应用于力矩受限自由漂浮空间机器人的神经网络自适应控制

在控制力矩受限情况下,为实现具有模型不确定性自由漂浮空间机器人的轨迹跟踪控制,文章设计了一种新的神经网络自适应控制策略;首先,用双曲函数对控制力矩输入进行限制;其次,设计一种神经网络自适应控制律,对输入力矩受限条件下的非线性系统模型进行在线逼近,同时,利用鲁棒项对神经网络逼近误差和外界干扰进行消除;最后,根据李雅普诺夫理论,证明了所设计控制策略能够使自由漂浮空间机器人系统渐进稳定;仿真实验表明,该控制策略在无需建立复杂系统模型的情况下,便能够对控制力矩进行有效限制,从而使自由漂浮空间机器人在控制力矩受限情况下得到较好的控制.     

输入有界的自由漂浮柔性空间机器人轨迹跟踪控制

针对自由漂浮柔性空间机器人轨迹跟踪控制问题, 首先利用拉格朗日和假设模态法建立了动力学模型. 分析系统动力学模型, 综合考虑欠驱动、柔性振动等特点, 将其简化为一种带有柔性振动扰动完全可控的动力学模型; 在此基础上, 考虑控制输入受限, 提出一种自适应状态反馈控制策略. 该策略采用自适应技术实时在线学习柔性振动扰动参数, 从而保证控制律对柔性振动扰动具有良好的鲁棒性; 最后, 基于Lyapunov方法证明了该控制策略能够实现关节期望轨迹的跟踪. 仿真验证了该控制策略对控制输入受限系统轨迹跟踪控制的有效性和可靠性.     

输入力矩受限的机器人鲁棒自适应跟踪控制

在输入力矩受限的情况下, 提出一种全新的简单鲁棒自适应跟踪控制算法, 当参数的估计范围包含其真实值时, 证明了闭环系统的渐近稳定跟踪;当有干扰存在, 常规参数估计自适应控制算法不能实现稳定控制时, 本算法仍然使系统稳定, 在本算法中, 所估计的参数在跟踪控制律前馈项中表现为非线性, 这是区别于常规参数估计自适应算法的一个最重要特征. 因此本算法控制器的设计更有灵活性, 另一方面获得更好的控制品质和鲁棒性, 特别是对参数域估计误差即参数范围估计错误的强鲁棒性, 均为仿真算例所验证.     

力矩输入有界的柔性关节机器人轨迹跟踪控制

为解决柔性关节机器人在关节驱动力矩输出受限情况下的轨迹跟踪控制问题,提出一种基于奇异摄动理论的有界控制器.首先,利用奇异摄动理论将柔性关节机器人动力学模型解耦成快、慢两个子系统.然后,引入一类平滑饱和函数和径向基函数神经网络非线性逼近手段,依据反步策略设计了针对慢子系统的有界控制器.在快子系统的有界控制器设计中,通过关节弹性力矩跟踪误差的滤波处理加速系统的收敛.同时,在快、慢子系统控制器中均采用模糊逻辑实现控制参数的在线动态自调整.此外,结合李雅普诺夫稳定理论给出了严格的系统稳定性证明.最后,通过仿真对比实验验证了所提出控制方法的有效性和优越性.     

基于神经网络的自由漂浮空间机器人关节空间鲁棒跟踪控制

针对存在不确定性以及干扰的自由漂浮空间机器人关节空间轨迹跟踪问题,提出了一种基于鲁棒控制思想的神经网络鲁棒控制方法.对于控制器中由系统惯性参数不确定性引起的非线性不确定项,利用径向基函数(RBF)神经网络进行逼近,并且利用鲁棒控制器使系统镇定并保证从干扰到跟踪误差的增益小于或等于给定的指标.最后,对本文提出的控制方案进...     

空间柔性双臂机器人的神经网络协调控制

基于对机器人闭链系统运动特性的分析,采用假设模态法及拉格朗日方程建立了自由浮动空间柔性双臂机器人协调操作刚性负载闭链系统的动力学模型,然后采用基于小脑模型的模糊神经网络与非线性PD并行控制的方法对该动力学模型进行轨迹跟踪,并对内力采用积分控制;通过仿真实验比较,该方法比一般的非线性PD控制,在跟踪误差、抗干扰性、鲁棒性方面,都有很大的改善.     

