遗传算法在柔性臂控制中的应用

提出了一种双连杆柔性机械臂的控制方法.首先运用拉格朗日法建立双连杆柔性机械臂的动力学模型,并利用奇异摄动方法将柔性臂系统分解为慢变和快变子系统后,设计了组合控制器.对慢变子系统利用遗传算法确定两杆的PD控制参数,而时快变子系统采用最优H2综合策略设计控制器后,对柔性臂系统施加组合控制律.仿真结果表明了所设计控制器的有效性.     

柔性关节柔性连杆机械臂的动力学建模

柔性关节柔性连杆机械臂是典型的非线性、强耦合、欠驱动系统,其控制难度高.对于这类系统,选择合适的动力学模型进行控制器设计对于提高控制性能是非常有帮助的.为此,研究了具有柔性关节柔性连杆机械臂的动力学建模问题,并提出了一种改进的建模方法.在该方法中,连接柔性连杆的柔性关节首先被简化为刚性关节和柔性连杆的弹性约束边界.然后,根据结构动力学理论、哈密顿原理和假设模态法建立系统的刚柔耦合动力学方程.相较于将柔性关节简化为刚性关节和扭簧的传统处理方式,所采用的简化方式一方面可以降低系统的自由度,另一方面可以得到更适合控制器设计的动力学模型.最后,通过数值仿真验证了本文方法的有效性和优势.     

滑模控制及其在柔性臂轨迹控制中的应用

变结构控制系统是一类特殊的非线性控制系统,滑模控制就是其中一种。滑模控制对系统参数及扰动的变化反应迟钝,具有很强的鲁棒性,该类控制方法特别适合于机械臂系统的控制。本文中采用奇异摄动方法将双连杆柔性机械臂系统分解为慢变和快变两个子系统,并对慢变子系统采用滑模控制方法设计了控制器。采用MATLAB进行的数值仿真结果表明了所设计控制器的有效性;     

空间机械臂柔性连杆的弯曲形状

研究单连杆旋转柔性空间机械臂的弯曲形状,以确定空间柔性机械臂末端的位置.在考虑连杆变形对力矩影响和曲率表示式中不忽略形状函数一阶导数平方的情况下,利用Taylor展式表示连杆的变形,导出了连杆变形的数学模型.通过降阶方法导出形状函数一阶导数的解析式,并给出了由此解析式利用数值积分确定形状函数的方法.给出了利用二分法确定变形后的连杆在其固连坐标系中最大横坐标的方法.针对一组参数进行了仿真,证实了这种方法确定空间柔性机械臂末端的位置的可行性.     

柔性遥操作机器人系统的滑模变结构控制

针对遥操作机器人系统,在空间与医疗手术中应用时出现的从机械臂柔性问题,为了提高跟踪性能,采用拉格朗日法结合假设模态法,建立了末端有集中质量的单连杆柔性从机械臂的动力学模型。在此基础上,设计了一种基于柔性从机械臂的遥操作机器人系统的新型滑模变结构控制方案。进行实例仿真,结果表明,控制方案能有效地抑制柔性从机械臂的弹性振动,自由运动情况下系统能较好地实现位置跟踪。     

网络化单连杆刚性关节机械臂系统的可控性

主要研究了网络化单连杆刚性关节机械臂系统的可控性问题.基于单输入—状态反馈线性化技术,提出了一种可行的网络化单连杆刚性关节机械臂系统可控性分析方法,并给出了网络化单连杆刚性关节机械臂系统可控性的一般准则.准则表明链式拓扑结构下的网络化单连杆刚性关节机械臂系统总是可控的,并且其控制输入能够精确地显式表达出来.最后通过仿真验证了所给理论结果的有效性.     

一种双连杆柔性臂动力学模型的研究

针对末端位置受约束的双连杆柔性机械臂,通过哈密顿原理得到系统的分布参数模型,推导了描述关节角,柔性杆振动和接触力之间关系的动力学模型.根据3个假设条件推导柔性臂简化的集中参数动力学模型及准静态方程,利用了计算力矩法设计的控制器对模型进行控制,通过Matlab进行仿真,利用Matlab的符号运算功能,编制M文件实现数学模型自动推导,整个建模和运算过程简单、直观和高效,并绘制了参数轨迹图像,验证了模型的有效性,已应用到小波神经网络控制算法研究中.     

