未知环境下基于模糊神经网络的机器人力控制研究

机器人力控制是机器人研究的一个热点和难点.在未知环境中为实现精确的接触力控制,需要力控制器能够适应环境的变化.本文将模糊神经网络引入到机器人的力控制中来适应未知环境的变化,首先用一个神经网络根据机器人对未知环境的动态响应来对未知环境进行分类,然后选择一个合适的模糊神经网络力控制器对其控制.实验研究表明所设计的控制器是可行和有效的.     

三自由度绳牵引平面并联机器人力/位控制研究

针对三自由度平面绳牵引并联机器人在如何保持一定绳张力的条件下实现位置控制的问题进行了仿真研究.利用牛顿-欧拉法建立了机器人的包括电机在内的动力学方程.考虑到系统正常工作时绳需要具有一定的张力,给出了力分解模型,提出了一种力/位并行控制策略:力回路用于控制绳的张力大小,位置回路用于实现末端轨迹跟踪.最后对机器人控制系统进行了仿真分析,仿真结果表明,该控制策略不仅能够解决绳的张力问题,而且满足对轨迹跟踪要求.     

未知时变环境下机器人自校正阻抗控制研究

针对未知、时变环境下机器人力位控制需求,基于非线性接触动力学模型进行了阻抗控制的位置自校正修正算法研究.通过建立基于Hunt-Crossley非线性接触动力学模型的阻抗控制系统,构建了在刚性或柔顺作业环境下机器人末端位置与接触力之间的动态关系;在自校正控制系统中引入自扰动递推最小二乘辨识算法,以接触力误差最小为设计指标...     

机器人跟踪未知环境的智能新策略

以Ａｄｅｐｔ－Ⅲ机器人跟踪玻璃灯泡未知表面为对象,综合运用了大系统、模糊数学和神经网络等智能理论,进行机器人力控制跟踪类依从运动研究,系统地提出了智能力／位并环控制新策略：即将复杂的机器人力控制作为一个大系统来研究,建立了机器人力控制大系统模型和相应的智能力／位并环控制的结构：在此基础上,进行了力／位并环混合运动学分析研究,建立了相应神经网络模型,并推导出力／位混合隶属函数,实现了力控制大系统的基于模糊协调的递阶控制算法。从理论上和技术实现上解决了机器人力控制跟踪类难题。     

未知环境下机器人导航算法与避障算法研究

针对机器人在复杂动态工作环境下如何进行定位和导航的问题做了深入研究,全文阐述了机器人混合导航和地图重建的基本方法。首先,介绍了机器人机械结构与软件平台,并分析了经典双轮差动模型,并总结了机器人的应用范围;然后,通过对视觉数据的数据采集实现了对于环境特征的提取,建立了导航图像和导航机器人导航;最后,建立了基于超声波传感器的模糊避障系统,解决了由于障碍物定位不准确、检测盲区、镜子反射等缺陷导致避障不稳定的问题。经过实验证明,该机器人具有良好的避障性能和导航性能,实现了机器人视觉导航算法。     

未知环境下机器人受限机械手PLC阻抗控制系统

机械手作为能够自动定位控制且可重新编程的多功能机器人,为提高生产效率,提出未知环境下机器人受限机械手PLC阻抗控制系统。分析机械手组成结构,结合理想惯性、黏性阻尼与刚度矩阵,构建阻抗模型;选用西门子S7-200PLC可编程逻辑控制器,明确其主要构成模块与优势;确定旋转编码器、光电传感器等控制系统主要硬件;建立物体空间坐标系,获取定向矩阵;结合协调控制思想,将物体与目标坐标系的六维位移通过空间平移、旋转、连接三种虚拟弹簧表示;计算各类弹簧的储存能量,分别得出弹簧形成的电机力矩,设置阻抗控制规律,针对控制策略设计软件控制程序,完成阻抗系统设计。仿真实验证明,该方法在未知环境下对阻抗具有良好的跟踪性能,能够实现稳定控制。     

振动自适应模糊控制方法研究

将自适应模糊控制理论引入振动控制工程领域,提出了一种基于模糊逻辑系统的在线自学习控制方法。给出了该控制方法的学习算法及初始振动模糊控制器的产生方法。分析了该方法与其它非线性控制方法（神经网络控制）相比所具有的优点。仿真结果表明该自适应模糊控制方法能有效地抑制振动。     

一种未知环境下室内移动机器人路径规划新算法

针对目前室内移动机器人的路径规划算法理论研究为主,实际应用较少的问题,通过建立了移动机器人的运动学模型,采用栅格化建模方法将室内环境进行了分块,再利用先锋机器人P3-DX及其配套的仿真软件MobileSim,设计并且成功应用了一种未知环境下室内移动机器人路径规划的新算法。该算法在结合了机器人宽度信息的基础上,进行了栅格化模型建模,采用"点到点"的路径规划方法。最后在MobilesSim和P3-DX真实机器人上分别进行了仿真和实验验证,研究结果表明,该方法有效,且具有很好的可操作性。     