机器人轨迹跟踪的自适应模糊神经网络控制

研究机器人跟踪轨迹控制问题,针对模型未知的机器人系统,为提高跟踪精度和控制性能,提出了一种基于T-S型模糊RBF神经网络的H∞轨迹跟踪控制方法,用模糊神经网络为模型未知的机器人系统建模,克服了系统鲁棒性差,对机动目标跟踪性能差等缺点。然后设计自适应控制器,将H∞控制理论与模糊神经网络有机地结合起来,借助鲁棒补偿项将建模误差及外部干扰衰减到期望的程度以下,而控制器与改进Elman神经网络的结合,便于处理建模有界干扰以及非结构化的未建模的动力学,并进行仿真。仿真结果表明了所提出的控制算法的可行性。     

柔性关节机操手的神经网络控制

本文在关节柔性较弱的情况下,对柔性关节机器人操作手的轨迹跟踪问题,提出了一种基于奇异摄动理论的机器人神经网络控制设计方法,在一般框架下证明了系统跟踪误差最终一致有界,并且可以通过选取增益矩阵使该误差界任意地小. 该方法克服了对模型参数线性化条件的要求,无需求解回归矩阵,因而具有很强的鲁棒性和模型推广能力. 数值试验表明,所提出的控制方法是可行且有效的.     

模糊CMAC的柔性空间机器人轨迹跟踪自学习控制

针对不确定自由漂浮柔性空间机器人系统,采用模糊CMAC神经网络自学习控制策略来解决轨迹跟踪控制问题.首先建立漂浮基空间机器人的动力学方程,然后利用具有快速学习能力的模糊CMAC神经网络来逼近非线性柔性臂的逆动力学模型.网络参数采用改进的有监督的Hebb学习规则进行自适应在线调整,并通过关联搜索进行自学习和自组织,其误差代价函数由PID控制器提供.仿真结果表明,这种模糊CMAC逆模PID控制器能够达到较高的控制精度,具有一定的工程应用价值.     

平面双连杆受限柔性机器人臂的自适应模糊力/位置控制

本文对一类平面双连杆受限柔性机器人的力/位置控制问题进行研究,提出了一种 新的自适应模糊控制方案．利用结构分解技术对模糊推理系统进行简化,用梯度法对参数进 行自适应调整,从而实现对受限柔性机器人系统末端的混合力/位置控制．计算机仿真结果 表明,本文提出的控制方案是合理、有效的．     

Adaptive RBF neural network control of robot with actuator nonlinearities

In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinearities compensator. Since the actuator nonlinearities are usually included in the robot driving motor, a compensator using radial basis function (RBF) network is proposed to estimate the actuator nonlinearities and eliminate their effects. Subsequently, an adaptive neural network controller that neither requires the evaluation of inverse dynamical model nor the time-consuming training process is given. In addition, GL matrix and its product operator are introduced to help prove the stability of the closed control system. Considering the adaptive neural network controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded (UUB). The whole scheme provides a general procedure to control the robot manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.     

不确定机器人的自适应神经网络控制与学习

针对具有未知动态的电驱动机器人,研究其自适应神经网络控制与学习问题.首先,设计了稳定的自适应神经网络控制器,径向基函数(RBF)神经网络被用来逼近电驱动机器人的未知闭环系统动态,并根据李雅普诺夫稳定性理论推导了神经网络权值更新律.在对回归轨迹实现跟踪控制的过程中,闭环系统内部信号的部分持续激励(PE)条件得到满足.随着PE条件的满足,设计的自适应神经网络控制器被证明在稳定的跟踪控制过程中实现了电驱动机器人未知闭环系统动态的准确逼近.接着,使用学过的知识设计了新颖的学习控制器,实现了闭环系统稳定、改进了控制性能.最后,通过数字仿真验证了所提控制方法的正确性和有效性.     

基于扩张状态观测器和反步滑模法的四旋翼无人机轨迹跟踪控制

为了解决欠驱动四旋翼无人机（UAV）在实际飞行中存在的外界干扰问题,同时提高在系统参数摄动情况下的精确轨迹跟踪效果,设计了一种基于扩张状态观测器（ESO）和积分型反步滑模算法的飞行控制策略。首先,根据系统的半耦合特性和严反馈结构特点,采用反步法设计姿态内环和位置外环控制器;然后,将抗干扰能力较强的滑模控制融入其中,使得系统的鲁棒性得到增强;接着,为了减小系统的稳态误差,引入积分环节;最后,利用ESO实时估算出系统的内、外总扰动并对控制量进行补偿。通过Lyapunov稳定判据,可以说明该系统是一个全局渐进稳定的系统,并通过仿真分析验证了所提控制方法的有效性和鲁棒性。     

模糊B样条基神经网络及其在机器人轨迹跟踪中的应用

提出一种模糊神经网络控制器并用于机器人轨迹跟踪控制．这种模糊神经网络利用B样条基函数作为隶属函数,可在线根据误差调整隶属函数的形状,使模糊神经网络具有更强的学习和适应能力．仿真与实验结果表明这种网络能很好的用于机器人的轨迹跟踪控制,具有很好的性能．     

Adaptive fuzzy neural network finite-time command filtered control of n-link robotic systems with actuator saturation

The finite time tracking control of n-link robotic system is studied for model uncertainties and actuator saturation. Firstly, a smooth function and adaptive fuzzy neural network online learning algorithm are designed to address the actuator saturation and dynamic model uncertainties. Secondly, a new finite-time command filtered technique is proposed to filter the virtual control signal. The improved error compensation signal can reduce the impact of filtering errors, and the tracking errors of system quickly converge to a smaller compact set within finite time. Finally, adaptive fuzzy neural network finite-time command filtered control achieves finite-time stability through Lyapunov stability criterion. Simulation results verify the effectiveness of the proposed control.     

