基于双模控制的焊接机器人焊缝自动跟踪系统

该文提出了一种基于双模控制的焊接机器人焊缝自动跟踪系统.系统中应用新一代激光焊缝传感器测量焊缝的位置,并采用Fuzzy-P双模分段控制进行焊缝的纠偏.系统中采用DSP作为核心控制器产生控制信号,驱动焊枪横向步进电机和纵向步进电机动作,实现焊接机器人焊枪对焊缝的实时自动跟踪.实验证明,基于双模控制的焊缝自动跟踪系统可以实现焊接机器人焊枪对焊缝的实时自动跟踪.该系统完全满足实际焊接工程的需要.     

焊接机器人焊缝自动跟踪系统

本文提出了一种焊接机器人焊缝自动跟踪系统用以实现焊枪对焊缝的实时自动跟踪。系统中应用激光焊缝传感器测量焊缝的位置,并采用Fuzzy-P双模分段控制进行焊缝的纠偏。DSP作为系统的核心控制器产生控制信号,驱动焊枪横向步进电机和纵向步进电机动作,调整焊枪实时跟踪焊缝。实验证明,基于双模控制的焊缝自动跟踪系统可以实现焊接机器人焊枪对焊缝的实时自动跟踪。该系统完全满足实际焊接工程的需要。     

机器人的模拟复合正交神经网络学习控制

提出一种神经网络与PD并行控制的机器人学习控制系统。为了加快神经网络的学习算法，在数字复合正交神经网络的基础上给出一种模拟复合正交神经网络的学习算法，以两关节机器人为对象仿真结果表明，该控制方法使机器人跟踪期望轨迹，其系统响应、跟踪精度和鲁棒性优于常规的控制方法，位置跟踪获得了满意的控制效果。该模拟神经控制器为不确定系统的控制提供了一种新的途径。     

基于强化学习神经网络的车速跟踪控制

提出一种用于汽车排放试验中驾驶机器人对车速跟踪控制的新方法.该控制方法基于神经网络并结合强化学习的自适应能力,通过神经网络的在线学习对车速进行跟踪控制.利用试验汽车所获得的数据,首先开发出用于车速控制的神经网络模型.然后基于强化学习神经网络结构设计神经网络控制器以取得车速跟踪的自适应控制.在仿真研究中,使用神经网络车速控制模型替代实际汽车来训练初始控制器,并用开发与训练好的自学习神经网络控制器用于汽车车速跟踪控制.结果表明,所开发的神经网络控制器具有良好的车速跟踪性能,控制效果明显.     

基于神经网络的机器人自学习控制器

提出一种神经网络与PID控制相结合的机器人自学习控制器.为加快神经网络的 学习收敛性,研究了有效的优化学习算法.以两关节机器人为对象的仿真表明,该控制器使机 器人跟踪希望轨迹,其系统响应、跟踪精度和鲁棒性优于常规的控制策略.     

神经网络自适应滑模控制的不确定机器人轨迹跟踪控制

提出一种针对机器人跟踪控制的神经网络自适应滑模控制策略。该控制方案将神经网络的非线性映射能力与滑模变结构和自适应控制相结合。对于机器人中不确定项,通过RBF网络分别进行自适应补偿,并通过滑模变结构控制器和自适应控制器消除逼近误差。同时基于Lyapunov理论保证机器手轨迹跟踪误差渐进收敛于零。仿真结果表明了该方法的优越性和有效性。     

基于LS-SVM非线性内模控制在焊缝跟踪中的运用

针对单纯的模糊控制器在焊接机器人的焊缝跟踪中的控制精度欠佳、自适应性不强等问题,设计了一种新的用于焊缝跟踪的LS-SVM非线性内模控制器。通过样本数据建立系统固定的LS-SVM逆模型,与系统串联成精确的伪线性系统,对伪线性系统采用鲁棒性强的内模控制。仿真结果表明该方法具有很好的跟踪结果。     

基于RBF神经网络的多关节机器人固定时间滑模控制

针对具有典型非线性特性的多关节机器人轨迹跟踪控制问题,提出一种基于径向基函数(RBF)神经网络的固定时间滑模控制方法.首先,基于凯恩方法建立包括系统模型不确定性以及外部干扰在内的多关节机器人动力学模型;然后,根据机器人动力学模型设计一种固定时间收敛的滑模控制器,RBF神经网络用来逼近系统模型中的不确定性项,并利用Lyapunov理论证明该系统跟踪误差能在固定时间内收敛;最后,对特定型号的多关节机器人虚拟样机进行仿真分析,结果表明:与基于RBF神经网络的有限时间滑模控制器相比,所提出控制器具有良好的跟踪性能且能保证系统状态在固定时间内收敛.     

