Robust Tracking Control for Self-balancing Mobile Robots Using Disturbance Observer

In this paper, a robust tracking control scheme based on nonlinear disturbance observer is developed for the self-balancing mobile robot with external unknown disturbances. A desired velocity control law is firstly designed using the Lyapunov analysis method and the arctan function. To improve the tracking control performance, a nonlinear disturbance observer is developed to estimate the unknown disturbance of the self-balancing mobile robot. Using the output of the designed disturbance observer, the robust tracking control scheme is presented employing the sliding mode method for the selfbalancing mobile robot. Numerical simulation results further demonstrate the effectiveness of the proposed robust tracking control scheme for the self-balancing mobile robot subject to external unknown disturbances.      

基于运动控制和频域分析的移动机器人能耗最优轨迹规划

本文针对两轮自平衡可移动机器人, 提出了一种新的能耗最优运动轨迹规划方法.本文将轨迹规划与由轨迹跟踪控制器和机器人动力学方程组成的运动控制模型相结合, 基于期望轨迹与实际电机输入电压间的传递函数和能量在时域和频域上的对应关系, 通过频域分析的方法得到了具有明确机理表达的线性能耗模型, 并采用最小二乘线性回归法对模型参数进行辨识.对于能耗最优轨迹, 由全局路径规划得到的路径点作为局部轨迹规划的局部目标点, 通过一定的数学转换和参数求导, 可直接得到相邻两个局部目标点间的能耗最优运行轨迹和对应的运行时间.通过仿真实验证明了本文所提能耗模型的准确性和所得轨迹的能耗最优性.     

自平衡两轮移动机器人的LQR控制策略研究

采用dsPIC30F4011作为主控芯片,利用自制倾角传感器、陀螺仪和自制的电机编码器作为系统的传感器,设计了价格低廉的自平衡两轮移动机器人,很适合作为各种控制方法的研究平台。根据机器人的系统参数和运动状态,设计了电机模型、车轮模型及车身前进模型,构建了系统的动力学模型,得到了机器人平衡前进时的状态空间表达式,设计了LQR最优控制器,并进行了MATLAB仿真,得到了预期的平衡效果,验证了系统具有良好的鲁棒性,同时表明硬件设计和系统建模的合理有效性。     

基于并联腿机构的四足仿生机器人开发

本论文主要对生物的节律运动控制机理进行仿生,模仿生物中枢模式发生器(CPG)产生节律运动控制信号,并对其工程模型进行了简化,方便控制方法实现。同时在机器人腿部结构方面,采用了具有高精度和高刚度的并联闭环机构,提高了机器人的承载能力。经实验表明,基于此并联机构腿的四足机器人可以灵活的实现平面、坡面的多种步态的稳定快速运动。     

OCPA仿生自主学习系统及在机器人姿态平衡控制上的应用

针对本质上非线性、强耦合的两轮自平衡机器人复杂动态系统,构造操作条件反射概率自动机(OCPA)仿生自主学习系统。OCPA仿生自主学习系统是一个基于Skinner操作条件反射的概率自动机,主要特征在于模拟生物的操作条件反射机制,具有仿生的自组织功能,包括自学习和自适应功能,可用于描述、模拟、设计各种自组织系统。从理论上分析OCPA学习系统的操作条件反射学习机制的收敛性。应用于两轮机器人姿态平衡控制的仿真和实验结果均表明,设计的OCPA仿生自主学习系统不需要系统的模型,通过模拟生物的操作条件反射机制,自组织地渐进形成、发展和完善其姿态平衡控制技能。     

简易两轮自平衡机器人的设计与制作

两轮自平衡机器人与倒立摆的基本原理相同,都是自动控制理论的典型研究对象,但要自行制作都有一定难度。介绍了用市场上很容易买到的玩具车改装而成的两轮自平衡机器人。在单片机的控制下,实现了连续稳定的直立平衡。可以作为自动控制系统的实验平台,进行各种控制算法的可行性与效果验证。     

基于竞赛自平衡小车的双闭环控制设计与仿真研究

针对具有多变量、不稳定、非线性特性的自平衡小车系统,提出了一种基于偏角伺服控制与LQR控制相结合的双闭环控制方法.在该方法中,先根据系统的等效模型,采用LQR方法控制自平衡小车的倒立平衡,采用伺服控制进行偏角跟随控制.并且在设计过程中,先保证自平衡小车的倒立平衡,然后再进行伺服控制,运行中两个闭环实时控制,达到使小车倒立快速寻迹运行.数字仿真结果与理论分析基本一致,表明了该方法的可行性.     

