机器人控制器的现状及展望

机器人控制器是影响机器人性能的关键部分之一,它 从一定程度上影响着机器人的发展．本文介绍了目前机器人控制器的现状,分析了它们各自 的优点和不足,探讨了机器人控制器的发展方向和要着重解决的问题．     

机器人控制器体系结构研究的现状和发展

本文首先给出了机器人控制器的定义和分类,介绍了机器人控制器体系结构方面的 研究情况,提出了有关机器人体系结构研究发展的几个方向．     

CANopen协议在机器人控制器中的应用

针对当前机器人控制器中通讯方式不统一,复用性差的现状,本文提出了基于CANopen协议的机器人控制器实时通讯方案。首先介绍了控制器局域网络(CAN)的高层协议CANopen,并结合机器人控制器与多个电机驱动器通讯的实际需求,实现了基于CANopen通讯协议的机器人控制器,并在实际机器人系统中进行测试。实验结果表明基于CANopen通讯协议的机器人控制器可以满足机器人控制系统的实时性和灵活性要求。     

开放式机器人控制器综述

本文对开放式机器人控制器的研究进行了概括和总结,综合叙述了开放式机器人控制 器的思想及优点,从控制结构、硬件和软件实现的角度总结了已有的研究工作,指出了开放 式机器人控制器的发展方向．     

用IGA优化的直流电机的仿人智能控制

讨论了当前足球机器人运动控制系统的控制算法对足球机器人运动性能的影响。在分析了足球机器人运动控制系统组成和电机数学模型的基础上,对足球机器人运动控制器采用多模态控制的仿人智能控制（Human-Simulated Intelligent Control,HSIC）算法,利用改进的遗传算法（Improved Genetic Algorithm,IGA）对仿人智能控制器参数进行优化。通过与目前普遍采用的常规PID控制器作对比实验,表明采用IGA参数整定后的HSIC控制器对电机具有更好的控制品质,并改善了足球机器人的运动性能。     

基于干扰观测器的机器人关节控制

提出了基于干扰观测器的仿人机器人关节控制方法。在建立仿人机器人单关节物理模型的基础上,设计了相应的干扰观测器和控制器,解决了关节控制中时变非线性的重力力矩对控制性能的影响。与PID控制器进行性能比较,并探讨了关节模型摄动对干扰观测器的影响,仿真结果表明该方法在仿人机器人关节控制中的可行性和有效性。     

基于超稳定性理论的机器人关节模型参考自适应控制

本文针对机器人电液位置伺服系统的特点,应用POPOV的超稳定性理论,设计了机器人关节模型参考自适应控制器,来提高机器人的位置精度和跟踪性能。实验表明,该控制器能够克服机器人系统固有的时变、非线性等不利因素的影响,具有较好的位置精度和跟踪性能,此外,还具有良好的鲁棒性。     

一种两轮轮式机器人点镇定智能控制实现

针对实际RoboCup机器人,分析了采用双闭环轮速跟随电机执行系统的两轮轮式移动机器人的数学模型,考虑了实际电机系统必然存在的速度饱和、加速度饱和限制对点镇定控制的影响,基于动觉图式的仿人智能控制理论,提出了一种两轮轮式机器人点镇定的分段比例智能控制器,利用遗传算法整定了控制器的参数.比较了比例控制器,比例余弦控制器和提出的智能控制器的仿真控制效果,并在实际RoboCup机器人上有效实现了提出的控制器.仿真和实际系统实验都证明了该控制器的有效性.     

云模型控制器在两轮自平衡机器人中的应用

两轮自平衡机器人控制系统具有高阶次、多变量、非线性且强耦合的特性,因此难以建立精准的数学模型。针对两轮自平衡机器人系统的复杂性,对其平衡控制系统进行了研究,提出了一维云模型控制器的设计方法。运用该方法,成功地实现了两轮自平衡机器人的平衡控制,并比较了一维云模型控制器在三规则和五规则下对系统性能的影响。试验结果表明:一维云模型控制器在两轮自平衡机器人平衡控制系统中具有良好的控制性能和强抗干扰性,五规则控制器具有更加优越的控制效果。云模型控制器成功应用在两轮自平衡机器人平衡系统中,并在试验样机平台体现了良好的平衡性能,为今后云模型控制器的设计提供参考,也推进了云模型控制器在硬件平台实现的进程。     

