基于矢量场在线更新的人和机器人身体交流控制

为实现人和机器人身体相互交流时的运动同步,提出基于矢量场在线更新的控制方法,并将此方法应 用于人和机器人握手的研究中．首先,对该方法中任意吸引子的矢量场进行多项式近似设计,使其具备自振动特性; 其次,通过在线更新设计,控制系统具有输入输出同步特性,并且可以通过调节系统中的忘却系数和负荷系数来改 变其同步程度;最后,基于7 自由度机器人臂,将该方法用于人和机器人握手实验中,结果表明了此方法的有效性．     

基于同步控制的人和机器人握手动力学仿真

为实现人和机器人握手运动的同步,提出了基于神经元振荡器同步控制的方法,并将此方法应用于 人和机器人握手的研究中．同时,在分析现有神经元振荡器特性的基础上,设计了一种新的人和机器人握手的神 经元振荡器,并将该神经元振荡器应用于同步控制方法中进行人和机器人握手的动力学仿真,仿真结果证明了该 控制方法的有效性．     

一种基于行为控制的两自由度机械臂智能控制器

基于行为的控制方法相对于传统的控制方法在解决未知环境中的机器人中有着更好的鲁棒性和实时性.本文提出了一种基于反应式行为控制的智能控制器,以强化学习作为智能控制器的学习算法.通过采用评价-控制模型,该智能控制器能够不依赖于系统模型,通过连续地在线学习得到机器人的行为.将该智能控制器应用到两自由度仿真机械臂的控制中,仿真结果表明该智能控制器可以实现对两自由度机械臂的连续控制,使其能够迅速达到目标位置.     

实现人身和机器人交流的神经振动子控制算法

为实现人身和机器人交流时的运动同步,首先提出了一类非线性多关节神经振动子运动控制算法,其输入为机器人和人相互作用所产生的关节扭矩信号,输出为机器人关节期望角度;然后对具有代表性的二关节神经振动子控制算法中各参数的耦合特性进行了分析;最后,基于7自由度机器人臂平台对该神经振动子控制算法的有效性进行实验.实验结果表明,该控制算法能够实现人和机器人相互运动的同步,通过调节神经振动子的增益参数,同步的程度能够被改变.     

七连杆双足机器人建模和控制系统仿真

研究双足机器人稳定性控制问题,步行机器人作为一种载运工具适应地况能力强,结构复杂,运动控制难.实现类人型机器人动态行走,针对行走的稳定性,必须对机器人进行动力学建模、步态设计和稳定姿态控制算法设计.研究了一种七连杆双足机器人的动力学建模和控制系统仿真方法.建立两足步行机器人腿的可参数化仿真模型,对七平面双足机器人的运动情况和控制输入输出进行仿真,得出试验结果.并对影响步行机器人稳定性能的参数进行分析,为后面机器人样机的研制提供理论及数据依据.     

基于Virtual.lab的柔性并联机器人仿真平台

为了实现柔性并联机器人的快速可视化建模,提供柔性并联机器人动力学分析的新途径,针对柔性并联机器人系统内刚体、柔体耦合的难点,利用Virtual.lab软件对VBA的支持及其处理机械系统弹性动力学问题的优势,结合数据库技术、参挝数化建模原理和柔性并联机器人动力学仿真的特点,对Virtual.lab软件进行二次开发,建立柔性并联机器人动力学仿真平台,可实现柔性并联机器人动力学特性的分析.该平台操作简单,建模效率高,计算速度快,便于机械工程师在机械设计中使用.为柔性并联机器人结构优化设计及运动学、动力学规划指标的确定提供科学依据.     

DGR—5A机器人的动力学及控制

摘要 DGR-5A 机器人是我们自行设计的五自由度全关节型电动机器人.本文讨论了该机器人的动力学及控制问题.为此对机器人的各杆臂建立坐标系并详尽地计算了各杆臂的惯性参数.在推导了该机器人前三个自由度的动力学方程和分析了动力学特性之后,我们提出了一种实时计算量少,易于工程实现的控制方案.最后对该方案进行了仿真研究,仿真的结果是颇为令人满意的.     

