动力学特性,在摄像机的内外参数未知的情况下,提出了一种直接自适应控制方案实现位置控制或轨迹跟踪;利用矩阵变换的方法使得图像雅可比矩阵的组成元素线性地出现在闭环动力学方程中,从而设计了一种新的自适应估计方法在线估计图像雅可比矩阵;利用李亚普诺夫的方法证明了图像误差的渐近收敛性。仿真结果验证了该方法的有效性。     

机器人/视觉系统非标定的平面运动跟踪

研究机器人与未标定关系的视觉传感系统之间的协调控制策略，其中目标运动及机器人跟踪运动均限定在二维工作平面。摄像机固定在工作区域中，它与机器人坐标系和工作平面之间的关系未知。机器人运动由摄像机观察到的上一控制周期中规划的手运动及目标当前位置决定，机器人与视觉系统之间的协调与它们两者之间的标定关系无关。文末的仿真显示了这一方法的有效性。     

机器人视觉伺服控制半物理仿真系统的研制

为了模拟机器人在自由运动的工作状态,验证实际物理手眼视觉伺服控制算法的效果,在4台PC机的基础上,用三维图形库OpenGL开发了仿真应用软件,组建了机器人视觉伺服控制半物理仿真系统。该仿真系统利用计算机三维图形学、机器人运动学和动力学、视觉测量以及视觉伺服控制等技术,成功仿真了在空间自由运动状态中,机器人对目标物体的捕获。     

基于神经网络的自由漂浮空间机器人鲁棒控制

针对带有模型误差及外界扰动的自由漂浮空间机器人轨迹跟踪问题,提出了一种基于神经网络的自适应鲁棒控制策略。采用对神经网络状态空间进行划分后与滑模变结构结合的控制器,对不确定非线性进行自适应学习,逼近误差作为外部干扰由鲁棒控制器消除。该方法从整个闭环系统的稳定性出发,利用H_∞理论设计的鲁棒控制器及神经网络权值的在线调整规则保证了系统的稳定性,并能使系统L2增益小于给定的指标,具有较好的控制精度及动态特性。仿真分析进一步证明了该自适应鲁棒控制算法的有效性。     

改进最小二乘算法在天文定位中的应用

运用数字天顶仪进行天文定位时，需采用最小二乘算法建立电荷耦合元件图像坐标系和天球切平面坐标系之间的映射关系。针对最小二乘算法只考虑了观测量中的误差，没有顾及系数矩阵中的误差和数据中可能存在的粗大误差问题，为了提高数字天顶仪进行天文定位时的解算精度，将最小二乘算法与总体最小二乘算法进行有效结合构成了混合最小二乘算法，该算法能够同时顾及坐标转换中系数矩阵和观测量中的误差。为了消除识别恒星数据中可能存在的粗大误差对天文解算的影响，对所提算法进行了稳健加权，并结合星等设置合理的权阵。数据分析的结果证明，稳健加权混合最小二乘算法在进行天文解算时具有较高的精度。     

基于辅助匹配的1点RANSAC单目视觉导航算法

针对1点随机抽样一致性(random sample consensus,RANSAC)单目视觉导航算法中的主动视觉匹配失效问题,提出了一种基于辅助匹配的1点RANSAC单目视觉导航算法。首先,该算法通过引入尺度不变特征变换(scale invariant feature transform,SIFT)算法完成特征匹配;其次,采用RANSAC算法解算基础矩阵和匹配点;最后,通过实验验证了算法的有效性。实验结果表明,该算法能够解决主动视觉匹配失效问题,提高1点RANSAC单目视觉导航算法的导航精度。SIFT辅助求解的有效匹配点精度在5个像素之内,航向角平均误差减小5.04°,俯仰角平均误差减小1.21°,滚动角平均误差减小3.03°。     

微型足球机器人伺服控制实时仿真系统研究

基于dSPACE实时仿真技术,应用MATLAB/Simulink的系统建模方法和dSPACE系统的软硬件环境,设计开发了微型足球机器人伺服控制实时仿真系统。将实际受控对象-足球机器人放置在仿真系统回路中进行仿真研究,实时监控系统运行,在线对控制参数进行调整,并对系统的动态性能进行测试,利用测得的实验数据对机器人小车模型进行了辨识,取得了较好的控制效果。仿真结果表明,该系统是一个很好的实验研究平台。     

基于SIFT匹配算法的移动机器人单目视觉定位研究

以单目视觉为模型给出基于视觉的移动机器人目标定位系统算法,利用尺度不变特征变换匹配方法(SIFT)求得基本矩阵与本质矩阵,分解本质矩阵后进一步得到相机的运动参数,最后结合立体视觉方法以及运动恢复结构方法获得目标的深度信息,实现目标定位。给出了三维信息获取过程与计算机仿真结果。实验结果表明,算法能满足移动机器人对目标实现定位的精度要求。