未知环境下移动机器人自主感知斜坡地形方法

环境感知是实现移动机器人自主导航的基础与根本保证.为了正确感知斜坡地形,分析了其描述模型.根据移动机器人从不同位置观测斜坡时深度信息的变化趋势,确定移动机器人观测斜坡的方向.在此基础上,提出了应用径向基(RBF)神经网络强非线性逼近能力估算地形坡度值的新方法.实验结果表明,所提出的算法能满足移动机器人感知斜坡地形的要求,同时算法具有简单、准确、鲁棒性强的优点.     

含未知磁滞输入的机械臂系统预设性能控制

针对一类含有未知磁滞输入的单连杆机械臂系统,提出一种基于动态面的自适应预设性能控制方案。利用Bouc-Wen模型描述机械臂执行机构内的磁滞现象,通过RBF神经网络预估该系统内的未知函数,同时利用Funnel控制确保跟踪误差的最大超调量,据此按照动态面策略设计控制器以避免传统反演策略所固有的微分膨胀问题。理论分析表明该方案能有效消除未知磁滞的影响,能同时确保系统的瞬态及稳态性能,并能确保闭环系统内全部信号达到半全局有界。最后,通过仿真对该方案的有效性进行验证。     

On position/force tracking control problem of cooperative robot manipulators using adaptive fuzzy backstepping approach

In this paper, the position and force tracking control problem of cooperative robot manipulator system handling a common rigid object with unknown dynamical models and unknown external disturbances is investigated. The universal approximation properties of fuzzy logic systems are employed to estimate the unknown system dynamics. On the other hand, by defining new state variables based on the integral and differential of position and orientation errors of the grasped object, the error system of coordinated robot manipulators is constructed. Subsequently by defining the appropriate change of coordinates and using the backstepping design strategy, an adaptive fuzzy backstepping position tracking control scheme is proposed for multi-robot manipulator systems. By utilizing the properties of internal forces, extra terms are also added to the control signals to consider the force tracking problem. Moreover, it is shown that the proposed adaptive fuzzy backstepping position/force control approach ensures all the signals of the closed loop system uniformly ultimately bounded and tracking errors of both positions and forces can converge to small desired values by proper selection of the design parameters. Finally, the theoretic achievements are tested on the two three-link planar robot manipulators cooperatively handling a common object to illustrate the effectiveness of the proposed approach.     

A compliance control strategy for robot manipulators under unknown environment

In this paper, a compliance control strategy for robot manipulators that employs a self-adjusting stiffness function is proposed. Based on the contact force, each entry of the diagonal stiffness matrix corresponding to a task coordinate in the operational space is adaptively adjusted during contact along the corresponding axis. The proposed method can be used for both the unconstrained and constrained motions without any switching mechanism which often causes undesirable instability and/or vibrational motion of the end-effector. The experimental results involving a two-link direct drive manipulator interacting with an unknown environment demonstrates the effectiveness of the proposed method.     

A robust and adaptive force/position control for two cooperating robot arms under uncertainty

This paper presents a motion coordination of a two-cooperating robot arm when there are unknown system parameters and bounded input disturbances. The order of the model of the two-arm system is reduced. To control this, a force/position control scheme based on an inverse dynamics control scheme is devised. On the top of the control scheme, an adaptive control scheme to take care of parametric uncertainties, and a robust control scheme to compensate coupling forces between two arms and input disturbances are devised. The adaptive and the robust control scheme are derived based on a devised Lyapunov function. The adaptive control algorithm is practical since it does not require the feedback of the second derivative of joint angles and interacting forces. The robust control scheme guarantees that the tracking error of the leader arm and the interacting forces between two arms are confined in a certain region. Numerical examples using dual 3 degree of freedom robot arm are shown.     

Sonar based position estimation system for an autonomous mobile robot operating in an unknown environment

The paper presents a new method of position estimation for robot navigation in a completely unknown environment. The method is different from conventional ones in that it does not need any kind of a priori reference model or man-made landmarks. A series of local maps is built and updated from sonar data while the robot is exploring the unknown environment. Among the constructed local maps, the robot autonomously selects the ones with distinctive features and memorizes them as reference landmarks. An orientation clustering method is developed which enables the robot to extract the features of the map. The maps selected in such a way are then used to estimate the position and orientation of the robot while undertaking the given task. In doing so correspondence indices are defined to determine the corresponding reference map to the current local one among the numerous stored maps. The two maps are matched so as to minimize the discrepancy between them, thus enabling one to estimate the position and orientation of the robot. The usefulness of all these approaches is illustrated with the results produced by a real robot equipped with ultrasonic sensors.     