云控制器在机器人平衡系统中应用与研究

针对云模型理论的研究现状,提出了基于单片机一维云模型控制器的设计方法,应用该方法设计的云模型控制器成功地实现了对轮式机器人动态倒立的平衡控制,同时比较了三规则云模型控制器和五规则控制器对系统的性能影响。实验结果表明,该云模型控制器可以保证轮式机器人倒立平衡系统具有良好的控制性能和较强的抗干扰性,并证明拟合人类思维更好的五规则控制器具有更好的控制效果,为以后规则云模型控制器设计提供了参考。     

A design of a controller as a component of a robotic manufacturing system

This paper specifies the functional design of a robot or machine tool controller suitable for inclusion in robotic manufacturing systems. The principle interest is the unmanned manufacturing cells which are being developed as major components of new manufacturing facilities. The functional specifications are motivated by recent experiences with the creation of a prototype cell for an open die forging process. There are two key technical design requirements. The first relates to the robot or machine tool being controlled. The controller must direct the machine actions, and, for unmanned operation, the controller must receive and process sensor information for process modification and for fault tolerance. The second relates to communication with the central cell computer (the host). The operation of an unmanned system requires a robot communication channel between the host and the controller; the controller must also respond to a variety of instructions transmitted from the host. Additional controller design requirements are imposed by economics. The proposed controller can be used today, and can develop in an evolutionary manner to meet the needs of future manufacturing systems. The evolutionary development is made possible by modular design, organized in a hierarchical manner.     

Model predictive control for fast reaching in clutter

A key challenge for haptically reaching in dense clutter is the frequent contact that can occur between the robot’s arm and the environment. We have previously used single-time-step model predictive control (MPC) to enable a robot to slowly reach into dense clutter using a quasistatic mechanical model. Rapid reaching in clutter would be desirable, but entails additional challenges due to dynamic phenomena that can lead to higher forces from impacts and other types of contact. In this paper, we present a multi-time-step MPC formulation that enables a robot to rapidly reach a target position in dense clutter, while regulating whole-body contact forces to be below a given threshold. Our controller models the dynamics of the arm in contact with the environment in order to predict how contact forces will change and how the robot’s end effector will move. It also models how joint velocities will influence potential impact forces. At each time step, our controller uses linear models to generate a convex optimization problem that it can solve efficiently. Through tens of thousands of trials in simulation, we show that with our dynamic MPC a simulated robot can, on average, reach goals 1.4 to 2 times faster than our previous controller, while attaining comparable success rates and fewer occurrences of high forces. We also conducted trials using a real 7 degree-of-freedom (DoF) humanoid robot arm with whole-arm tactile sensing. Our controller enabled the robot to rapidly reach target positions in dense artificial foliage while keeping contact forces low.     

CAN总线在仿人机器人运动控制系统中的应用

文章先对CAN总线进行了介绍，然后把CAN总线应用到仿人机器人中，设计出一种适合仿人机器人的分布式运动控制系统，并给出了详细的设计过程。整个控制系统层次清晰，结构灵活，对仿人机器人的进一步发展具有积极的作用，同时为现场总线在仿人机器人中的应用提供了重要的参考。     

Implementation of robust impedance and force control

In this paper, we discuss the problem of implementing impedance control in the presence of model uncertainties and its application to robot force control. We first propose a sliding mode-based impedance controller. The implementation of the targeted impedance, and the preservation of stability in the presence of model uncertainties, are the key issues in the proposed approach. Using sliding mode control, a simple and robust algorithm is obtained so that the targeted impedance can be accurately implemented without the exact model of the robot. The controller is designed in terms of the task space coordinates. The chattering in the sliding mode control is eliminated by using a continuous function. The problem of force control is also addressed for the impedance controlled robot. An off-line estimation method of the environment model is suggested and used in the force control scheme. The proposed impedance and force control schemes have been experimentally verified on a two degree-of-freedom direct-drive robot arm. The experimental results are presented in this paper.     

两轮移动机器人运动控制系统的设计与实现

通过对两轮驱动机器人小车的运动模型的分析,提出一种非完整性两轮机器人小车运动控制器的设计方法.在将运动参量角速度和线速度进行解耦的基础上,引入速度控制器,通过反馈抑制了左右轮的扰动及参数差异对控制性能的影响,􀁯并且以数字信号处理器芯片TMS320LF2407A为控制器核心,具体实现了非完整性两轮机器人小车运动控制.实验结果证明了上述方法的有效性.     

