双连杆柔性臂轨迹跟踪控制

研究了在垂直平面内双连杆柔性机械臂的轨迹跟踪控制问题。基于假设模态法，导出了双连杆柔性机械臂系统的动力学方程，给出了轨迹跟踪控制的补偿控制方法。设计了以微机为核心的柔性臂系统实验台。最后，通过实验台对提出的控制方法进行了实验研究。     

柔性机械臂的一种组合控制

在柔性机械臂轨迹控制中,要实现定位目标的同时必须消除柔性振动,利用奇异摄动方法将柔性臂系统分解为慢变和快变两个降阶子系统,并对慢变和快变子系统分别采用滑模控制方法和H∞控制方法设计了控制器。采用Matlab进行的仿真结果表明,所设计的组合控制器是有效的。控制性能稳定。     

柔性臂振动的双目三维检测和主动控制

针对柔性臂的振动问题,设计并搭建了一套基于双目视觉检测,以伺服电机驱动控制的柔性臂实验平台进行研究。根据电机的运行状态将柔性臂振动分为定点振动和旋转振动,通过双目视觉对柔性臂上的特征点进行三维重建,进而获取柔性臂的定点和旋转振动信号。对振动信号进行模态分析获取柔性臂的振动特性,并据此设计基于蚁群优化参数的PD控制器抑制柔性臂的弯曲振动,然后与常规PD控制效果进行比较。实验结果表明,基于蚁群优化参数的PD控制能够在较短时间内有效抑制柔性臂的定点振动,相较于常规PD控制抑振效果有明显改善,并且旋转振动的抑制效果也很显著,以此验证了蚁群PD控制算法在快速抑振上的有效性。     

双重驱动双连杆柔性臂系统建模与抑振仿真

以宏微双重驱动的双连杆柔性臂机器人为背景，研究其建模与振动控制问题。通过忽略弹性振动对刚性运动的影响，用拉格朗日方程建立了对刚性运动模型，建立了由伺服电机和压电陶瓷共同作用下的振动方程，然后在刚性模型基础上叠加振动模型实现了对柔性臂的描述；这种模型的优点是系统总的模型简单且易于同时考虑多个振动模态。最后给出了压电陶瓷抑振过程的计算机仿真结果。     

永磁直线电机驱动单连杆柔性臂系统的试验模型辨识

针对永磁直线电机（PMLM）驱动单连杆柔性机械臂（SLFM）做点到点运动时存在残余振动从而影响柔性臂末端迅速精准定位的问题,研究了基于PMLM平动柔性臂系统的试验模型辨识问题。建立了从位置指令到柔性臂根部应变振动信号的传递函数模型,并对模型采用阶跃输入法和扫频激励法进行了试验辨识,为开展基于PMLM的平动柔性臂系统抑制残余振动控制提供了模型基础。试验结果得出辨识模型在2种激励信号下的仿真输出与实际结构动态响应的吻合度均较高,验证了模型辨识结果的正确性,并反映了基于PMLM的平动柔性臂系统的动力学特性。     

柔性机械臂末端位置的滑模变结构控制

用拉格朗日结合假设模态法推出了单连杆柔性机械臂的动力学模型并转化成适于控制系统综合的状态空间表达式,然后用滑模可达条件推出了柔性机械臂末端位置的变结构控制律,最后了仿真研究。     

双连杆柔性臂机器人的模糊补偿滑模控制

提出了宏微双重驱动双连杆柔性臂机器人递阶控制系统结构，下层为根据柔性臂刚性模态设计的实现关节角位置控制的滑模算法和压电陶瓷抑振器，上层为根据关节角误差信号和滑模函数变化趋势建立的模糊推理表用以确定对滑模控制律的修正。借助自动机模型分析了系统的稳定性，将控制律在包含柔性模态的完整柔性臂机器人模型上进行计算机仿真，取得了满意的结果。     

柔性机器人臂动力学模型的研究

对平面双连杆柔性机器人臂的动力学建模问题进行研究,并用Lagrange方程对双连杆柔性机器人臂进行了动力学建模,得到了以机器人臂关节转角为参量的系统运动方程和系统柔性连杆的振动方程。由此建立的数学模型结构简单,精度较高,为柔性机器人臂的控制研究提供了理论依据。     

柔性机械臂运动控制策略的研究

讨论一种双映射神经网络,并采用递阶进化规划学习算法对该网络拓扑结构和参数进行优化,实现了柔性机械臂的轨迹跟踪控制。计算机仿真表明该控制策略可以实现柔性机械臂的运动控制,且具有较高的控制精度。     

基于行波超声波电机直接驱动的机械臂精密定位系统研制

研制了基于行波超声波电机与步进电机驱动的机器人多自由度机械臂精密定位控制系统。采用行波超声波微步控制技术实现了机械臂低速下的高精度定位控制，并在理论与试验研究基础上提出了一种实用的行波超声波电机精密定位控制方法。此外，还进行了定位误差分析及控制系统设计。所给的方法简单易行，在超声波电机精密定位中具有普遍的应用意义和广泛的应用价值。该方法成功运用于机器人机械臂控制中，不需要使用高精度角位移传感器就可使机械臂达到很高的定位精度。     

Learning gravity compensation in robots: Rigid arms,elastic joints,flexible links

