考虑柔性关节的机械臂自适应运动控制策略研究

具有柔性关节的轻型机械臂因其自重轻、响应迅速、操作灵活等优点,取得了广泛应用;针对具有柔性关节的机械臂系统的关节空间轨迹跟踪控制系统动力学参数不精确的问题,提出一种结合滑模变结构设计的自适应控制器算法;通过自适应控制的思想对系统动力学参数进行在线辨识,并采用Lyapunov方法证明了闭环系统的稳定性;仿真结果表明,该控制策略保证了机械臂系统对期望轨迹的快速跟踪,具有良好的跟踪精度,系统具有稳定性。     

Uncertainty analysis on process responses of conventional spinning using finite element method

Conventional spinning is a widely used metal forming process to manufacture rotationally axis-symmetric and asymmetric products. Considerable efforts have been made to investigate the forming quality of spun parts using the process in recent years. However, inherent uncertainty properties involved in the spinning process are rarely considered in previous studies. In this paper, an uncertainty analysis and process optimisation procedure have been developed and implemented on conventional spinning with 3D Finite Element Method (FEM). Three process variables are randomized by Gaussian distribution to study the probabilistic characteristics of two process responses. Linear and quadratic approximate representations are constructed by Monte Carlo based Response Surface Method (RSM) with Latin Hypercube Sampling (LHS). The Most Probable Point (MPP) method, which has been widely used to estimate the failure probability in other applications, is further developed in this paper to obtain the probability distribution of the system responses. Following an evaluation of the system responses conducted by the MPP method, a control variable method is used to reduce the variance of spun part wall thickness and total roller force to satisfy the 3σ quality requirement. This uncertainty analysis and process optimisation procedure can be easily implemented in other metal spinning processes.     

基于RBF-BP算法的机械臂多自由度分拣控制系统设计

为提升机械臂设备的准确分拣能力,设计基于RBF-BP算法的机械臂多自由度分拣控制系统。利用RBF-BP前馈神经网络,规划机械臂设备的运动轨迹路线,实现基于RBF-BP算法的机械臂运动轨迹建模。确定工业相机在总功能框架中所处连接位置,根据机械臂选型情况,确定可编程逻辑控制器、变频控制器对于所选机械臂元件的调节能力,完成对系统功能模块的开发。借助传输信道,将Sorting分拣指令、ToolControl控制指令反馈回核心控制主机,建立完整的指令执行回路,再联合相关硬件设备结构,实现基于RBF-BP算法的机械臂多自由度分拣控制系统设计。实验结果表明,在抓取能力相同的情况下,应用RBF-BP算法控制系统机械臂成功分拣的工件数量为19件,机械臂抓取成功率为95%,说明所设计系统满足提升机械臂准确分拣能力的设计初衷。     

电能表检验台体串联机械臂时间最优轨迹规划

时间最优轨迹规划有助于缩短机械臂运动时间,提高工作效率,在机械臂实际应用场景中起着至关重要的作用.针对串联机械臂点到点运动的时间最优轨迹规划问题,提出一种基于改进多种群遗传算法的最优轨迹规划方法.通过五次多项式插值对机械臂运动路径进行拟合,利用改进的多种群遗传算法对机械臂运动时间进行优化,改进之处包括:设计含有惩罚项的...     

基于不确定逼近的机械手自适应鲁棒预测控制

针对具有参数不确定性和未知外部干扰的机械手轨迹跟踪问题提出了一种多输入多输出自适应鲁棒预测控制方法. 首先根据机械手模型设计非线性鲁棒预测控制律, 并在控制律中引入监督控制项; 然后利用函数逼近的方法逼近控制律中因模型不确定性以及外部干扰引起的未知项. 理论证明了所设计的控制律能够使机械手无静差跟踪期望的关节角轨迹. 仿真验证了本文设计方法的有效性.     

Hybrid Force and Vision-Based Contour Following of Planar Robots

Manipulators interacted with uncalibrated environments have limited dexterity due to constraints imposed by unknown environments. However, to perform science or industrial operations, it is necessary to be able to position and orient these manipulators on targets in order to accomplish required control tasks. This article describes how one might enhance manipulator dexterity for planar contour following tasks using hybrid force and vision-based control. The proposed control approach can guarantee task precision employing only a single-axis force sensor and an imprecisely calibrated CCD camera whose optical axis is perpendicular to the planar workspace. The goal of the autonomous task is to drive an instrument mounted on the end-effector of a planar robotic manipulator to follow a visually determined planar contour and continue tracking the contour in desired pose, contact force, and speed, all demanding time-varying, with precision. The proposed control architecture is suitable for applications that require simultaneous force and pose control in unknown environments. Our approach is successfully validated in a real task environment by performing experiments with an industrial robotic manipulator.     

