共查询到19条相似文献,搜索用时 406 毫秒
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针对建模不精确的机器人,提出了一种基于神经网络补偿的机器人轨迹跟踪稳定自适应控制方法,文中通过设计神经网络补偿器和自适应鲁棒控制项,有效地补偿了模型的不确定性部分和网络逼近误差.由于算法包含有补偿神经网络逼近误差的鲁棒控制项,实际应用中对神经网络规模的要求可以降低;而且神经网络连接权是在线调整的,不需要离线学习过程.理论表明算法能够保证跟踪误差及神经网络连接权估计最终一致有界,仿真结果也验证了算法的有效性. 相似文献
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机械手的在线鲁棒自适应神经网络跟踪控制 总被引:3,自引:3,他引:0
考虑了一类具有外界干扰和不确定性的机械手臂轨迹跟踪鲁棒控制问题. 控制器由自适应RBF(radial basis function)神经网络控制器和PD控制器组成. 采用基于神经元灵敏度和获胜神经元概念的GP–RBF算法, 在线确定神经网络的初始结构和参数. 当误差满足一定要求时, 根据Lyapunov稳定性理论的自适应律进一步调整网络权值, 以保证机械手位置误差和速度跟踪误差渐近收敛于零. 所设计的控制器可保证闭环系统的稳定性和鲁棒性. 仿真结果证明了本文方法的有效性. 相似文献
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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。 相似文献
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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... 相似文献
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在非完整移动机器人轨迹跟踪问题中,针对机器人运动学与动力学模型的参数和非参数不确定性,提出了一种混合神经网络鲁棒自适应轨迹跟踪控制器,该控制器由运动学控制器和动力学控制器两部分组成;其中,采用了参数自适应的径向基神经网络对运动学模型的未知部分进行了建模,并采用权值在线调整的单层神经网络和自适应鲁棒控制项构成了动力学控制器;基于Lyapunov方法的设计过程保证了系统的稳定性和收敛性,仿真结果证明了算法的有效性。 相似文献
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船舶航向非线性系统鲁棒跟踪控制 总被引:7,自引:2,他引:5
对船舶航向非线性系统, 提出了一种基于神经网络方法的鲁棒跟踪控制器. 系统由船舶运动非线性响应模型和舵机伺服系统串联构成, 其中运动响应模型考虑了建模误差和外界干扰力等非匹配不确定性. 对建模误差和期望舵角的一阶导数项应用在线二层神经网络予以辨识和补偿, 不确定性干扰项处理应用L2增益设计. 采用Lyapunov函数递推法, 得到包括神经网络权值算法在内的跟踪控制器. 跟踪误差和神经网络权值误差的一致终值有界性保证了系统的鲁棒稳定性, 合理的控制器参数选择保证了控制精度. 仿真结果验证了控制器的有效性. 相似文献
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针对不确定性机器人提出一种具有H^∞跟踪特性的神经网络(NN)控制器,使H^∞控制理论与NN有机地结合起来。通过适当选择控制变量加权因子可以使由于NN近似误差以及外部干扰引起的误差动态衰减到期望的程度下。文基基于Lyapunov方法给出了NN学习自适应律,H^∞跟踪特性的证明。最后通过在两自由度机器人控制中的应用表明该方案的可行性。 相似文献
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针对含运动学未知参数以及动力学模型不确定的非完整轮式移动机器人轨迹跟踪问题,基于Radical Basis Function(径向基函数)神经网络,提出了一种鲁棒自适应控制器.首先,考虑移动机器人运动学参数未知的情况,提出了一种含自适应参数的运动学控制器,用以补偿参数不确定性导致的系统误差;其次,利用神经网络控制技术,对于机器人在移动中动力学模型不确定问题,提出了一种具有鲁棒性的动力学控制器,使得移动机器人可以在不知道具体动力学模型的情况下跟踪到目标轨迹;最后利用Lyapunov稳定性理论证明了整个系统的稳定性.通过数值仿真验证了所设计的控制器的可行性. 相似文献
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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. 相似文献
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对一种球形滚动机器人的路径跟踪问题进行研究,设计一种基于自适应滑模控制策略的路径跟踪控制器。所设计的路径跟踪控制器采用鲁棒滑模自适应增益控制律,能够有效实现带有扰动和不确定性的实际球形滚动机器人的路径跟踪。推导球壳纯滚动和无自转非完整约束下球形滚动机器人的运动方程,并在此基础上设计自适应滑模路径跟踪控制器。对于给定的参考几何路径,所设计的路径跟踪控制器能够确保路径跟踪误差在有限时间内收敛至很小的零邻域内。基于Lyapunov稳定性理论证明了闭环控制系统的稳定性,数值仿真与样机实验结果进一步验证了所设计的路径跟踪控制器的有效性。 相似文献
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A robust neural controller for underwater robot manipulators 总被引:1,自引:0,他引:1
Minho Lee Hyeung-Sik Choi 《Neural Networks, IEEE Transactions on》2000,11(6):1465-1470
Presents a robust control scheme using a multilayer neural network with the error backpropagation learning algorithm. The multilayer neural network acts as a compensator of the conventional sliding mode controller to improve the control performance when initial assumptions of uncertainty bounds of system parameters are not valid. The proposed controller is applied to control a robot manipulator operating under the sea which has large uncertainties such as the buoyancy, the drag force, wave effects, currents, and the added mass/moment of inertia. Computer simulation results show that the proposed control scheme gives an effective path way to cope with those unexpected large uncertainties. 相似文献
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Robust Adaptive Dead Zone Technology for Fault-Tolerant Control of Robot Manipulators Using Neural Networks 总被引:4,自引:0,他引:4
In this paper, a multi-layered feed-forward neural network is trained on-line by robust adaptive dead zone scheme to identify simulated faults occurring in the robot system and reconfigure the control law to prevent the tracking performance from deteriorating in the presence of system uncertainty. Consider the fact that system uncertainty can not be known a priori, the proposed robust adaptive dead zone scheme can estimate the upper bound of system uncertainty on line to ensure convergence of the training algorithm, in turn the stability of the control system. A discrete-time robust weight-tuning algorithm using the adaptive dead zone scheme is presented with a complete convergence proof. The effectiveness of the proposed methodology has been shown by simulations for a two-link robot manipulator. 相似文献