The setpoint regulation problem for robotic manipulators is a basic task that can be solved either by PID control or by model-based gravity compensation. These approaches are commonly applied both to rigid arms and to robots with flexible links and/or elastic joints. However, PID control requires fine and lengthy tuning of gains in order to achieve good performance over the whole workspace. Moreover, no global convergence proof has yet been given for this control law in the case of flexible links or elastic joints. On the other hand, a constant or even a configuration-dependent gravity compensation is only an approximate solution when an unknown payload is present or when model parameters are poorly estimated. In this paper a simple iterative scheme is proposed for generating exact gravity compensation at the desired setpoint without the knowledge of dynamic model terms. The resulting control law is shown to be global asymptotically stable for rigid arms as well as for manipulators with elastic joints or flexible links. Starting with a PD action on the error at the joint (i.e. motor) level, an additional feedforward term is built and updated at discrete instants. Convergence of the scheme is proved under a mild condition on the proportional gain, related to a bound on the gravity terms. In the presence of concentrated or distributed flexibility a structural property of the joint or of the link stiffness is further required, largely satisfied in practice. Simulation results are given for a three-link rigid arm and experimental results are also presented for a two-link robot with a flexible forearm.     

A learning control algorithm for periodic robot synchronization: Experimental results

A repetitive learning control algorithm, that achieves asymptotic joint position tracking for robotic manipulators characterized by uncertain dynamics and performing a repetitive task, can be theoretically and experimentally endowed with a recursive period identifier. Experimental results illustrate its application to a 2‐link robot master‐slave synchronization problem, in which the joint positions of the master, ie, “periodic” with uncertain and even time‐varying period, are only available at runtime.     

基于TMS320F206 DSP的冗余度TT-VGT机器人的轨迹控制

文章提出了采用多个TMS320F206 DSP芯片,将冗余度四面体变几何桁架（TT-VGT）机器人的轨迹现划计算与关节运动控制并行的方案;并且在程序设计中采用了多种技巧以实现优化计算;最后实现了冗余度TT-VGT机器人的实时控制。     

变刚度柔性关节机构的驱动调节控制优化

变刚度柔性关节机构在主-被动复合驱动调节时受到关节部位阻尼的作用,导致驱动调节的全局稳态性不好,提出一种基于复合式幅值饱和非线性状态反馈的变刚度柔性关节机构优化控制设计方法,构造变刚度柔性关节机构的动力学模型,以柔性关节等效刚度、阻尼力和关节弹簧压缩量等参量为控制约束参量,建立变刚度柔性关节机构的被控对象模型,结合幅值饱和非线性状态反馈控制方法进行自适应阻尼误差修正,根据主-被动复合刚度特性进行变刚度柔性关节机构的驱动惯性参量调节,实现关节机构驱动调节控制。仿真结果表明,采用该方法进行变刚度柔性关节机构控制的稳定性较好,驱动调节能力较强。     

BACKSTEPPING-BASED HYBRID ADAPTIVE CONTROL OF ROBOT MANIPULATORS INCORPORATING ACTUATOR DYNAMICS

By using the integrator backstepping technique, the control of rigid link, electrically driven robot manipulators is addressed in the presence of arbitrary uncertain manipulator inertia parameters and actuator parameters. The control scheme developed is computationally simple owing to the avoidance of the derivative computation of the regressor matrix. Semiglobal asymptotic stability of the controller is established in the Lyapunov sense. Simulation results are included to demonstrate the tracking performance. © 1997 by John Wiley & Sons, Ltd.     

Tracking control for flexible joint robots based on adaptive fuzzy compensation with uncertain parameters

This article presents a control scheme for flexible joint robots which has uncertain parameters based on adaptive fuzzy compensation. Considering the unknown parameters, the proposed state feedback control approach utilizes measured variables to establish a cascade structure that is based on simplified dynamics. After reducing the number of fuzzy rules, the adaptive fuzzy logic system is added as compensation to decrease the approximated errors, and the robust terms are also used to enhance the robustness of closed-loop system. Then, the global asymptotic stability could be confirmed through Lyapunov stability principle and Barbalat's lemma. Compared with the other two controllers, the proposed control method has not only higher position accuracy and better dynamic performance but also robustness to the approximation of motor inertia, friction torque and link torque. Some simulation experiments are conducted to show the validity of the proposed scheme.     

Adaptive backstepping repetitive learning control design for nonlinear discrete‐time systems with periodic uncertainties

This paper addresses a tracking problem for uncertain nonlinear discrete‐time systems in which the uncertainties, including parametric uncertainty and external disturbance, are periodic with known periodicity. Repetitive learning control (RLC) is an effective tool to deal with periodic unknown components. By using the backstepping procedures, an adaptive RLC law with periodic parameter estimation is designed. The overparameterization problem is overcome by postponing the parameter estimation to the last backstepping step, which could not be easily solved in robust adaptive control. It is shown that the proposed adaptive RLC law without overparameterization can guarantee the perfect tracking and boundedness of the states of the whole closed‐loop systems in presence of periodic uncertainties. In addition, the effectiveness of the developed controller is demonstrated by an implementation example on a single‐link flexible‐joint robot. Copyright © 2014 John Wiley & Sons, Ltd.