快速连续反应-避障作业环境下的七自由度灵巧臂轨迹规划

人类经长期学习训练后能对高速物体 (如棒球、乒乓球等)具有快速连续反应作业的运动技能, 从深层次上揭示是由于人体在其训练过程中不断学习优选了相应手臂的动作轨迹, 并储存了丰富的经验和知识. 受人体手臂动作此行为机制启发, 本文提出一种 7-DOF灵巧臂快速连续反应-避障作业的轨迹规划方法. 该方法将灵巧臂对高速物体目标作业的轨迹规划问题转化为动作轨迹参数化优选问题, 考虑作业过程中灵巧臂的机构物理约束和障碍约束条件, 以灵巧臂目标可作业度指标构建适应度函数, 采用粒子群优化 (Particle swarm optimization, PSO)方法优选作业轨迹中的冗余参数; 在此基础上 利用灵巧臂动作轨迹参数化优选方法构建相应作业环境下的知识数据库, 实现灵巧臂对高速物体目标的快速连续反应作业. 以仿人机器人乒乓球对弈作业为例, 将该方法应用于 7-DOF灵巧臂乒乓球作业的轨迹规划中. 数值实验及实际对弈试验结果表明, 该方法不仅能使灵巧臂所规划的轨迹 满足灵巧臂机构物理约束与障碍约束条件, 同时能实现灵巧臂对乒乓球体的快速连续反应作业, 验证了该方法的有效性.     

面向机械臂轨迹规划的强化学习奖励函数设计

针对基于深度强化学习的机械臂轨迹规划方法学习效率较低,规划策略鲁棒性差的问题,提出了一种基于语音奖励函数的机械臂轨迹规划方法,利用语音定义规划任务的不同状态,并采用马尔科夫链对状态进行建模,为轨迹规划提供全局指导,降低深度强化学习优化的盲目性。提出的方法结合了基于语音的全局信息和基于相对距离的局部信息来设计奖励函数,在每个状态根据相对距离与语音指导的契合程度对机械臂进行奖励或惩罚。实验证明,设计的奖励函数能够有效地提升基于深度强化学习的机械臂轨迹规划的鲁棒性和收敛速度。     

Control of robotic manipulators—a neural network approach

A neural-network-based scheme is used for the control of a robotic manipulator. The main idea is that, by using a neural network to learn the characteristics of the robot system (or specifically its inverse dynamics), accurate trajectory following and good performance results are obtained. However, the traditional back-propagation algorithm commonly used for control and identification of nonlinear systems suffers from a slow rate of convergence. We investigate the effect of adusting the slope of the activation function (the node nonlinearity) on the performance of a back-propagation algorithm. It is shown that learning speed is increased significantly by making the slope of non-linearity adaptive. The results demonstrate that the proposed method gives better error minimization and faster convergence. The suggested method is applied to a two-link robotic manipulator. The resulting controller is sufficiently robust with respect to the changing conditions.     

基于ANFIS的机械手运动学逆解的求取

提出一种基于自适应神经模糊推理系统（ANFIS）求取机械手运动学逆解的方法。本文以SCARA（Selective Compliance Assembly Robot Arm）型四自由度机械手为研究对象,研究SCARA机械手末端执行器笛卡儿空间坐标与机械手关节空间关节变量之间的对应关系。首先根据笛卡儿运动轨迹选取起点、终点和中间点,并求得与之对应的关节变量值序列。然后利用插值方法求得关节空间的角度变化曲线,最后在关节曲线上随机选取样本点,进而利用得到的数据训练并验证自适应神经模糊推理系统求解逆解的正确性和精确性。与传统基于BP神经网络求取运动学逆解的方法进行仿真对比分析,结果表明ANFIS在运动学逆解的求取精度和运算时间上均优于BP神经网络。     

Unconstrained and constrained motion control of a planar two-link structurally flexible robotic manipulator