基于模糊神经的柔性臂变负载控制方法

基于奇异摄动将单连杆柔性机械臂动力学模型分解为慢、快变子系统,传统方法分别采用PD控制和最优控制能取得较好控制效果,但负载不确定时,控制效果并不理想.提出对于慢变子系统,采用模糊神经、PD控制相结合的控制方法,对于快变子系统,采用模糊神经、最优控制相结合的控制方法.当负载变化时,采用模糊神经控制器根据实际负载对PD参数及最优控制参数进行调整,达到更优的控制效果.分别采用传统方法及本文提出的改进方法在变负载条件下作了仿真实验,结果表明后者的控制效果明显优于前者.给出了慢、快变子系统模糊神经控制器在变负载条件下训练参数的获取方法.     

时滞柔性关节机械臂自适应位置/力控制

针对具有时滞的柔性关节机械臂自适应位置和力控制问题进行了研究.首先,通过坐标变换得出降维的位置/力控制模型.随后,将时间滞后近似表示成一阶滞后,进行时滞补偿.利用自适应算法修正机械臂系统参数,克服模型参数不确定性对系统的影响.同时,采用反步控制技术设计机械臂位置/力控制器,运用Lyapunov稳定性定理证明控制器能使机械臂位置和力跟踪误差收敛.最后的仿真研究验证了控制方案的有效性.     

柔性机械臂的双时间尺度组合控制

本文研究柔性机械臂的轨迹跟踪和振动抑制问题. 首先, 利用Lagrange法和假设模态法建立柔性机械臂的动态模型, 进而利用奇异摄动理论得到柔性机械臂的双时间尺度模型. 然后, 基于慢时间尺度模型利用滑模控制理论设计轨迹跟踪控制器; 借助于快时间尺度模型利用自适应动态规划设计参数不精确已知情况下的最优振动抑制控制器; 将二者相结合, 构造双时间尺度组合控制器, 利用奇异摄动理论证明闭环系统稳定. 最后, 在Matlab/Simulink环境下进行实验, 与现有方法相比, 本文设计的控制器对柔性振动具有更好的振动抑制效果, 跟踪精度更高.     

Position and force tracking of a two‐manipulator system manipulating a flexible beam

This article discusses the issue of hybrid position and force control of a two‐manipulator system manipulating a flexible beam in trajectory tracking. Unlike our previous approach of set‐point position control in the trajectory tracking, the system coordinates are hard to be regulated to the desired states with nonzero tracking velocities under continuous feedback control. In this study, we design a hybrid position and force tracking controller while using saturation control to compensate for the effect of beam vibration dynamics on the tracking performance. All parameters and states used in the controller are readily available so that the proposed method is feasible to implement. Under the proposed controller, the tracking error asymptotically converges to a predetermined boundary. Simulation results demonstrate the validity of the proposed approach. © 2001 John Wiley & Sons, Inc.     

线束插植机械臂控制系统设计

针对线束插植作业过程中多线束可编程插植和插植力控制问题,研究一种基于串联弹性执行器(Series Elastic Actuator 简称SEA)的自动化线束插植的三轴机械臂控制系统。首先,分析了多线束插植过程的工艺流程要求;其次,给出了线束插植机械臂的设计方案;然后,研制了线束插植运动控制器,包括坐标系示教、多线束自动插植编程、插植力控制功能模块;最后,利用研制的线束插植控制器进行了手动和自动插植力控制实验,验证了所设计系统的可行性、有效性。     

Adaptive control of flexible link manipulators using a pseudolink dynamic model

This paper presents a novel adaptive control scheme for a lightweight manipulator arm governed by electric motors. The controller design is based on the dynamic model of the arm in a quasi-static approximation which consists of the transports subsystem and the motor equations corrected for the elastic compliance of the plant. A passivity property of the flexible electromechanical system is established and an adaptive motor controller is developed which contains the rigid manipulator controller as a part. The motor controller updates all unknown rigid manipulator parameters as well as elastic parameters and ensures global asymptotic stability of the tracking errors with all signals in the system remaining bounded. Projecting of parameter estimates is used in the update law to avoid possible singularities when generating control input. Simulation results for a single-link elastic arm confirm the validity and demonstrate advantages of the proposed method.     

文献扫描机器人多关节机械臂滑膜控制系统设计

为提升机器人机械臂关节的传动性能,使其处于良好的反步自适应工作环境,设计文献扫描机器人多关节机械臂滑膜控制系统。利用关键控制电路,实现机械臂全局PID滑膜控制器与机器人多关节滑膜控制器间的定向连接,完成新型控制系统的硬件运行环境搭建。通过机器人控制传感器标定操作,建立等效控制及动态滑膜方程,并利用上述计算结果界定机械臂滑膜的动态品质,实现新型控制系统的软件运行环境搭建,结合软、硬件运行单元,完成文献扫描机器人多关节机械臂滑膜控制系统设计。模拟文献扫描机器人多关节机械臂运行状态,设计对比实验结果表明,与传统系统相比应用新型滑膜控制系统后,机械臂关节的传动能力得到有效提升,反步自适应参数最大值可达到1.70     

Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

In this paper, we present a novel data-driven design method for the human-robot interaction (HRI) system, where a given task is achieved by cooperation between the human and the robot. The presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller design. The task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop, while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the inner-loop. Data-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance parameters. In the inner-loop, a velocity-free filter is designed to avoid the requirement of end-effector velocity measurement. On this basis, an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task space. The simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.      