基于Robotics Toolbox的机器人分步速度控制仿真研究

分步速度控制方法是利用雅可比矩阵计算关节速度以得到预定的笛卡尔速度。文章首先介绍了分步速度控制方法,然后在Matlab环境下,利用Robotics Toolbox建立了平面3R机器人,最后编写M文件对该机器人进行了分步速度控制仿真。通过仿真,得到了机器人各个关节的运动与时间的关系,实现了预定的目标,进而验证了分步速度控制方法的正确性和可行性。     

模糊操作条件概率自动机仿生自主学习系统和机器人自平衡控制

为了实现两轮机器人的自平衡控制, 利用Skinner操作条件反射机理, 以概率自动机为平台, 融入模糊推理, 构造了模糊操作条件概率自动机(OCPA)仿生自主学习系统. 该学习系统是一个从状态集合到操作行为集合的随机映射, 采用操作条件反射学习机制, 从操作行为集合中随机学习作为控制系统控制信号的最优行为, 并利用学习到的操作行为取向值信息, 调整操作条件反射学习算法. 此外, 学习系统还引入行为熵, 以验证其自学习和自组织能力. 应用于两轮机器人自平衡控制的仿真结果, 验证了模糊OCPA学习系统的可行性.     

Jerk-level synchronous repetitive motion scheme with gradient-type and zeroing-type dynamics algorithms applied to dual-arm redundant robot system control

Dual-arm redundant robot systems are usually required to handle primary tasks, repetitively and synchronously in practical applications. In this paper, a jerk-level synchronous repetitive motion scheme is proposed to remedy the joint-angle drift phenomenon and achieve the synchronous control of a dual-arm redundant robot system. The proposed scheme is novelly resolved at jerk level, which makes the joint variables, i.e. joint angles, joint velocities and joint accelerations, smooth and bounded. In addition, two types of dynamics algorithms, i.e. gradient-type (G-type) and zeroing-type (Z-type) dynamics algorithms, for the design of repetitive motion variable vectors, are presented in detail with the corresponding circuit schematics. Subsequently, the proposed scheme is reformulated as two dynamical quadratic programs (DQPs) and further integrated into a unified DQP (UDQP) for the synchronous control of a dual-arm robot system. The optimal solution of the UDQP is found by the piecewise-linear projection equation neural network. Moreover, simulations and comparisons based on a six-degrees-of-freedom planar dual-arm redundant robot system substantiate the operation effectiveness and tracking accuracy of the robot system with the proposed scheme for repetitive motion and synchronous control.     

Estimation of attraction domains in wheeled robot control

Considered is the control design problem for planar motion of a wheeled robot. The mathematical model of the robot accounts for kinematic relationships between the velocity of a given point of chassis referred to as the reference point, orientation of the chassis, and control. Among the kinematic relations is the requirement that each of the four wheels perform a slip-free motion. The rear wheels are assumed to be driving while the front wheels are responsible for the rotation of the chassis. The control objective is to place the reference point in the prespecified trajectory and to stabilize the motion of the reference point along the prespecified trajectory. The trajectory consists of line segments and circular arcs. In the mathematical model under consideration, the current curvature of the trajectory of the reference point is taken as control; it is related to the steering angle of the front wheels by a simple algebraic expression. The control is subject to two-sided constraints due to limitations on the steering angle of the front wheels. For the control law proposed, the attraction domain in the space “distance to the trajectory—orientation” is analyzed. For the initial conditions from this domain, the system is guaranteed to hit a trajectory with given index of exponential stability.     

Inverse dynamics of the 3-PRR planar parallel robot

Recursive modelling for the kinematics and dynamics of the known 3-PRR planar parallel robot is established in this paper. Three identical planar legs connecting to the moving platform are located in a vertical plane. Knowing the motion of the platform, we develop first the inverse kinematics and determine the positions, velocities and accelerations of the robot. Further, the principle of virtual work is used in the inverse dynamics problem. Several matrix equations offer iterative expressions and graphs for the power requirement comparison of each of three actuators in two different actuation schemes: prismatic actuators and revolute actuators. For the same evolution of the moving platform in the vertical plane, the power distribution upon the three actuators depends on the actuating configuration, but the total power absorbed by the set of three actuators is the same, at any instant, for both driving systems. The study of the dynamics of the parallel mechanisms is done mainly to solve successfully the control of the motion of such robotic systems.     

An Attempt to Raise the Level of Software Abstraction in Assembly Robotics through an Apposite Choice of Underlying Mechatronics

Robot manipulators were meant to be the production engineer"s flexible friend. Assembly robots, however, have failed to fulfill their promise. The problem that has continuously plagued robotic assembly is that of spatial uncertainty. It is our thesis that the ubiquitous problem of spatial uncertainty is an artefact of the fact that current industrial manipulators are designed for an operational paradigm that assumes position control is of primary importance. In this paper we propound an alternative approach based on sliding as the primary motion primitive. We first present a model that uses sliding to allow us to raise the level of abstraction of robot programming tasks. We then describe an inherently accommodating, (planar) three degree of freedom, direct-drive robot arm that was constructed to test our approach. Finally, we present data collected from representative (planar) manipulation tasks that substantiate our claims.     