基于反步法的移动机器人鲁棒跟踪控制

以四轮移动机器人为研究对象,建立了机器人完整的数学模型,包括运动学模型、动力学模型以及驱动电机模型。在机器人数学模型的基础上,采用反步法的思想设计具有全局收敛特性的鲁棒轨迹跟踪控制器,设计中考虑了驱动电机模型使控制器更符合实际控制要求,并将其分解为运动学控制器、动力学控制器以及电机控制器三部分,降低了控制器设计的难度。构造了系统的李雅普诺夫函数,证明了该类型移动机器人在所得控制器作用下,能实现对给定轨迹的全局渐近追踪。仿真实验结果表明基于反步法的控制器是有效的。     

Human-like natural behavior generation based on involuntary motions for humanoid robots

Human behaviors consist of both voluntary and involuntary motions. Almost all behaviors of task-oriented robots, however, consist solely of voluntary motions. Involuntary motions are important for generating natural motions like those of humans. Thus, we propose a natural behavior generation method for humanoid robots that is a hybrid generation between voluntary and involuntary motions. The key idea of our method is to control robots with a hybrid controller that combines the functions of a communication behavior controller and body balancing controllers. We also develop a wheeled inverted pendulum type of humanoid robot, named “Robovie-III”, in order to generate involuntary motions like oscillation. By applying our method to this robot and conducting preliminary experiments, we verify its validity. Experimental results show that the robot generates both voluntary and involuntary motions.     

Application of a hybrid controller to a mobile robot

This paper presents the application of a hybrid controller to the optimization of the movement of a mobile robot. Through hybrid controller processes, the optimal angle and velocity of a robot moving in a work space was determined. More effective movement resulted from these hybrid controller processes. The experimental scenarios involved a five-versus-five soccer game and a MATLAB simulation, where the proposed system dynamically assigned the robot to the target position. The hybrid controller was able to choose a better position according to the circumstances encountered. The hybrid controller that is proposed includes a support vector machine and a fuzzy logic controller. We used the method of generalized predictive control to predict the target position, and the support vector machine to determine the optimal angle and velocity required for the mobile robot to reach the goal. First, we used the generalized predictive control to predict the target position. Then, the support vector machine is used to classify the angle that must be followed by the mobile robot to reach the goal. Next, a fuzzy logic controller is designed to determine the velocity of the left and right wheels of the mobile robot. Thus generated, the velocity was optimized according to the measures obtained by the support vector machine. Finally, based on the optimal velocity of robot, the output membership function was modified. Consequently, the proposed hybrid controller allowed the robot to reach the goal quickly and effectively.     

自主轮式机器人THMR－V的混合模糊逻辑控制

轮式机器人的控制问题是控制研究的关键问题之一，对高速自主导航的轮式机器人，控制器的实时性、精确性和鲁棒性要求很高．在本文中，根据PID控制和模糊逻辑控制的各自优点，将传统的PID控制与模糊逻辑控制结合起来，提出了一种混合模糊逻辑控制算法. 经实验检验，该算法具有很高的实时性、控制精度和鲁棒性，能够满足机器人高速自主导航的需要．     

Omni-directional mobile robot controller based on trajectory linearization

In this paper, a nonlinear controller design for an omni-directional mobile robot is presented. The robot controller consists of an outer-loop (kinematics) controller and an inner-loop (dynamics) controller, which are both designed using the Trajectory Linearization Control (TLC) method based on a nonlinear robot dynamic model. The TLC controller design combines a nonlinear dynamic inversion and a linear time-varying regulator in a novel way, thereby achieving robust stability and performance along the trajectory without interpolating controller gains. A sensor fusion method, which combines the onboard sensor and the vision system data, is employed to provide accurate and reliable robot position and orientation measurements, thereby reducing the wheel slippage induced tracking error. A time-varying command filter is employed to reshape an abrupt command trajectory for control saturation avoidance. The real-time hardware-in-the-loop (HIL) test results show that with a set of fixed controller design parameters, the TLC robot controller is able to follow a large class of 3-degrees-of-freedom (3DOF) trajectory commands accurately.