双柔性臂机器人的建模和主动振动控制

针对双柔性臂机器人在操作过程中出现的结构振动和残余振动影响控制精度的问题,提出了采用多组压电换能器对柔性臂进行主动振动的控制方法.设计了基于超声电机驱动的双柔性臂机器人,采用拉格朗日法和假设模态法对双柔性臂进行了动力学建模,并考虑了在协作搬运过程中双柔性臂机器人需要满足的闭链约束条件.提出了采用独立关节PD反馈控制器和正位置反馈控制器相结合的混合控制方法.Matlab仿真表明,所提出的混合控制器能使柔性机器人在准确完成目标物体轨迹运动的同时,实现对柔性臂的振动抑制,提高了双柔性臂机器人的搬运精度和效率.     

基于人机耦合模型的上肢康复外骨骼闭环PD迭代控制方法

针对多关节上肢外骨骼重复性康复训练非线性求解困难问题,提出了一种闭环PD迭代学习控制方法。基于人体工学确定了六自由度上肢外骨骼康复机械臂的参数、自由度配置与关节运动范围。以人机交互力为耦合方式,建立了基于牛顿-欧拉法的人机耦合模型,完成了人机耦合动力学模拟分析。基于迭代学习控制理论提出外骨骼康复机械臂的闭环PD迭代学习控制方法,通过建模仿真分析了肩关节/肘关节迭代学习控制的轨迹误差、人机交互力和驱动力矩。第三次迭代后的轨迹误差小于0.05 rad,PD迭代学习控制器的输出对系统控制进行了有效的补偿,提高了系统状态的稳定性。研制了六自由度上肢外骨骼康复机械臂样机,开展试验测试。试验结果表明,随着控制试验在迭代域上的运行,系统的输出向着期望的系统状态转化,所提出的迭代学习控制算法可以提高上肢外骨骼康复训练重复性运动的控制精度,进而提高人机交互性能。     

基于迭代学习控制的码垛机器人轨迹优化

提出一种学习控制方法用于码垛机器人的速度优化。通过对系统跟随误差学习,实现在误差允许范围内运动速度最大化,从而实现码垛效率的优化。本方法对系统建模要求较低,易于在实际控制器上实现。系统仿真实验结果表明,在带入机械臂系统参数后,在对迭代算法改进后的最优轨迹与未改进的标准轨迹比较后,前者通过改进局部速度,使机器人运转时间缩短了10％～20％。     

基于模糊自整定PD控制器的机器人计算力矩控制

设计了应用于计算力矩控制方案中的模糊PD控制器(FLC),进一步提出了模糊自整定PD控制器(SFLC).用带有末端执行器的平面3自由度机器人为例进行了动力学仿真,并将采用固定PD控制器、模糊PD控制器和模糊自整定PD控制器的计算力矩控制方案进行了比较.仿真结果表明,采用模糊自整定PD控制器(SFLC)后能有效的克服模型不确定所造成的影响,得到比较小的轨迹跟踪误差,为机器人的实际控制提供了理论基础.     

三关节移动机械臂轨迹跟踪控制的研究

三关节移动机械臂是由移动平台及固定在移动平台上的三关节机械臂组成的机器人系统。文章对三关节移动机械臂进行了运动学和动力学建模,提出了一种基于自适应动态滑模控制的轨迹跟踪控制方法。对受外界干扰影响的移动机械臂系统的数学模型进行了输入/输出线性化处理,并设计了一种带有不确定性上界估计的自适应动态滑模轨迹跟踪控制器。从理论上分析了闭环机器人控制系统的稳定性,并利用MATLAB轨迹跟踪控制仿真实验验证了所提出的轨迹跟踪控制方法的有效性。     

模糊自适应控制在柔性机器人操作臂中的应用

将拉格朗日和假设模态法相结合，建立了柔性机器人操作臂的动力学方程，并提出了一种模糊自适应控制策略。在柔性操作臂末端带有不同的载荷时，分两种情况进行了控制仿真实验；定位控制和轨迹跟踪控制。与PD控制仿真实验的结果相比较，模糊自适应控制器在轨迹跟踪和振动抑制方面反映出更好的稳定和鲁棒性能。     

基于LabVIEW分数阶控制器的机械臂控制

针对机械臂存在着参数摄动和外界干扰等不确定性,提出一种基于Lab VIEW分数阶控制器的机械臂控制方法,以提高机器人的跟踪控制精度和鲁棒性。在CompactRIO智能实时控制器和机箱的基础上利用NI9401对伺服电机的编码器信号进行采集处理,Lab VIEW软件编程控制NI9263输出模拟电压信号到伺服驱动器实现电机的力矩控制,算法采用分数阶PD控制器实现二自由度机械臂高速和高精度的运动控制,通过实验说明了该方法的工程有效性和可行性。     