A dynamically generated fuzzy neural network and its application to torsional vibration control of tandem cold rolling mill spindles

Based on the model of Higgins and Goodman, we describe a dynamically generated fuzzy neural network (DGFNN) approach to control, from input–output data, using on-line learning. The DGFNN is complete with the following powerful features drawn or modified from the existing literature: (1) a small FNN is created from scratch—there is no need to specify initial network architecture, initial membership functions, or initial weights, (2) fuzzy rules are constantly combined and pruned to minimize the size of the network while maintaining accuracy, irrelevant inputs are detected and deleted; and (3) membership functions and network weights are trained with a backpropagation-type algorithm. We apply the DGFNN controller to a real-world application of controlling the torsional vibration of tandem cold-rolling mill spindles with a simulated plant. The results of the DGFNN controller are compared with the performances of a conventional proportional-integral controller and a neural controller using recurrent cascade correlation with quickpropagation through time. We show that while both neural approaches increase the control precision and robustness, the DGFNN controller gives the best results for reducing the speed deviation and suppressing the torsional vibration of the spindles, as well as is more computationally efficient.     

基于D-FNN的开关磁阻无位置传感器的研究

提出了一种基于扩展径向基函数(RBF)神经网络的动态模糊神经网络(D-FNN)的开关磁阻电机无位置传感器控制的新方法。动态模糊神经网络系统以在线采样的相绕组的电流和磁链为输入,以转子位置角度为输出,从而建立起电流和磁链、转子位置角度的非线性映射关系;训练完成后,用D-FNN输出结果取代位置传感器角度信号,实现电机无位置传感器运行。仿真和实验结果表明:由D-FNN获得的角度信号和由位置传感器获得的角度信号相比误差小,电机能够准确换相,且输出转矩波动小,转速曲线平滑,电机在无位置传感器下运行良好。     

Control system for the Khepera robot by a neural network with competition and cooperation

This article describes a new approach to control systems for a mobile robot Khepera by using a neural network with competition and cooperation as the processing unit for the robot sensors. Competition makes only one neuron active, while cooperation keeps them all active. In our research, we find that the Khepera controlled by this neural network can maintain a smoother trajectory than when it is controlled by the output values of its own sensors, especially in noisy environments. This work was presented in part at the Fifth International Symposium on Artificial Life and Robotics, Oita, Japan, January 26–28, 2000     

Lookup table control method for vibration suppression of a flexible manipulator with optimization of the minimum settling time and energy consumption

Active vibration suppression of flexible manipulators is important in many engineering applications, such as robot manipulators and high‐speed flexible mechanisms. The demand for a short settling time and low energy consumption of vibration suppression requires consideration of optimal control. Under a wide range of operating conditions, however, the fixed optimal parameters determined for a control algorithm might not produce the best performance. Therefore, to enhance performance, this paper suggests a lookup table control method for a flexible manipulator. This method can tune itself to the optimal parameters on the basis of initial maximum responses to the controlled system. In this study, a multi‐objective genetic algorithm is used to search for optimal parameters with regard to positive position feedback to the control algorithm. In turn, with the optimal parameters, the multi‐objective functions of the settling time and energy consumption during the vibration control of a flexible manipulator can be minimized. The simulation and experimental results both indicate that the energy consumption can be reduced significantly if the settling time is slightly increased. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Adaptive neural network control for a nonlinear Euler‐Bernoulli beam in three‐dimensional space with unknown control direction

In this paper, an adaptive neural network control system is developed for a nonlinear three‐dimensional Euler‐Bernoulli beam with unknown control direction. The Euler‐Bernoulli beam is modeled as a combination of partial differential equations (PDEs) and ordinary differential equations (ODEs). Adaptive radial basis function–based neural network control laws are designed to determine approximation of disturbances. A projection mapping operator is adopted to realize bounded approximation of disturbances. A Nussbaum function is introduced to compensate for the unknown control direction. The goal of this study is to suppress the vibrations of the Euler‐Bernoulli beam in three‐dimensional space. In addition, unknown control direction problem and bounded disturbances are considered to guarantee that the signals of the system are uniformly bounded. Numerical simulations demonstrate the effectiveness of the proposed method.