Unconstrained and constrained motion control of a planar two-link structurally-flexible robotic manipulator are considered in this study. The dynamic model is obtained by using the extended Hamilton's principle and the Galerkin criterion. A method is presented to obtain the linearized equations of motion in Cartesian space for use in designing the control system. The approach to solving the control problem is to use feedforward and feedback control torques. The feedforward torques maneuver the flexible manipulator along a nominal trajectory and the feedback torques minimize any deviations from the nominal trajectory. The feedforward and feedback torques are obtained by solving the inverse dynamics problem for the rigid manipulator and designing linear quadratic Gaussian with loop transfer recovery (LQG/LTR) compensators, respectively. The LQG/LTR design methodology is exploited to design a robust feedback control system that can handle modeling errors and sensor noise, and operate on Cartesian space trajectory errors. Computer simulated results are presented for an example planar, two-link, structurally flexible robotic manipulator. © 1994 John Wiley & Sons, Inc.     

State Space Constrained Iterative Learning Control for Robotic Manipulators

Real‐life work operations of industrial robotic manipulators are performed within a constrained state space. Such operations most often require accurate planning and tracking a desired trajectory, where all the characteristics of the dynamic model are taken into consideration. This paper presents a general method and an efficient computational procedure for path planning with respect to state space constraints. Given a dynamic model of a robotic manipulator, the proposed solution takes into consideration the influence of all imprecisely measured model parameters, making use of iterative learning control (ILC). A major advantage of this solution is that it resolves the well‐known problem of interrupting the learning procedure due to a high transient tracking error or when the desired trajectory is planned closely to the state space boundaries. The numerical procedure elaborated here computes the robot arm motion to accurately track a desired trajectory in a constrained state space taking into consideration all the dynamic characteristics that influence the motion. Simulation results with a typical industrial robot arm demonstrate the robustness of the numerical procedure. In particular, the results extend the applicability of ILC in robot motion control and provide a means for improving the overall trajectory tracking performance of most robotic systems.     

基于专家PID的带臂四旋翼无人机控制方法

相较于未带臂的无人机,带臂无人机（UAV）的飞行轨迹会出现较大偏差,更难以稳定控制。为了解决带臂UAV的精确轨迹控制问题,提出基于专家PID的带臂四旋翼无人机控制方法。首先,将机械臂搭载于UAV上把它们作为整体,并通过拉格朗日方程建立了带臂UAV的运动学和动力学系统模型;其次,设计专家PID控制器用于对系统的稳定控制;然后利用五次多项式对带臂UAV的机械臂进行轨迹规划;最后,通过仿真验证专家PID控制方法对带臂UAV稳定控制的有效性。实验结果表明,相较于常规PID控制,所提基于专家PID的控制方法提高了系统的响应速度,且能够有效地抑制外部扰动;该方法对于动作情况下的机械臂能够稳定地跟踪其轨迹,且具有很好的抗扰性和鲁棒性。     

Application of variable search space genetic algorithms to fine gain tuning of model-based robotic servo controller

In this paper, genetic algorithms with a variable search space function are proposed for fine gain tuning of a resolved acceleration controller which is one of model-based robotic servo controllers. Genetic algorithms proposed in this paper have a variable search space function which is activated if the optimal solution is not updated for a fixed number of generations. The function is terminated when the optimal solution is updated, or if the optimal solution is not updated within certain generations. This proposed method is evaluated through a trajectory following control problem in simulation. Simulations for sine curve trajectories are conducted using the dynamic model of the PUMA560 manipulator. The result shows the improvement of optimal solution and its convergence.     

Minimum effort factor approach for sensor-based control of a four-joint 6-dof redundant manipulator

A sensor-driven control model and a minimum effort control algorithm in terms of time and energy expended during the execution of a movement strategy are described and validated for a multijointed cooperating robotic manipulator. Considering smooth, human-like (anthropomorphic) movements, using joint motion profiles achievable in real time as well as sensory information from all joints, and evaluating the total work expended by each manipulator joint during the execution of a movement strategy, a minimum effort motion trajectory is synthesized to precisely and efficiently position the robotic arm end-effector. This sensor-based approach significantly reduces the computational requirements for such cooperative motion. The minimum effort control algorithm generates several human-like arm movement strategies and selects the best strategy on the basis of expendable effort. The algorithm has an inherent basis to deal with obstacles in an efficient way. Detailed examples are described from the simulation studies. © 1994 John Wiley & Sons, Inc.     

