共查询到18条相似文献,搜索用时 187 毫秒
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考虑系统存在的参数、外界扰动和未建模动态等不确定性,研究非完整移动机械手的鲁棒自适应控制器设计方法.基于用旋量理论建立的非完整移动机械手的动力学模型,设计了移动平台子系统的运动控制器,然后应用非线性反步控制技术和模糊逻辑系统的通用逼近性,用参数化线性模糊逻辑系统逼近非完整移动机械手动力学模型中的不确定项,基于Lyapu... 相似文献
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移动机械手运动/力鲁棒自适应轨迹跟踪 总被引:1,自引:0,他引:1
针对移动机械手控制器设计中用隐函数定理进行模型降阶时存在的一些问题,把完整和非完整约束的统一形式引入到系统的动力学模型降阶中.基于该降阶模型设计了不确定移动机械手稳定的运动/力鲁棒自适应线性参数模糊控制器.理论分析和仿真结果表明,设计的控制器简单有效. 相似文献
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针对移动机械手控制器设计中用隐函数定理进行模型降阶时存在的一些问题,把完整和非完整约束的统一形式引入到系统的动力学模型降阶中.基于该降阶模型设计了不确定移动机械手稳定的运动/力鲁棒自适应线性参数模糊控制器.理论分析和仿真结果表明,设计的控制器简单有效.
相似文献4.
基于观测器的机械手神经网络自适应控制 总被引:3,自引:0,他引:3
提出了一种基于观测器的机械手神经网络自适应轨迹跟随控制器设计方法,这里机
械手的动力学非线性假设是未知的,并且假设机械手仅有关节角位置测量.文中采用一个线
性观测器重构机械手的关节角速度,用神经网络逼近修正的机械手动力学非线性,改进系统
的跟随性能.基于观测器的神经网络自适应控制器能够保证机械手角跟随误差和观测误差的
一致终结有界性以及神经网络权值的有界性,最后给出了机械手神经网络自适应控制器-观
测器设计的主要理论结果,并通过数字仿真验证了所提方法的性能. 相似文献
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考虑高超声速飞行器飞行过程中气动参数变动导致的不确定,将模糊控制与二阶滑模控制相结合,形成自适应模糊二阶滑模控制器,用于控制高超声速飞行器姿态的飞行系统中.依据奇异摄动理论,设计快速和慢速双闭环系统控制角速率和姿态角.设计二阶滑模控制器用于有效地衰减抖振,同时对姿态角指令实现准确和快速跟踪.采用自适应模糊逻辑逼近高超声速飞行器动力学和运动学模型中的不确定部分,以对控制器进行有效补偿,基于Lyapunov稳定性理论,推导模糊规则参数的自适应律,确保整个闭环控制系统的稳定.仿真结果表明,所提出的高超声速飞行器的自适应模糊滑模控制系统能够有效抑制气动参数摄动的影响,对姿态角指令有较好的跟踪性能. 相似文献
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H∞环路成形方法设计的控制器阶次较高,不便于工程实现和参数调整;用传统方法确定模糊控制器隶属度函数的参数和模糊规则比较费时且难以保证鲁棒性能和时频域性能指标.针对上述情况,提出了一种综合运用H∞环路成形和自适应神经模糊推理系统来设计模糊控制器的方法.首先采用H∞环路成形设计方法,得到鲁棒裕量、动态和稳态性能都符合要求的控制器,然后用自适应神经模糊推理系统来逼近此控制器,最后根据自适应神经模糊推理系统参数确定相应的模糊控制器规则和参数.该方法确定模糊控制器隶属度函数的参数精确而省时,且能保证控制器具有较强的鲁棒性和良好的控制效果.通过对小车倒立摆系统进行的仿真,验证了该控制器设计方法的有效性. 相似文献
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This paper presents a PD manipulator controller with fuzzy adaptive gravity compensation. The main idea is to use a fuzzy adaptive controller to compensate for the gravity term of the robotic manipulator. This controller is designed by using Lyapunov's stability theorem, which guarantees system stability. Simulation is implemented on a two‐link manipulator by using MATALAB and SIMULINK. The results show that this fuzzy adaptive controller makes the manipulator trajectory converge to a desired position. Compared with other proposed fuzzy adaptive manipulator controllers, the PD manipulator controller with fuzzy adaptive gravity compensation is conceptually and structurally simpler and guarantees zero position error. ©2000 John Wiley & Sons, Inc. 相似文献
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Multi-joint manipulator systems are subject to nonlinear influences such as frictional characteristics, random disturbances and load variations. To account for uncertain disturbances in the operation of manipulators, we propose an adaptive manipulator control method based on a multi-joint fuzzy system, in which the upper bound information of the fuzzy system is constant and the state variables of the manipulator control system are measurable. The control algorithm of the system is a MIMO (multi-input-multi-output) fuzzy system that can approximate system error by using a robust adaptive control law to eliminate the shadow caused by approximation error. It can ensure the stability of complex manipulator control systems and reduce the number of fuzzy rules required. Comparison of experimental and simulation data shows that the controller designed using this algorithm has highly-precise trajectory-tracking control and can control robotic systems with complex characteristics of non-linearity, coupling and uncertainty. Therefore, the proposed algorithm has good practical application prospects and promotes the development of complex control systems. 相似文献