基于神经网络的非线性观测器及在线故障检测

提出一种基于径向基函数神经网络的非线性观测器的设计方法，并将其应用于复杂非线性系统的故障检测与隔离。该方法将神经网络离线学习与在线学习相结合，获取系统输入输出的非线性动力学特性，进而实时计算出残差并进行逻辑判决，可显著提高故障检测的快速性、鲁棒性及准确率。最后，针对非线性同步交流电机的结构损伤故障进行了仿真，结果表明本文所提方法的有效性。     

新型欠秩并联机器人机构3RRC仿真

分析了并联机器人机构3RRC的输入输出特性,给出了位置分析和连杆运动干涉分析。以directX为仿真引擎,vc6为软件平台,采用面向对象方法封装了机构正反解算法,编写了仿真软件,实现了该机器人机构运动学动态仿真,为类似机构结构设计和特性研究提供了有益参考。     

基于解耦线性化的变刚度关节动态刚度辨识

为了能够利用变刚度关节实现对机器人动态特性的调整,需要对关节的动态刚度进行有效的辨识和控制.本文首先根据机器人变刚度关节的结构特点建立了简化模型,并对其刚度输出特性表达做出假设;然后对模型中的力矩相关参数进行解耦,消除了关节刚度调节参数对力矩的影响,获取与刚度辨识相关的归一化力矩;利用泰勒展开对归一化力矩进行线性化处理,采用卡尔曼滤波器进行了系数优化,并进一步实现了对关节动态刚度的辨识.仿真中该刚度在线辨识方法可以将辨识误差控制在±2%以内,在实现动态刚度辨识的基础上研究了基于前馈的刚度闭环控制方法,通过仿真实验验证了该方法对于机器人关节刚度闭环控制是有效的.     

Adaptive Linear Quadratic Regulator for Continuous-Time Systems With Uncertain Dynamics

In this paper, adaptive linear quadratic regulator (LQR) is proposed for continuous-time systems with uncertain dynamics. The dynamic state-feedback controller uses input-output data along the system trajectory to continuously adapt and converge to the optimal controller. The result differs from previous results in that the adaptive optimal controller is designed without the knowledge of the system dynamics and an initial stabilizing policy. Further, the controller is updated continuously using input-output data, as opposed to the commonly used switched/intermittent updates which can potentially lead to stability issues. An online state derivative estimator facilitates the design of a model-free controller. Gradient-based update laws are developed for online estimation of the optimal gain. Uniform exponential stability of the closed-loop system is established using the Lyapunov-based analysis, and a simulation example is provided to validate the theoretical contribution.      

Model reference adaptive impedance control for physical human-robot interaction

This paper presents a novel enhanced human-robot interaction system based on model reference adaptive control. The presented method delivers guaranteed stability and task performance and has two control loops. A robot-specific inner loop, which is a neuroadaptive controller, learns the robot dynamics online and makes the robot respond like a prescribed impedance model. This loop uses no task information, including no prescribed trajectory. A task-specific outer loop takes into account the human operator dynamics and adapts the prescribed robot impedance model so that the combined human-robot system has desirable characteristics for task performance. This design is based on model reference adaptive control, but of a nonstandard form. The net result is a controller with both adaptive impedance characteristics and assistive inputs that augment the human operator to provide improved task performance of the human-robot team. Simulations verify the performance of the proposed controller in a repetitive point-to-point motion task. Actual experimental implementations on a PR2 robot further corroborate the effectiveness of the approach.     

