一种新的角加速度估计方法及其应用

首先提出了一种新的基于卡尔曼滤波及牛顿预测的角加速度估计方法,在已知电机驱动系统位置信息的情况下,利用卡尔曼滤波实时估计系统的角加速度;同时采用牛顿预测方法解决估计算法的滞后问题,进一步提高了估计加速度的响应频带．以此为基础,本文进一步分析了利用估计加速度进行反馈控制以增强系统对外扰动的鲁棒性问题,提出了加速度反馈控制策略的设计准则并分析了稳定性．在一个直接驱动机器人关节上针对上述加速度估计及控制方法进行了实验研究:将估计加速度的实验结果与实测加速度(利用加速度计)的实验结果进行了比较分析,从而定量地揭示出估计加速度及其反馈控制在实际系统中的可行性及有效性．     

冗余度机器人在关节坏死情况下的轨迹规划

本文在分析比较一些常见的机器人灵活度标准的基础上,提出一种轨迹规划算法,通过提高机器人关节坏死后的灵活度,解决了机器人在关节坏死后由于灵活度过低造成不可控的问题.分别对4 自由度连杆机器人和平面4 自由度宏--微机器人进行了仿真和实验研究,仿真和实验结果证明了算法的有效性.􀁱􀁽     

基于H∞的双足机器人关节控制方法研究

本文利用拉格朗日法建立了双足步行机器人的动力学模型,从中提取出干扰和摄动.通过分析和求解H∞状态反馈控制问题,设计了一个鲁棒控制器,该控制器既能使双足步行机器人控制系统对干扰不敏感,又对摄动保持稳定.得到H∞控制器后,给出了双足步行机器人的全局关节控制系统,并且对双足步行机器人进行了仿真实验,实验结果验证了控制方法的有效性.     

基于加速度传感器的下肢康复机器人示教训练

将康复治疗人员的临床治疗经验与康复机器人的训练结合,能够有效提高已有下肢康复机器人的训练性能,为此提出一种基于加速度传感器的示教训练方法及无线数据采集系统。通过已有的外骨骼式下肢康复机器人,分析并推导出关节角度与末端轨迹的关系及关节加速度与关节角度的关系。通过加速度信息分析出轨迹信息从而控制训练轨迹。研发的无线数据采集系统提取康复师训练时患者下肢加速度信息并转化成轨迹信息,进而被用于康复机器人示教训练控制。实验结果证明,该系统能够满足康复机器人的示教训练要求。     

工业机器人时间最优轨迹规划及轨迹控制的理论与实验研究

提出了一种用于工业机器人时间最优轨迹规划及轨迹控制的新方法, 它可以确保在关节位移、速度、加速度以及二阶加速度边界值的约束下, 机器人手部沿笛卡尔空间中规定路径运动的时间最短. 在这种方法中, 所规划的关节轨迹都采用二次多项式加余弦函数的形式, 不仅可以保证各关节运动的位移、速度、加速度连续而且还可以保证各关节运动的二阶加速度连续. 采用这种方法, 既可以提高机器人的工作效率又可以延长机器人的工作寿命. 以PUMA 5 6 0机器人为对象进行了计算机仿真和机器人实验, 结果表明这种方法是正确和有效的. 它为工业机器人在非线性运动学约束条件下的时间最优轨迹规划及控制问题提供了一种较好的解决方案.     

多关节机器人的一种非线性反馈控制方法

本文研究了一种基于高阶线性化近似方法的机器人非线性反馈控制问题。首先,给出了多关节机器人的动力学模型,研究了一般非线性系统相对于某一标称轨迹的高阶线性近似方法,建立了高阶反馈项系数的计算公式,然后,讨论了多关节机器人的二阶近似非线性反馈控制方案。最后,给出了数字仿真结果.     

辅助起立机器人反馈控制与速度前馈控制性能对比

对辅助起立机器人的位置控制方法进行了研究.在不同频率时,使用位置速度反馈控制对其进行位置控制测试,辅助起立机器人滑动关节的位置精度较好,但旋转关节位置精度较差.在反馈控制系统的基础上增加速度前馈控制之后进行测试,结果表明增加的速度前馈控制可以有效地提高辅助起立机器人两个运动关节的位置精度,其中旋转关节的位置精度明显改善.证明了在反馈控制的基础上增加速度前馈控制可以有效提高辅助起立机器人位置精度.     