Active Vibration Control for a Flexible‐Link Manipulator with Input Constraint Based on a Disturbance Observer

This paper mainly focuses on designing an active vibration control for a flexible‐link manipulator in the presence of input constraint and unknown spatially infinite dimensional disturbances. The manipulator we studied can be taken as an Euler–Bernoulli beam, the dynamic model of which has the form of partial differential equations. As the existence of spatially infinite dimensional disturbances on the beam, we first design a disturbance observer to estimate infinite dimensional disturbances. The proposed disturbance observer is guaranteed exponentially stable. Then, taking input saturation into account, a novel disturbance‐observer‐based controller is developed to regulate the joint angular position and rapidly suppress vibrations on the beam, which is the main contribution of this study. The closed‐loop system is validated asymptotically stable by theoretical analysis. The effectiveness of the proposed scheme is demonstrated by numerical simulations.     

Fuzzy sliding-mode control with rule adaptation for nonlinear systems

Abstract: A fuzzy sliding-mode control with rule adaptation design approach with decoupling method is proposed. It provides a simple way to achieve asymptotic stability by a decoupling method for a class of uncertain nonlinear systems. The adaptive fuzzy sliding-mode control system is composed of a fuzzy controller and a compensation controller. The fuzzy controller is the main rule regulation controller, which is used to approximate an ideal computational controller. The compensation controller is designed to compensate for the difference between the ideal computational controller and the adaptive fuzzy controller. Fuzzy regulation is used as an approximator to identify the uncertainty. The simulation results for two cart–pole systems and a ball–beam system are presented to demonstrate the effectiveness and robustness of the method. In addition, the experimental results for a tunnelling robot manipulator are given to demonstrate the effectiveness of the system.     

On the Robust Nonlinear Motion Position and Force Control of Flexible Joints Robot Manipulators

The design of a robust nonlinear position and force controller for a flexible joints robot manipulator interacting with a rigid environment is presented. The controller is designed using the concept of feedback linearization, sliding mode techniques, and LQE estimation methodologies. It is shown that the nonlinear robot manipulator model is feedback linearizable. A robust performance of the proposed control approach is achieved by accounting for the system parameters uncertainties in the derivation of the nonlinear control law. An upper bound of the error introduced by parametric uncertainties in the system is computed. Then, the feedback linearizing control law is modified by adding a switching action to compensate the errors and to guarantee the achievement of the desired tracking performance. The relationship between the minimum achievable boundary layer thickness and the parametric uncertainties is derived. The proposed controller is tested using an experimental flexible joints robot manipulator, and the results demonstrate its potential benefits in reducing the number of sensors required and the complexity of the design. This is achieved by eliminating the need for nonlinear observers. A robust performance is obtained with minimum control effort by taking into account the effect of system parameter uncertainties and measurement noise.     

并联机械手控制系统设计

随着工业自动化水平的提高,工业上对机械手的需求量越来越大。应用欧姆龙NJ系列运动控制器设计两轴并联机械手控制系统。系统由NJ运动控制器、触摸屏、伺服驱动器、伺服电机等硬件组成,NJ控制器通过EtherCAT网络通信进行运动控制,实现了两轴并联机械手的协调动作,达到特定运动轨迹。该控制系统具有响应速度快、定位准确、稳定性强、误差小、调速方便等特点。NJ控制器在运动控制方面表现出极高的优越性。     

Sliding Mode Control for Flexible-link Manipulators Based on Adaptive Neural Networks

This paper mainly focuses on designing a sliding mode boundary controller for a single flexible-link manipulator based on adaptive radial basis function (RBF) neural network. The flexible manipulator in this paper is considered to be an Euler-Bernoulli beam. We first obtain a partial differential equation (PDE) model of single-link flexible manipulator by using Hamiltons approach. To improve the control robustness, the system uncertainties including modeling uncertainties and external disturbances are compensated by an adaptive neural approximator. Then, a sliding mode control method is designed to drive the joint to a desired position and rapidly suppress vibration on the beam. The stability of the closed-loop system is validated by using Lyapunov’s method based on infinite dimensional model, avoiding problems such as control spillovers caused by traditional finite dimensional truncated models. This novel controller only requires measuring the boundary information, which facilitates implementation in engineering practice. Favorable performance of the closed-loop system is demonstrated by numerical simulations.