不确定性机器人轨迹跟踪控制的粒子群算法

由于机器人动力学系统中存在的不确定项以及外部干扰信号等因素的影响,传统控制方法实现机器人系统的轨迹跟踪时存在较大的跟踪误差。针对这一问题,提出了一种基于粒子群算法的机器人轨迹跟踪的学习补偿控制方法。首先利用PD控制的计算力矩法控制机器人系统的精确已知部分,再利用粒子群算法的进化学习功能对机器人系统中存在的不确定性因素进行补偿,消除不确定性因素对系统的影响,实现机器人系统运动轨迹跟踪的良好控制。文末以PUMA560型机器人为受控对象,给出了仿真算例,仿真结果表明了该方法的有效性。     

漂浮基双臂空间机器人系统协调运动的模糊小波神经网络控制

讨论了本体姿态受控、位置不受控的情况下,漂浮基双臂空间机器人系统协调运动的动力学控制问题。依据系统动量守恒关系和拉格朗日第二类方程,推导了漂浮基双臂空间机器人系统的动力学方程。在系统参数未知的情况下,提出了一种小波基模糊神经网络控制器,以使双臂空间机器人系统的本体姿态和机械臂关节铰协调地跟踪各自在关节空间的期望运动轨迹。提出控制器的优点在于：不仅不要求系统动力学方程关于惯性参数呈线性函数关系,而且也不需要知道系统参数;由于采用反向传播算法对网络参数进行在线训练,从而使模糊神经网络具有较好的自学习和自适应能力,这样就不需要事先进行离线训练,更适应于空间机器人系统的实际应用。利用一个双臂空间机器人系统的仿真结果,验证了所提出控制器的有效性。     

柔性宏刚性微空间机器人末端连续轨迹跟踪控制研究

对一类柔性宏刚性微结构的空间机器人系统进行了运动学和动力学建模,介绍了采用微机械臂快速精确运动来补偿宏机械臂由于弹性形变和振动产生的末端误差的设计方案,并对此进行了简化。在此基础上设计了两种控制方案。一种是对宏机械臂和微机械臂都采用PD(比例微分)反馈控制,另一种是宏机械臂采用PD控制而微机械臂采用滑模变结构控制。最后进行了仿真试验,比较了两种控制方案的不同控制效果,并得出补偿算法是有效的,且滑模变结构控制在该试验中优于PD控制的结论。     

Development of a new 5 dof mobile robot arm and its motion control system

In this paper, a new revolute mobile robot arm with five degree of freedom (d.o.f) was developed for autonomous moving robots. As a control system for the robot arm, a distributed control system composed of the main controller and five motor controllers for arm joints was developed. The main controller and the motor controllers were developed using the ARM microprocessor and the TMS320c2407 microprocessor, respectively. A new trajectory tracking algorithm for the motor controllers was devised employing pre-generated off-line trajectory data. Also, a 3-D simulator based on the OpenGL software to simulate the motion of the robot arm was developed. To validate the performance of the robot system, experiments to track a specified trajectory were performed.     

直接驱动型机械手的变结构控制

针对直接驱动型机械手的随动控制问题,提出了一种基于滑动模态扰动观测器的变结构控制器.通过观测由系统的非线性、模型的不确定性和外来干扰所造成的广义扰动,将非线性、强耦合的机械手动力学系统线性化,并解耦为多个单输入、单输出线性系统.控制器的设计不再依赖于机械手的精确模型.在二连杆机械手上做的仿真研究表明,在负载大范围变化的条件下,采用滑动模态扰动观测器的控制系统,比全状态反馈的控制系统具有更好的鲁棒性.     

Dynamics synthesis and control for a hopping robot with articulated leg

To investigate the design and the applications of elastic underactuated mechanisms for improving the energy efficiency of dynamic mechanical systems, the dynamics and control of a one-legged hopping robot with articulated leg is studied in this paper. To ensure the controllability of the elastic underactuated mechanism, a dynamics synthesis method is proposed for designing the underactuated mechanism so that the dynamics of the system can be transformed into the strict feedback normal form. To improve the energy efficiency of the one-legged locomotion system, an optimal motion planning method is presented to optimize the trajectories of the robot joints. A nonlinear controller is proposed to stabilize the hopping robot to its balance configuration in stance phase, and a switched linear controller is proposed to stabilize the continuous hopping movement. The stability of the presented controllers is analyzed in theory and verified by some numerical simulations.     

Implementation of a robust dynamic control for SCARA robot

A control system for SCARA robot is designed for implementing a robust dynamic control algorithm. This study focuses on the use of DSPs in the design of joint controllers and interfaces in between the host controller and four joint controllers and in between the joint controllers and four servo drives. The mechanical body of SCARA robot and the servo drives, are selected from the commercially available products. The four joint controllers, assigned to each joint separately, are combined into a common system through the mother board hardwarewise and through the global memory softwarewise. The mother board is designed to connect joint controllers onto the board through the slots adopting PC/104 bus structures. The global memory stores the common data which can be shared by joint controllers and used by the host computer directly, and it virtually combines the whole system into one. To demonstrate the performance and efficiency of the system, a robust inverse dynamic algorithm is proposed and implemented for a faster and more precise control. The robust inverse dynamic algorithm is basically derived from an inverse dynamic algorithm and a PID compensator. Based upon the derived dynamic equations of SCARA robot, the inverse dynamic algorithm is initially implemented with l msec of control cycle—0.3 msec is actually used for the control algorithm—in this system. The algorithm is found to be inadequate for the high speed and precision tasks due to inherent modelling errors and time-varying factors. Therefore a variable PID algorithm is combined with the inverse dynamic algorithm to reinforce robustness of control. Experimental data using the proposed algorithm are presented and compared with the results obtained from the PID and the inverse dynamic algorithms.     