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The work presented in this paper deals with the problem of autonomous and intelligent navigation of mobile manipulator, where the unavailability of a complete mathematical model of robot systems and uncertainties of sensor data make the used of approximate reasoning to the design of autonomous motion control very attractive.A modular fuzzy navigation method in changing and dynamic unstructured environments has been developed. For a manipulator arm, we apply the robust adaptive fuzzy reactive motion planning developed in [J.B. Mbede, X. Huang, M. Wang, Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators, IEEE Transactions on Fuzzy Systems 11 (2) (2003) 249-261]. But for the vehicle platform, we combine the advantages of probabilistic roadmap as global planner and fuzzy reactive based on idea of elastic band. This fuzzy local planner based on a computational efficient processing scheme maintains a permanent flexible path between two nodes in network generated by a probabilistic roadmap approach. In order to consider the compatibility of stabilization, mobilization and manipulation, we add the input of system stability in vehicle fuzzy navigation so that the mobile manipulator can avoid stably unknown and/or dynamic obstacles. The purpose of an integration of robust controller and modified Elman neural network (MENN) is to deal with uncertainties, which can be translated in the output membership functions of fuzzy systems. 相似文献
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Abstract: A fuzzy sliding-mode control with rule adaptation design approach with decoupling method is proposed. It provides a simple way to achieve asymptotic stability by a decoupling method for a class of uncertain nonlinear systems. The adaptive fuzzy sliding-mode control system is composed of a fuzzy controller and a compensation controller. The fuzzy controller is the main rule regulation controller, which is used to approximate an ideal computational controller. The compensation controller is designed to compensate for the difference between the ideal computational controller and the adaptive fuzzy controller. Fuzzy regulation is used as an approximator to identify the uncertainty. The simulation results for two cart–pole systems and a ball–beam system are presented to demonstrate the effectiveness and robustness of the method. In addition, the experimental results for a tunnelling robot manipulator are given to demonstrate the effectiveness of the system. 相似文献
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自适应神经模糊推理结合PID控制的并联机器人控制方法 总被引:1,自引:0,他引:1
针对6自由度液压驱动并联机器人的精确控制问题,提出一种结合自适应神经模糊推理系统(ANFIS)和比例积分微分(PID)控制的机器人控制方法。首先,利用浮动坐标系描述法(FFRF)来模拟机器人柔性组件,并构建并联机器人的拉格朗日动力学模型。然后,根据模糊推理中的模糊规则来自适应调整PID控制器参数。最后,利用神经自适应学习算法使模糊逻辑能计算隶属度函数参数,从而使模糊推理系统能追踪给定的输入和输出数据。将该控制器与传统PID控制器、模糊PID控制器进行比较,结果表明,ANFIS自整定PID控制器大大减小了末端器位移误差,能很好的控制并联机器人末端机械手的运动。 相似文献
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This article presents a direct adaptive fuzzy control scheme for a class of uncertain continuous-time multi-input multi-output nonlinear (MIMO) dynamic systems. Within this scheme, fuzzy systems are employed to approximate an unknown ideal controller that can achieve control objectives. The adjustable parameters of the used fuzzy systems are updated using a gradient descent algorithm that is designed to minimize the error between the unknown ideal controller and the fuzzy controller. The stability analysis of the closed-loop system is performed using a Lyapunov approach. In particular, it is shown that the tracking errors are bounded and converge to a neighborhood of the origin. Simulations performed on a two-link robot manipulator illustrate the approach and exhibit its performance. 相似文献
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《Simulation Modelling Practice and Theory》2007,15(7):801-816
In this paper, a stable adaptive fuzzy-based tracking control is developed for robot systems with parameter uncertainties and external disturbance. First, a fuzzy logic system is introduced to approximate the unknown robotic dynamics by using adaptive algorithm. Next, the effect of system uncertainties and external disturbance is removed by employing an integral sliding mode control algorithm. Consequently, a hybrid fuzzy adaptive robust controller is developed such that the resulting closed-loop robot system is stable and the trajectory tracking performance is guaranteed. The proposed controller is appropriate for the robust tracking of robotic systems with system uncertainties. The validity of the control scheme is shown by computer simulation of a two-link robotic manipulator. 相似文献