基于神经网络的带补偿作用的机器人逆动力学机制

本文提出了一种新的基于神经网络的机器人逆动力学控制方案，不需要机器人的模型知识，利用神经网络实现外部扰动的补偿和闭环控制。该方法具有良好的跟踪精度和抗干扰性能。     

基于神经网络的带补偿作用的机器人逆动力学控制

本文提出了一种新的基于神经网络的机器人逆动力学控制方案，不需要机器人的模型知识，利用神经网络实现外部扰动的补偿和闭环控制．该方法具有良好的跟踪精度和抗干扰性能．     

基于神经网络的并联3自由度机器人位置正解

并联机器人位置正解是机器人运动学中难点问题之一，常规求解方法比较复杂且难度较大，通常需要对大量的非线性方程组进行推导计算且得到的解不唯一。该文提出了一种将人工神经网络用于并联机器人位置正解求解的通用方法，并结合实际机构对并联3自由度机器人进行了具体求解。通过对神经网络拓扑结构的设计以及选取有效的学习算法并用大量的位置反解数据对神经网络进行训练，获得了用于求解位置正解的神经网络模型，该网络可以实现位置正解问题的求解计算，从而避免了复杂的推导和演算。计算机仿真与实验结果表明了该方法的有效性与可行性。     

基于神经网络的机器人的逆运动学分析

文章提出了基于BP神经网络算法的机器人逆运动学的求解方法,给出了基于神经网络的机器人逆运动学求解的具体步骤和设计神经网络的相关注意事项.通过时KLD-600六自由度机器人仿真表明该算法简单可靠.     

基于神经网络的机器人智能控调

本文综述了智能控制与机器人控制的特点，并在智能控制的框架下，重点论述了神经网络控制在机器人控制中的应用及其于神经网络的机器人各种控制方法，同时指出今后的研究方向，为神经网络控制乃至智能控制在机器人控制中的应用提供了参考。     

基于神经网络的机器人运动模型辨识及其实验研究

针对机器人楚模中不确定因素的影响，采用神经网络辨识机器人输入输出间的非线性关系，建立机器人的运动学模型，为了提高神经网络的辨识速度，基于Elman动态递归网络，通过增加网络输入输出的部分信息，提出一种新的动态神经网络结构——状态廷迟输入动态递归神经网络(SDIDRNN)，提高了网络的学习速度和稳态精度。以PowerCube^TM模块化机器人为研究对象，把根据机器人返回的关节位置信息和利用OPTOTRAK3020三维运动测量系统测得的机器人末端位置信患作为SDIDRNN的学习样本，对包含各种影响因素的机器人运动模型进行辨识，得到了满意的结果，说明了该神经网络的优越性。     

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 BP神经网络的 X光图像畸变校正技术的研究 *

为解决基于图像导航的机器人辅助外科手术过程中 X光图像的畸变特性问题 ,提出了一种新型的基于 BP神经网络的 X光畸变图像校正方法。该方法首先从一个标准模板的 X光畸变图像中提取标定样本位置信息作为神经网络输入 ,以模板的标定样本真实位置信息为神经网络输出 ,构建 BP神经网络。该 BP神经网络能够实现畸变图像与真实模板之间的映射关系 ,从而达到图像畸变校正的目的。最后通过机器人辅助髓内钉锁孔实验对该方法进行了实验验证 ,证明了该方法的有效性。     

采掘机器人的模糊监督——神经网络控制器技术

介绍一种基于规则的自学习神经网络控制器在采掘机器人上的应用。它根据实时执行的结果，采用多步学习－模糊监督学习方法，修正神经网络的教师信号，使控制算法简化，提高了计算的实时性，加快了学习速度实验验证了采用该方法取得的一些结果。     

基于神经网络模糊PID的足球机器人控制算法研究

通过对足球机器人运动学模型进行分析,以足球机器人系统为实验平台,论证了神经网络模糊PID控制技术应用于足球机器人运动控制的可行性。将传统的PID控制与神经网络模糊控制相结合,通过PID算法实现控制的准确性,利用神经网络模糊控制提高控制的快速性与自适应性。针对足球机器人运动控制中的实际问题,着重提出了基于神经网络和模糊控制相结合动态调整PID控制器的三个参数KP,KI,KD的设计方法。实验证明该方法增强了控制器的调节能力和简化了控制器设计,同时本方法对模型和环境具有较好的适应能力和较强的鲁棒性。     