New modifying self-organizing fuzzy controller for robotic motion control

The precise motion control of robotic manipulators is important in improving productivity and quality. However, robotic manipulators are multivariable nonlinear dynamic systems. Designing a model-based controller for robotic system control is difficult because its mathematical model is hard to accurately establish. This study proposed a self-organizing fuzzy controller (SOFC) to control a robotic system and evaluate its control performance. The SOFC continually updates the learning strategy in the form of fuzzy rules during the control process. The learning rate and the weighting distribution value of the controller are hard to regulate, so its fuzzy control rules may be modified to such an extent that the system response generally causes oscillatory phenomena. Two fuzzy logic controllers were designed according to the system output error and the error change, and introduced to the SOFC to determine the appropriate parameters of the learning rate and the weighting distribution, in order to eliminate this oscillation. This new modifying self-organizing fuzzy controller (NMSOFC) can effectively improve the control performance of the system, reduce the time consumed to establish a suitable fuzzy rule table, and support practical and convenient fuzzy controller applications. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old robot for a control system to evaluate the feasibility of motion control. Experiment results indicate the NMSOFC has better control performance in reducing the tracking errors of the joint-space trajectories and the positions, and requires less computational time than does the traditional fuzzy controller.     

Wall-following control of a two-wheeled mobile robot

Wall-following control problem for a mobile robot is to move it along a wall at a constant speed and keep a specified distance to the wall.This paper proposes wall-following controllers based on Lyapunov function candidate for a two-wheeled mobile robot (MR) to follow an unknown wall. The mobile robot is considered in terms of kinematic model in Cartesian coordinate system. Two wall-following feedback controllers are designed: full state feedback controller and observer-based controller. To design the former controller, the errors of distance and orientation of the mobile robot to the wall are defined, and the feedback controller based on Lyapunov function candidate is designed to guarantee that the errors converge to zero asymptotically. The latter controller is designed based on Busawon’s observer as only the distance error is measured. Additionally, the simulation and experimental results are included to illustrate the effectiveness of the proposed controllers.     

面向大批量定制的可重构动态调度

动态可重构制造系统是实现大批量定制的关键需求之一。将强化学习方法应用于可重构动态调度系统．给出了一类典型的可重构多机并行调度决策的问题描述。针对该问题建立了基于分布耦合控制的强化学习决策方法。所给方法利用状态分解将原问题转化为一组设备控制器的耦合决策问题，以简化单个控制器的求解空间。在局部决策中进一步采用递阶的分层强化学习控制方法，使得设备排序调度决策与重构决策分离。重构控制器通过排序调度决策信息及外部设备控制器反馈信息调整设备加工模式。达到负荷平衡及优化调度的目的。     

基于网络的开放式双臂机器人控制器

针对开放式控制器的原有结构，提出了一种融合网络的简化分层控制模型，结合本实验室的双臂机器人，详细阐述了基于网络的开放式控制器的硬件结构和软件结构，最后介绍了系统的具体实现方式。     

Neural network controller for robotic motion control

Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations.     

Vision-based stabilization of nonholonomic mobile robots by integrating sliding-mode control and adaptive approach

Vision-based pose stabilization of nonholonomic mobile robots has received extensive attention.At present,most of the solutions of the problem do not take the robot dynamics into account in the controller design,so that these controllers are difficult to realize satisfactory control in practical application.Besides,many of the approaches suffer from the initial speed and torque jump which are not practical in the real world.Considering the kinematics and dynamics,a two-stage visual controller for solving the stabilization problem of a mobile robot is presented,applying the integration of adaptive control,sliding-mode control,and neural dynamics.In the first stage,an adaptive kinematic stabilization controller utilized to generate the command of velocity is developed based on Lyapunov theory.In the second stage,adopting the sliding-mode control approach,a dynamic controller with a variable speed function used to reduce the chattering is designed,which is utilized to generate the command of torque to make the actual velocity of the mobile robot asymptotically reach the desired velocity.Furthermore,to handle the speed and torque jump problems,the neural dynamics model is integrated into the above mentioned controllers.The stability of the proposed control system is analyzed by using Lyapunov theory.Finally,the simulation of the control law is implemented in perturbed case,and the results show that the control scheme can solve the stabilization problem effectively.The proposed control law can solve the speed and torque jump problems,overcome external disturbances,and provide a new solution for the vision-based stabilization of the mobile robot.