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In this paper, a new adaptive neuro controller for trajectory tracking is developed for robot manipulators without velocity measurements, taking into account the actuator constraints. The controller is based on structural knowledge of the dynamics of the robot and measurements of joint positions only. The system uncertainty, which may include payload variation, unknown nonlinearities and torque disturbances is estimated by a Chebyshev neural network (CNN). The adaptive controller represents an amalgamation of a filtering technique to generate pseudo filtered tracking error signals (for the elimination of velocity measurements) and the theory of function approximation using CNN. The proposed controller ensures the local asymptotic stability and the convergence of the position error to zero. The proposed controller is robust not only to structured uncertainty such as payload variation but also to unstructured one such as disturbances. Moreover the computational complexity of the proposed controller is reduced as compared to the multilayered neural network controller. The validity of the control scheme is shown by simulation results of a two-link robot manipulator. Simulation results are also provided to compare the proposed controller with a controller where velocity is estimated by finite difference methods using position measurements only. 相似文献
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In this paper we propose a neural network adaptive controller to achieve end-effector tracking of redundant robot manipulators. The controller is designed in Cartesian space to overcome the problem of motion planning which is closely related to the inverse kinematics problem. The unknown model of the system is approximated by a decomposed structure neural network. Each neural network approximates a separate element of the dynamical model. These approximations are used to derive an adaptive stable control law. The parameter adaptation algorithm is derived from the stability study of the closed loop system using Lyapunov approach with intrinsic properties of robot manipulators. Two control strategies are considered. First, the aim of the controller is to achieve good tracking of the end-effector regardless the robot configurations. Second, the controller is improved using augmented space strategy to ensure minimum displacements of the joint positions of the robot. Simulation examples are also presented to verify the effectiveness of the proposed approach. 相似文献
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Huabin Chen Fenglin Lv Tao Lin Shanben Chen 《Journal of Intelligent and Robotic Systems》2009,56(5):565-578
The real-time detection of the state of the gap and weld penetration control are two fundamental issues in robotic arc welding.
However, traditional robotic arc welding lacks external information feedback and the function of real-time adjusting. The
objective of this research is to adopt new sensing techniques and artificial intelligence to ensure the stability of the welding
process through controlling penetration depth and weld pool geometry. A novel arc welding robot system including function
modules (visual modules, data acquisition modules) and corresponding software system was developed. Thus, the autonomy and
intelligence of the arc welding robot system is realized. Aimed at solving welding penetration depth, a neural network (NN)
model is developed to calculate the full penetration state, which is specified by the back-side bead width (Wb), from the
top-side vision sensing technique. And then, a versatile algorithm developed to provide robust real-time processing of images
for use with a vision-based computer control system is discussed. To this end, the peak current self adaptive regulating controller
with weld gap compensation was designed in the robotic arc welding control system. Using this closed-loop control experiments
have been conducted to verify the effectiveness of the proposed control system for the robotic arc welding process. The results
show that the standard error of the Wb is 0.124 regardless of the variations in the state of the gap. 相似文献