人机智能技术及系统研究进展综述

人机智能系统是能够实现人机智能协作的机器人系统,近年来成为了机器人领域的研究热点,具有广泛的应用前景。针对人机智能系统技术和应用的国内外研究现状,从人机智能系统的关键技术和典型应用领域两方面进行了进展综述。重点综述了与传统机器人系统存在差异性的人机智能系统关键技术,从建模、交互、协同和优化4个方面的研究进展分别展开论述,对涉及的典型应用领域及典型人机智能系统进行总结,并对人机智能系统发展的挑战和未来研究方向进行了展望。     

A self-tuning fuzzy controller for a class of multi-input multi-output nonlinear systems

This paper presents a systematic design procedure of a multivariable fuzzy controller for a general Multi-Input Multi-Output (MIMO) nonlinear system with an input-output monotonic relationship or a piecewise monotonic relationship for each input-output pair. Firstly, the system is modeled as a Fuzzy Basis Function Network (FBFN) and its Relative Gain Array (RGA) is calculated based on the obtained fuzzy model. The proposed multivariable fuzzy controller is constructed with two orthogonal fuzzy control engines. The horizontal fuzzy control engine for each system input-output pair has a hierarchical structure to update the control parameters online and compensate for unknown system variations. The perpendicular fuzzy control engine is designed based on the system RGA to eliminate the multivariable interaction effect. The resultant closed-loop fuzzy control system is proved to be passive stable as long as the augmented open-loop system is input-output passive. Two sets of simulation examples demonstrate that the proposed fuzzy control strategy can be a promising way in controlling multivariable nonlinear systems with unknown system uncertainties and time-varying parameters.     

Intelligent control of SVC using wavelet neural network to enhance transient stability

In order to enhance transient stability in a power system, a new intelligent controller is proposed to control a Static VAR compensator (SVC) located at center of the transmission line. This controller is an online trained wavelet neural network controller (OTWNNC) with adaptive learning rates derived by the Lyapunov stability. During the online control process, the identification of system is not necessary, because of learning ability of the proposed controller. One of the proposed controller features is robustness to different operating conditions and disturbances. The test power system is a two-area two-machine system power. The simulation results show that the oscillations are satisfactorily damped out by the OTWNNC.     

基于鲁棒滑模观测器的两关节柔性机械手控制

柔性机械手系统为非最小相位系统, 当控制有界时, 该特性阻碍其端点位移渐近跟踪期望轨迹. 本文首先重新定义柔性机械手系统的输出, 通过输入输出线性化, 将系统分解为输入输出子系统和零动态子系统; 然后提出一种用于观测柔性模态导数的鲁棒滑模观测器, 使状态估计达到预期的指标, 解决了柔性模态导数难以获得的问题; 设计积分滑模控制策略, 使输入输出子系统在有限时间收敛到零; 选择适当的控制器参数, 使零动态子系统在 平衡点附近渐近稳定, 从而保证整个系统的渐近稳定. 本文提出的方法设计过程简单, 易于实现. 仿真结果证明了设计的有效性.     

A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System

In this paper, a novel real time non-linear model predictive controller(NMPC) for a multi-variable coupled tank system(CTS) is designed. CTSs are highly non-linear and can be found in many industrial process applications. The involvement of multi-input multi-output(MIMO) system makes the design of an effective controller a challenging task. MIMO systems have inherent couplings,interactions in-between the process input-output variables and generally have an complex internal structure. The aim of this paper is to design, simulate, and implement a novel real time constrained NMPC for a multi-variable CTS with the aid of intelligent system techniques. There are two major formidable challenges hindering the success of the implementation of a NMPC strategy in the MIMO case. The first is the difficulty of obtaining a good non-linear model by training a non-convex complex network to avoid being trapped in a local minimum solution. The second is the online real time optimisation(RTO) of the manipulated variable at every sampling time.A novel wavelet neural network(WNN) with high predicting precision and time-frequency localisation characteristic was selected for an MIMO model and a fast stochastic wavelet gradient algorithm was used for initial training of the network. Furthermore, a genetic algorithm was used to obtain the optimised parameters of the WNN as well as the RTO during the NMPC strategy. The proposed strategy performed well in both simulation and real time on an MIMO CTS. The results indicated that WNN provided better trajectory regulation with less mean-squared-error and average control energy compared to an artificial neural network. It is also shown that the WNN is more robust during abnormal operating conditions.