神经网络滑模控制下肢外骨骼机器人的轨迹跟踪

针对下肢外骨骼机器人行走稳定性与步态轨迹跟踪控制问题,对下肢外骨骼机器人三连杆模型进行动力学建模与轨迹仿真。通过拉格朗日法建立下肢外骨骼机器人的动力学模型,设计了神经网络自适应滑模控制算法。引入神经网络,对下肢外骨骼机器人步态轨迹跟踪系统的不确定项进行逼近,在控制器中采用了改进的趋近律,使用李雅普诺夫稳定性理论进行了稳定性分析,并通过MATLAB对改进后的控制算法进行了仿真验证。仿真结果表明,采用该算法对具有关节摩擦和外界环境干扰的下肢外骨骼机器人进行轨迹跟踪时,具有较好的跟踪效果;通过改进的趋近律,能削弱系统的抖振。相比于基于计算力矩法的滑模控制,该控制算法有更好的跟踪效果,能应用到下肢外骨骼机器人行走的稳定性和步态轨迹跟踪控制中。     

履带式智能弧焊机器人焊缝跟踪控制系统

针对新型履带式弧焊机器人焊缝跟踪问题进行了实验研究.通过对爬行机器人行走电机的控制完成机器人焊缝跟踪粗调，对十字滑块电机控制完成机器人焊缝跟踪细调，最终实现了机器人焊缝跟踪，实验表明跟踪效果良好．     

Design of Adaptive Robot Control System Using Recurrent Neural Network

The use of a new Recurrent Neural Network (RNN) for controlling a robot manipulator is presented in this paper. The RNN is a modification of Elman network. In order to solve load uncertainties, a fast-load adaptive identification is also employed in a control system. The weight parameters of the network are updated using the standard Back-Propagation (BP) learning algorithm. The proposed control system is consisted of a NN controller, fast-load adaptation and PID-Robust controller. A general feedforward neural network (FNN) and a Diagonal Recurrent Network (DRN) are utilised for comparison with the proposed RNN. A two-link planar robot manipulator is used to evaluate and compare performance of the proposed NN and the control scheme. The convergence and accuracy of the proposed control scheme is proved.     

Fuzzy-neuro position/force control for robotic manipulators with uncertainties

The performance of a controller for robot force tracking is affected by the uncertainties in both the robot dynamic model and the environmental stiffness. This paper aims to improve the controller’s robustness by applying the neural network to compensate for the uncertainties of the robot model at the input trajectory level rather than at the joint torque level. A self-adaptive fuzzy controller is introduced for robotic manipulator position/force control. Simulation results based on a two-degrees of freedom robot show that highly robust position/force tracking can be achieved, despite the existence of large uncertainties in the robot model.     

Fuzzy and Recurrent Neural Network Motion Control among Dynamic Obstacles for Robot Manipulators

An integration of fuzzy controller and modified Elman neural networks (NN) approximation-based computed-torque controller is proposed for motion control of autonomous manipulators in dynamic and partially known environments containing moving obstacles. The fuzzy controller is based on artificial potential fields using analytic harmonic functions, a navigation technique common used in robot control. The NN controller can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The NN weights are tuned on-line, with no off-line learning phase required. The stability of the closed-loop system is guaranteed by the Lyapunov theory. The purpose of the controller, which is designed as a neuro-fuzzy controller, is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems.     

Robust control of robot manipulators with fast load adaptation

Many adaptive robot controllers have been proposed in the literature to solve manipulator trajectory tracking problems for high-speed operations in the presence of parameter uncertainties. However, most of these controllers stem from the applications of the existing adaptive control theory, which is traditionally focused on tracking slowly time-varying parameters. In fact, manipulator dynamics have fast transient processes for high-speed operations and load changes are abrupt. These observations motivate the present research to incorporate change detection techniques into self-tuning schemes for tracking abrupt load variations and achieving fast load adaptation. To this end, a robustly global stabilizing controller for a robot model with parametric and non-parametric uncertainies is developed based on the Lyapunov second method, and it is then made adaptive via the self-tuning regulator concept. The two-model approach to online change detection in load is used and the estimation algorithm is reinitialized once load changes are detected. This allows a much faster adaptive identification of load parameters than the ordinary forgetting factor approach. Simulation results demonstrate that the proposed controller achieves better tracking accuracy than the existing adaptive and non-adaptive controllers.     