爆炸箔起爆器参数对电爆性能的影响

为了避免机器人坡面行进姿态与平坦地形直行姿态出现较大偏差,保证多关节机器人运动稳定性,研究基于激光雷达的多关节机器人姿态自动控制方法。结合激光雷达定位导航技术,构建CPG单元振荡器模型,根据运动步态生成原则优化处理足结构参数,完成多关节机器人的运动姿态参数设定。根据姿态参数设定结果实现运动坐标转换,利用动力学方程的简化与分解表达式,确定非线性耦合项参数化处理结果,整合所得变量数据建立反馈控制器连接闭环,利用反馈控制器连接闭环自动控制多关节机器人姿态。对比实验结果表明,在激光雷达技术作用下,机器人上、下坡步长与平坦直行步长之间的误差最大值仅为10%,机器人行进过程中不会出现明显晃动情况,多关节机器人运动稳定性较高。     

含有非驱动关节机器人的学习控制

含有非驱动关节的机器人的运动控制比一般的机器人要困难得多．因为非驱动关节 不能直接控制，系统属于非完全可控系统，一般的光滑反馈控制方法对这样的系统是无效的 ．本文提出了一种学习控制的方法，通过学习获得高精度的前馈控制，实现欠驱动机器人的 高精度运动控制，并在一台实际的欠驱动机器人上进行了实验，给出了实验结果．     

基于车体加速度反馈的轮式移动机器人轨迹跟踪控制研究

本文分析了轮式移动机器人在运行过程中由于动力 学不确定性引起的力矩扰动，并提出了一种基于传感器的移动机器人控制方法．在利用线加 速度传感器实时测量机器人车体加速度信号的基础上，实现了车体加速度反馈控制，实验 证明了该方法的有效性．     

Feedback gains for correcting small perturbations to standingposture

A dynamical model of the neuro-musculo-skeletal mechanics of a cat hindlimb is developed to investigate the feedback regulation of standing posture under small perturbations. The model is a three-joint limb, moving only in the sagittal plane, driven by 10 musculotendon actuators, each with response dynamics dependent on activation kinetics and muscle kinematics. Under small perturbations, the nonlinear postural regulation mechanism is approximately linear. Sensors exist which could provide state feedback. Thus, the linear quadratic regulator is proposed as a model for the structure of the feedback controller for regulation of small perturbations. System states are chosen to correspond to the known outputs of physiological sensors: muscle forces (sensed by tendon organs), a combination of muscle lengths and velocities (sensed by spindle organs), joint angles and velocities (sensed by joint receptors), and motoneuron activities (sensed by Renshaw cells). Thus, the feedback gain matrices computed can be related to the spinal neural circuits. Several proposals for control strategy have been tested under this formulation. It is shown that a strategy of regulating all the states leads to controllers that best mimic the externally measured behavior of real cats     

一种基于运动状态反馈的机器人接触力控制

针对机器人从自由空间运动控制过渡到约束空间力控制的过程中，存在冲撞振荡，甚至不稳定等问题，提出了利用加速度反馈为力控制提供阻尼，克服单纯依赖速度反馈的局限，进而稳定力控制系统的方法。这种方法不增加系统的复杂性，易于实现。     

自由飞行空间机器人的关节驱动力矩递推算法及其仿真

由于空间特有的微重力环境和卫星本体重心不固定,机械手和卫星本体之间存在运动学和动力学耦合作用,已有针对地面机器人提出的计算关节力矩的 Ｎｅｗ ｔｏｎ Ｅｕｌｅｒ 法和地面机器人的控制策略不能直接应用于自由飞行空间机器人（ Ｆ Ｆ Ｓ Ｒ）的研究报告．文中首先分析了 Ｆ Ｆ Ｓ Ｒ 的速度和加速度关系,在此基础上建立了计算 Ｆ Ｆ Ｓ Ｒ机械手各链杆的速度和加速度的递推公式;其次基于已有的针对地面机器人提出的 Ｎｅｗ ｔｏｎ Ｅｕｌｅｒ 动力学算法,开发了适用于 Ｆ Ｆ Ｓ Ｒ 的关节驱动力矩的递推算法;最后,用计算机仿真验证了文中提出的关节力矩算法的有效性     

Adaptive Tracking Controller for Rigid-Link Elastic-Joint Robots with Link Acceleration Estimation

This paper presents a adaptive switching control scheme for elastic joint robot manipulators. The characteristics of both flexible and rigid subsystems are assumed unknown except the joint stiffness. An adaptive estimator compensates the uncertainty due to the unknown robot characteristics. The actuators and links position, velocity and an estimation of the link acceleration are used as feedback to the control law. A filter is designed to estimate the links acceleration and its accuracy is insured by the linear control theory. Lyapunov theory is used to verify the asymptotic stability of the control law. The performance of the proposed control method is tested using a simulated planar robot with two rotational degrees of freedom for high and low joint stiffness.     