Observer-based adaptive control of robot manipulators: Fuzzy systems approach

This paper presents a fuzzy adaptive control suitable for motion control of multi-link robot manipulators with structured and unstructured uncertainties. When joint velocities are available, full state fuzzy adaptive feedback control is designed to ensure the stability of the closed loop dynamic. If the joint velocities are not measurable, an observer is introduced and an adaptive output feedback control is designed based on the estimated velocities. In the proposed control scheme, we need not derive the linear formulation of robot dynamic equation and tune the parameters. To reduce the number of fuzzy rules of the fuzzy controller, we consider the properties of robot dynamics and the decomposition of the uncertainties terms. The proposed controller is robust against uncertainties and external disturbance. Further, it is shown that required stability conditions, in both cases, can be formulated as LMI problems and solved using dedicated software. The validity of the control scheme is demonstrated by computer simulations on a two-link robot manipulator.     

Adaptive terminal sliding mode control for rigid robotic manipulators

In order to apply the terminal sliding mode control to robot manipulators, prior knowledge of the exact upper bound of parameter uncertainties, and external disturbances is necessary. However, this bound will not be easily determined because of the complexity and unpredictability of the structure of uncertainties in the dynamics of the robot. To resolve this problem in robot control, we propose a new robust adaptive terminal sliding mode control for tracking problems in robotic manipulators. By applying this adaptive controller, prior knowledge is not required because the controller is able to estimate the upper bound of uncertainties and disturbances. Also, the proposed controller can eliminate the chattering effect without losing the robustness property. The stability of the control algorithm can be easily verified by using Lyapunov theory. The proposed controller is tested in simulation on a two-degree-of-freedom robot to prove its effectiveness.     

Quasi sliding mode‐based single input fuzzy self‐tuning decoupled fuzzy PI control for robot manipulators with uncertainty

This paper presents a robust adaptive control strategy for robot manipulators, based on the coupling of the fuzzy logic control with the so‐called sliding mode control (SMC) approach. The motivation for using SMC in robotics mainly relies on its appreciable features. However, the drawbacks of the conventional SMC, such as chattering effect and required a priori knowledge of the bounds of uncertainties can be destructive. In this paper, these problems are suitably circumvented by adopting a reduced rule base single input fuzzy self tuning decoupled fuzzy proportional integral sliding mode control approach. In this new approach a decoupled fuzzy proportional integral control is used and a reduced rule base single input fuzzy self‐tuning controller as a supervisory fuzzy system is added to adaptively tune the output control gain of the decoupled fuzzy proportional integral control. Moreover, it is proved that the fuzzy control surface of the single‐input fuzzy rule base is very close to the input/output relation of a straight line. Therefore, a varying output gain decoupled fuzzy proportional integral sliding mode control approach using an approximate line equation is then proposed. The stability of the system is guaranteed in the sense of the Lyapunov theorem. Simulations using the dynamic model of a 3DOF planar manipulator with uncertainties show the effectiveness of the approach in high speed trajectory tracking problems. The simulation results that are compared with the results of conventional SMC indicate that the control performance of the robot system is satisfactory and the proposed approach can achieve favorable tracking performance, and it is robust with regard to uncertainties and disturbances. Copyright © 2011 John Wiley & Sons, Ltd.     

Adaptive non-singular fast terminal sliding mode based on prescribed performance for robot manipulators

A fast convergent non-singular terminal sliding mode adaptive control law based on prescribed performance is formulated to solve the uncertainties and external disturbances of robot manipulators. First, the tracking error of robot manipulators is transformed by using the prescribed performance function, which improves the transient behaviors and steady-state accuracy of robot manipulators. Then, a novel fast convergent non-singular terminal sliding mode surface is brought up according to the transformed error, and the control law is derived to meet the stability requirements of robot manipulators. In practice, the upper boundary of the lumped disturbances cannot be accurately obtained. Therefore, an adaptive prescribed performance control (PPC) controller to lumped disturbances is brought up to ensure the stability and finite-time convergence of robot manipulators. Finally, the system stability of robot manipulators is proved by the Lyapunov theorem. Simulation results and comparative analysis demonstrate the superiority and robustness of the raised strategy.     