Robust nonlinear control of a planar 2-DOF parallel manipulator with redundant actuation

A new robust nonlinear controller is presented and applied to a planar 2-DOF parallel manipulator with redundant actuation. The robust nonlinear controller is designed by combining the nonlinear PD (NPD) control with the robust dynamics compensation. The NPD control is used to eliminate the trajectory disturbances, unmodeled dynamics and nonlinear friction, and the robust control is used to restrain the model uncertainties of the parallel manipulator. The proposed controller is proven to guarantee the uniform ultimate boundedness of the closed-loop system by the Lyapunov theory. The trajectory tracking experiment with the robust nonlinear controller is implemented on an actual planar 2-DOF parallel manipulator with redundant actuation. The experimental results are compared with the augmented PD (APD) controller, and the proposed controller shows much better trajectory tracking accuracy.     

Adaptive unified motion control of mobile manipulators

This paper presents a unified motion controller for mobile manipulators which not only solves the problems of point stabilization and trajectory tracking but also the path following problem. The control problem is solved based on the kinematic model of the robot. Then, a dynamic compensation is considered based on a dynamic model with inputs being the reference velocities to the mobile platform and the manipulator joints. An adaptive controller for on-line updating the robot dynamics is also proposed. Stability and robustness of the complete control system are proved through the Lyapunov method. The performance of the proposed controller is shown through real experiments.     

Trajectory Tracking Control of Omnidirectional Wheeled Mobile Manipulators: Robust Neural Network-Based Sliding Mode Approach

This paper addresses the robust trajectory tracking problem for a redundantly actuated omnidirectional mobile manipulator in the presence of uncertainties and disturbances. The development of control algorithms is based on sliding mode control (SMC) technique. First, a dynamic model is derived based on the practical omnidirectional mobile manipulator system. Then, a SMC scheme, based on the fixed large upper boundedness of the system dynamics (FLUBSMC), is designed to ensure trajectory tracking of the closed-loop system. However, the FLUBSMC scheme has inherent deficiency, which needs computing the upper boundedness of the system dynamics, and may cause high noise amplification and high control cost, particularly for the complex dynamics of the omnidirectional mobile manipulator system. Therefore, a robust neural network (NN)-based sliding mode controller (NNSMC), which uses an NN to identify the unstructured system dynamics directly, is further proposed to overcome the disadvantages of FLUBSMC and reduce the online computing burden of conventional NN adaptive controllers. Using learning ability of NN, NNSMC can coordinately control the omnidirectional mobile platform and the mounted manipulator with different dynamics effectively. The stability of the closed-loop system, the convergence of the NN weight-updating process, and the boundedness of the NN weight estimation errors are all strictly guaranteed. Then, in order to accelerate the NN learning efficiency, a partitioned NN structure is applied. Finally, simulation examples are given to demonstrate the proposed NNSMC approach can guarantee the whole system's convergence to the desired manifold with prescribed performance.     

Desired Compensation Adaptive Robust Control of Electrical-optical Gyro-stabilized Platform with Continuous Friction Compensation Using Modified LuGre Model

Continuous friction compensation along with other modeling uncertainties is concerned in this paper, to result in a continuous control input, which is more suitable for controller implementation. To accomplish this control task, a novel continuously differentiable nonlinear friction model is synthesized by modifying the traditional piecewise continuous LuGre model, then a desired compensation version of the adaptive robust controller is proposed for precise tracking control of electrical-optical gyro-stabilized platform systems. As a result, the adaptive compensation and the regressor in the proposed controller will depend on the desired trajectory and on-line parameter estimates only. Hence, the effect of measurement noise can be reduced and then high control performance can be expected. Furthermore, the proposed controller theoretically guarantees an asymptotic output tracking performance even in the presence of modeling uncertainties. Extensively comparative experimental results are obtained to verify the effectiveness of the proposed control strategy.