基于加速度计的手指运动姿态检测

针对动态测量手指关节角度的需要,设计了一种基于MEMS加速度计的手指运动姿态检测方法。在该方法中,通过固定于手指上的三轴加速度计ADXL330的各个敏感轴感受到的重力加速度分量的大小来检测手指的关节角度。为了验证该方法的可行性,设计了一步进电机控制的能在竖直平面内转动的装置,通过同步检测传感器的输出和电机转动的角度,评估传感器在动态条件下的测量精度,结果表明:在动态条件下,其绝对误差为1°～2.5°,相对误差为3%～6%。在此基础上,将传感器用于测量手指关节的运动姿态,采集手指敲击键盘时传感器的输出,通过数据处理获得食指MCP关节、PIP关节和DIP关节随时间的变化关系。实验结果表明:加速度计可以有效地检测手指的运动姿态。     

Joint acceleration feedback control for robots: analysis, sensing and experiments

Independent joint control for robots is enhanced to suppress the dynamic couplings by incorporating an acceleration feedback loop that is designed in terms of its stability and ability in resisting the dynamic coupling disturbances. The sensing and modeling of joint acceleration via linear accelerometers is dealt from the viewpoint of practical implementation. Extensive experiments are conducted on a three-link direct drive robot, to mainly investigate the ability of the independent joint controller with the acceleration loop in resisting the coupling torque, and demonstrating the enhanced trajectory tracking performance. Results are given against the ones obtained by conventional independent joint control without the acceleration feedback control.     

Reinforcement learning based compliance control of a robotic walk assist device

ABSTRACT

Millions of people around the globe have to deal with walking disability. Robotic walk assist devices can help people with walking disabilities, especially those with weak legs. However, safety, cost, efficiency and user friendliness are some of the key challenges. For robotic walk assist devices, light weight structure and energy efficient design as well as optimal control are vitally important. In addition, compliance control can help to improve the safety of such devices as well as contribute to their user friendliness. In this paper, an optimal adaptive compliance control is proposed for a Robotic walk assist device. The suggested scheme is based on bio-inspired reinforcement learning. It is completely dynamic-model-free scheme and employs joint position and velocity feedback as well as sensed joint torque (applied by user during walk) for compliance control. The efficiency of the controller is tested in simulation on a robotic walk assisting device model.     

On inverse kinematics with inequality constraints: new insights into minimum jerk trajectory generation

The problem of inverse kinematics is revisited in the present paper. The paper is focusing on the problem of solving the inverse kinematics problem while minimizing the jerk of the joint trajectories. Even though the conventional inverse kinematics algorithms have been proven to be efficient in many applications, it has been proven that constraints on the accelerations or the jerk cannot be guaranteed, and even yields to divergence or makes the problem unsolvable. The proposed algorithm yields smooth velocity and acceleration trajectories, which are highly desired features for industrial robots. The algorithm uses the joint jerk as the control parameter instead of the classical use of the joint velocity as result constraints on the jerk function can be easily incorporated. To validate the proposed approach, we have conducted several simulations scenarios. The simulation results have revealed that the proposed method can efficiently solve the inverse kinematics problem while considering constraints on the joint acceleration and jerk.     

Robust robot manipulator control with autonomous consideration algorithm of torque saturation

As each joint actuator of a robot manipulator has a limit value of torque, the motion control system should consider the torque saturation. Conventional motion control based on robust acceleration controller cannot consider the torque saturation and it often causes an oscillated or wrong response. This paper proposes a new autonomous consideration method of joint torque saturation for robust manipulator motion control. The proposed method consists of three on-line autonomous algorithms. These algorithms are the torque limitation algorithm in joint space, the adjustment algorithm of motion control in Cartesian space, and the adjustment algorithm of motion reference in Cartesian space. The robot motion control using the proposed algorithms realizes smooth and robust robot motion response.     

基于改进梯度投影算法的移动机械臂轨迹规划研究

移动机械臂由移动平台和机械臂构成,是一种典型的冗余机械臂系统。其运动学和轨迹规划问题是研究热点。梯度投影法是求解该问题的一类常用算法,其应用难点主要集中在比例因子的选取上。极值法通过最优化方法求解比例因子,将比例因子作为目标函数,并基于关节速度极限、可优化度等设计约束条件。针对极值法存在使关节加速度超限的问题,提出一种考虑关节加速度约束的新算法,并通过仿真对比验证该算法的有效性。