Global regulation of a planar robot arm striking a surface

Considers the problem of modeling and controlling the impact of a two-degree-of-freedom planar robot arm against an infinitely rigid and massive surface. For this case study, the basic equations describing the motion of the robot arm are derived for noncontact and contact conditions. A control scheme is proposed on the basis of a reduced-order observer that is able to asymptotically estimate the impact-induced forces and to allow their asymptotic compensation when the robot arm is in contact with the surface. The resulting control system ensures the global asymptotic regulation of the position of the arm with an assigned impact-induced force. The case study is completed by a simulation test     

基于单神经元控制器的独轮自平衡机器人双闭环自适应控制

针对竖直飞轮的独轮自平衡机器人系统,提出了一种基于自适应单神经元控制的双闭环(DLSN)控制方法.根据对独轮自平衡机器人动力学模型的分析,将独轮自平衡机器人分成两个子系统,提出了一种具有俯仰倾角和横滚倾角内环、前向位移外环的双闭环自适应控制结构,其中每个控制环均由单神经元自适应控制器构成.仿真实验结果表明:所设计的基于双闭环单神经元自适应独轮自控制方法是有效的.     

Maneuvering control of a rolling four-links robot

This work deals with the maneuvering control of the planar motion of a rolling four-links robot as described in Figure 1. The system is composed of four links, two identical wheels, and a mass m_O attached to the joint O. The problem that is addressed is to develop control laws for the rolling four-links robot such that the mass m_O performs prescribed maneuvers in the vertical (X, Z)-plane.     

基于遗传算法模式匹配的机器人实时视觉伺服

对于机器人手臂来讲 ,对工作环境的识别是完成一个智能任务的最重要的问题之一 .因为这种智能可以使它工作在一个变化的环境中 .本文提出了一种新的机器人手臂的控制策略 ,可以利用视觉信息来指导机器人的手臂在它的工作空间中捡起一个已知形状但任意位置和方向的物体 .在对物体的搜索过程中 ,利用基于视觉闭环的视觉伺服来完成对机器人手臂的运动控制 .本系统利用遗传算法 (Genetic Algorithm ,GA)和模式匹配技术完成对搜索空间的搜索并获得了良好的结果 .本文完成了对带有两连杆手臂的视觉伺服系统的仿真 ,仿真结果证明了算法的有效性     

Cable-direct-driven-robot (CDDR) with a 3-link passive serial support

Cable-direct-driven-robots (CDDRs) provided with a passive serial support represent an interesting and rising evolution of planar cable robots. The paper is devoted to present and analyze a novel CDDR robot. The robot consists in a fully actuated CDDR supported against loading normal to the motion plane with a 3-link passive planar serial manipulator. This hybrid structure combines positive features of both parallel and serial architectures, and prevents out-of-plane movements without the necessity for the robot to be supported on the motion plane. The adoption of a 3-link serial manipulator ensures a greater workspace area compared with similar structures that adopt a smaller number of links, and improves specific characteristics of their dynamics. Nevertheless undesired oscillations may occur since the serial manipulator is underconstrained. For this reason damping elements are inserted in the structure. Simulation examples are presented to demonstrate the novel CDDR concept and its dynamics. In addition, a strategy to select proper values of damping coefficients is presented.     

Robust adaptive motion/force tracking control design for uncertain constrained robot manipulators

In the presence of uncertain constraint and robot model, an adaptive controller with robust motion/force tracking performance for constrained robot manipulators is proposed. First, robust motion and force tracking is considered, where a performance criterion containing disturbance and estimated parameter attenuations is presented. Then the proposed controller utilizes an adaptive scheme and an auxiliary control law to deal with the uncertain environmental constraint, disturbances, and robotic modeling uncertainties. After solving a simple linear matrix inequality for gain conditions, the effect from disturbance and estimated parameter errors to motion/force errors is attenuated to an arbitrary prescribed level. Moreover, if the disturbance and estimated parameter errors are square-integrable, then an asymptotic motion tracking is achieved while the force error is as small as the inversion of control gain. Finally, numerical simulation results for a constrained planar robot illustrate the expected performance.     

Skinner 操作条件反射的一种仿生学习算法与机器人控制

针对两轮自平衡机器人的运动平衡控制问题,提出了基于Skinner 操作条件反射理论的BP 神经网络 与资格迹相结合的仿生自主学习算法作为两轮机器人的学习机制．该算法利用资格迹能解决延迟影响、加快学习速 度和提高可靠性的特点,将其与BP 神经网络相结合构成复合学习算法,能够预测机器人将要获得的行为评价函数, 并依据概率取向机制以一定的概率选择最大评价值对应的最优行为,从而使机器人能够在未知环境下通过与环境的 交互、学习和训练,获得像人或动物一样的自主学习技能,实现对两轮机器人的运动平衡控制．最后,分别用基于 Skinner 操作条件反射理论的BP 算法和BP 资格迹复合算法对两轮机器人做了仿真实验并进行了比较．结果表明, 基于Skinner 操作条件反射理论的BP 资格迹复合仿生自主学习算法的学习机制能够使机器人获得良好的动态性能 和较快的学习速度,体现了机器人较强的自主学习技能和平衡控制能力．