Determining the initial configuration of uninterrupted redundant manipulator trajectories in a manufacturing environment

In a manufacturing environment where a redundant manipulator is programmed to follow a specific trajectory such as welding, painting, and soldering, it is often the case that the manipulator reaches a singular configuration where the program is stopped, the manipulator has to be switched to a new configuration, and the motion continued. This paper presents a method of determining the initial configuration of robotic manipulators to traverse a path trajectory without halting the motion. The method consists of: (1) identifying singular curves/surfaces using the Jacobian rank deficiency method; (2) determining non-crossable curves/surfaces using acceleration-based crossability criteria; (3) finding if the path intersects any non-crossable singular curve/surface. If there is not any intersection, the manipulator does not interrupt with any initial configuration on the trajectory. Otherwise, the intersection is determined, and the non-crossable singular configuration at the intersection point is determined; (4) solving the differential algebraic system of equations (DAE) of index 2 using the Runge–Kutta method; and (5) solving the optimization problem. The method is illustrated through examples.     

基于干扰观测器的机械臂广义模型预测轨迹跟踪控制

为实现对多自由度机械臂关节运动精确轨迹跟踪,提出一种基于非线性干扰观测器的广义模型预测轨迹跟踪控制方法。针对机械臂轨迹跟踪运动学子系统,采用广义预测控制（Generalized Predictive Control,GPC）方法设计期望的虚拟关节角速度。对于机械臂轨迹跟踪动力学子系统,考虑机械臂的参数不确定性和未知外界扰动,利用GPC方法设计关节力矩控制输入,基于非线性干扰观测器方法实时估计和补偿系统模型中的不确定性。在李雅普诺夫稳定性理论框架下证明了机械臂关节角位置和角速度的跟踪误差最终收敛于零的小邻域。数值仿真验证了所提出控制方法的有效性和优越性。     

Tolerance design of manipulator parameters using design of experiment approach

A robotic arm must manipulate objects with high accuracy and repeatability to perform precise tasks. There are many factors that cause variations in performance and they referred as noise factors. A probabilistic approach has been used to model the effects of noise factors and an experimental design technique has been adopted to select optimal tolerance of kinematic and dynamic parameters for minimal performance variations. The control and noise factor arrays are employed to identify statistically significant parameters and their interactions. The performance measures like signal to noise ratio and reliability have been utilized and results are validated by Monte Carlo simulations. The proposed design of experiment methodology requires minimal computations. The tolerance design methodology of manipulator is illustrated by 2-DOF revolute–revolute planar manipulator following cubic and quintic trajectory to perform a task. The statistical analysis of simulated performances is carried out using analysis of variance technique, which showed that statistically significant parameters are independent of trajectory. The individual parameter tolerance sensitivity has also been carried out.     

Experimental validation and comparative analysis of optimal time-jerk algorithms for trajectory planning

In this paper, two minimum time-jerk trajectory planning algorithms for robotic manipulators have been considered, evaluated and experimentally validated. These algorithms consider both the execution time and the integral of the squared jerk along the whole trajectory, so as to take into account the need for fast execution and the need for a smooth trajectory, by adjusting the values of two weights. A comparative analysis of these algorithms with two different trajectory planning techniques taken from the literature has been carried out, by means of experimental tests performed on a real robotic manipulator. The results prove the experimental effectiveness of the proposed techniques.     

Sliding mode-based adaptive tube model predictive control for robotic manipulators with model uncertainty and state constraints

In this paper, the optimal tracking control for robotic manipulatorswith state constraints and uncertain dynamics is investigated, and a sliding mode-based adaptive tube model predictive control method is proposed. First, utilizing the high-order fully actuated system approach, the nominal model of the robotic manipulator is constructed as the predictive model. Based on the nominal model, a nominal model predictive controller with the sliding mode is designed, which relaxes the terminal constraints, and realizes the accurate and stable tracking of the desired trajectory by the nominal system. Then, an auxiliary controller based on the node-adaptive neural networks is constructed to dynamically compensate nonlinear uncertain dynamics of the robotic manipulator. Furthermore, the estimation deviation between the nominal and actual states is limited to the tube invariant sets. At the same time, the recursive feasibility of nominal model predictive control is verified, and the ultimately uniformly boundedness of all variables is proved according to the Lyapunov theorem. Finally, experiments show that the robotic manipulator can achieve fast and efficient trajectory tracking under the action of the proposed method.