A precise robust fuzzy control of robots using voltage control strategy

Fuzzy control of robot manipulators with a decentralized structure is facing a serious challenge. The state-space model of a robotic system including the robot manipulator and motors is in non-companion form, multivariable, highly nonlinear, and heavily coupled with a variable input gain matrix. Considering the problem, causes and solutions, we use voltage control strategy and convergence analysis to design a novel precise robust fuzzy control (PRFC) approach for electrically driven robot manipulators. The proposed fuzzy controller is Mamdani type and has a decentralized structure with guaranteed stability. In order to obtain a precise response, we regulate a fuzzy rule which governs the origin of the tracking space. The proposed design is verified by stability analysis. Simulations illustrate the superiority of the PRFC over a proprotional derivative like (PD-like) fuzzy controller applied on a selective compliant assembly robot arm (SCARA) driven by permanent magnet DC motors.     

Neural and Neurofuzzy FELA Adaptive Robot Control Using Feedforward and Counterpropagation Networks

In this paper, the application of neural networks and neurofuzzy systems to the control of robotic manipulators is examined. Two main control structures are presented in a comparative manner. The first is a Counter Propagation Network-based Fuzzy Controller (CPN-FC) which is able to self-organize and correct on-line its rule base. The self-tuning capability of the fuzzy logic controller is attained by taking advantage of the structural equivalence between the fuzzy logic controller and a counterpropagation network. The second control structure is a more familiar neural adaptive controller based on a feedforward (MLP) network. The neural controller learns the inverse dynamics of the robot joints, and gradually eliminates the model uncertainties and disturbances. Both schemes cooperate with the computed torque control algorithm, and in that way the reduction of their complexity is achieved. The ability of adaptive fuzzy systems to compete with neural networks in difficult control problems is demonstrated. A sufficient set of numerical results is included.     

不确定性机器人的自适应跟踪控制

提出了一种自适应控制策略 ,用于不确定性机器人的轨迹跟踪 ,该控制器结构简单 ,且无需计算回归矩阵 ,通过对二自由度的机器人的仿真 ,证明该方法能使跟踪误差快速趋近于零     

机器人自适应PID饱和输出反馈控制

针对机器人系统在仅有位置传感、驱动器饱和、存在建模不确定性及干扰等条件下的轨迹跟踪控制问题,提出了一种新的自适应PID控制方案。采用高精度滤波器估计机器人关节速度,采用带饱和函数的控制器限制输出力矩,采用自适应PID控制器补偿建模不确定性和干扰。通过Lyapunov直接法,证明系统的稳定性。最后以两关节机器人为例,给出仿真实验结果,验证了算法的有效性。     

Adaptive Control of a Two-Link Robot Using Batch Least-Square Identifier

We design a regulation-triggered adaptive controller for robot manipulators to efficiently estimate unknown parameters and to achieve asymptotic stability in the presence of coupled uncertainties.Robot manipulators are widely used in telemanipulation systems where they are subject to model and environmental uncertainties.Using conventional control algorithms on such systems can cause not only poor control performance,but also expensive computational costs and catastrophic instabilities.Therefore,system uncertainties need to be estimated through designing a computationally efficient adaptive control law.We focus on robot manipulators as an example of a highly nonlinear system.As a case study,a 2-DOF manipulator subject to four parametric uncertainties is investigated.First,the dynamic equations of the manipulator are derived,and the corresponding regressor matrix is constructed for the unknown parameters.For a general nonlinear system,a theorem is presented to guarantee the asymptotic stability of the system and the convergence of parameters'estimations.Finally,simulation results are discussed for a two-link manipulator,and the performance of the proposed scheme is thoroughly evaluated.     

基于非线性H_∞状态反馈的机器人鲁棒控制

针对机器人存在参数不确定性以及外界未知干扰的情况 ,运用反推 (backstepping)设计方法 ,通过求解非线性 H∞ 状态反馈控制问题 ,设计了一种鲁棒控制器 ,实现了对外界未知干扰的有效衰减 ,以及只考虑参数不确定性时系统跟踪误差的渐近收敛性 .最后给出了对两连杆刚性机器人的仿真 ,验证了控制效果 .