柔性臂空间机器人的神经网络自适应控制及振动模态分级模糊控制

由于太空中的在轨作业一般要求空间机器人具有手臂长、负载大,对空间机器人动力学及控制进行分析时必须考虑到机械臂杆件的柔性问题。为此探讨了参数未知柔性臂空间机器人载体姿态、关节协调运动控制及柔性振动抑制问题。由系统动量守恒关系及假设模态法,利用Lagrange方法建立了漂浮基柔性臂空间机器人的系统动力学方程。运用奇异摄动理论的双时间刻度分解,导出了适用于控制系统算法设计的奇异摄动数学模型。利用该模型,针对慢时标子系统——等价刚性空间机械臂,设计了系统参数不确定情况下的径向基函数神经网络补偿控制算法,以控制柔性臂空间机器人的载体姿态及机械臂关节铰协调地来完成各自在关节空间的期望运动;神经网络控制算法的研究目的是基于神经网络良好的在线自学习能力,大大提高整个系统的控制精度。针对快时标子系统——柔性臂的振动,设计了分级模糊控制算法来主动抑制柔性杆的振动。分级模糊控制算法的研究目的是为了减少模糊规则库的大小,有效提高模糊控制器的计算效率。通过计算机仿真验证了提出方法的有效性和可行性。     

A robot fuzzy motion planning approach in unknown environments

A fuzzy robot motion planning approach is proposed in unknown environments for three-degree industrial robots. The proposed planning system is composed of several separate fuzzy units, which control individually each manipulator joint. Each unit combines a repelling influence, which is related to the nearby obstacle, with the attracting influence produced by the final manipulator configuration, to generate actuating command for each link. Effectiveness of the proposed approach is verified through simulation. __________ Translated from Journal of Harbin Institute of Technology, 2005, 37(10) (in Chinese)     

不确定机器人鲁棒自适应控制

针对不确定、时变和非线性机器人系统的实时性要求,提出了采用滑模变结构和RBF神经网络相结合来构造控制器.用带有符号函数的滑模变结构控制器来产生一个控制输入信号,同时利用具有快速学习能力的RBF神经网络来学习外界的不确定性,增强系统的自适应能力,使之达到更佳的控制效果,并在文中证明了系统的稳定性.最后给出了对两连杆机器人的仿真,验证了控制效果.     

Visual feedback control of a robot in an unknown environment (learning control using neural networks)

In this paper, a visual feedback control approach based on neural networks is presented for a robot with a camera installed on its end-effector to trace an object in an unknown environment. First, the one-to-one mapping relations between the image feature domain of the object to the joint angle domain of the robot are derived. Second, a method is proposed to generate a desired trajectory of the robot by measuring the image feature parameters of the object. Third, a multilayer neural network is used for off-line learning of the mapping relations so as to produce on-line the reference inputs for the robot. Fourth, a learning controller based on a multilayer neural network is designed for realizing the visual feedback control of the robot. Last, the effectiveness of the present approach is verified by tracing a curved line using a 6-degrees-of-freedom robot with a CCD camera installed on its end-effector. The present approach does not necessitate the tedious calibration of the CCD camera and the complicated coordinate transformations. This revised version was published online in October 2004 with a correction to the issue number.     

未知环境下移动机器人实时模糊路径规划

针对机器人在未知环境下避障的要求,设计了一个结合目标点位置信息和局部障碍物信息的评价函数实时导航算法,使机器人可以在无碰撞的情况下较快地到达目标点。该算法以二维激光雷达作为探测环境的主要传感器,采用基于行为的控制策略及模糊控制器技术,通过添加自由出口提高其路径搜寻的效率,最后为了验证此模糊路径规划方法进行了仿真实验。仿真实验结果表明,该算法具有良好的效果,机器人可以有效避障,且轨迹平滑、实时性好。     

Fuzzy force control of constrained robot manipulators based on impedance model in an unknown environment

To precisely implement the force control of robot manipulators in an unknown environment, a control strategy based on fuzzy prediction of the reference trajectory in the impedance model is developed. The force tracking experiments are executed in an open-architecture control system with different tracking velocities, different desired forces, different contact stiffnesses and different surface figurations. The corresponding force control results are compared and analyzed. The influences of unknown parameters of the environment on the contact force are analyzed based on experimental data, and the tunings of predictive scale factors are illustrated. The experimental results show that the desired trajectory in the impedance model is predicted exactly and rapidly in the cases that the contact surface is unknown, the contact stiffness changes, and the fuzzy force control algorithm has high adaptability to the unknown environment. Translated from Journal of Northeastern University (Natural Science), 2005, 26(8): 766–769 [译自: 东北大学学报 (自然科学版)]