共查询到19条相似文献,搜索用时 78 毫秒
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针对具有不确定性的机器人系统,为提高系统的稳态跟踪精度,提出一种非奇异终端神经滑模轨迹跟踪控制方案.控制器采用改进的非奇异终端滑模面,并基于径向基函数神经网络自适应调整控制律的切换项,不但克服了在设计中需要知道系统不确定性的上界的限制,而且平滑了控制信号.可应用Lyapunov稳定性理论证明了系统的渐近稳定性和跟踪误差的渐近收敛性.仿真结果验证了控制方法不仅能够保证机器人系统轨迹跟踪控制的快速性和鲁棒性,而且有效地削弱了抖振,可见方案是可行且有效的. 相似文献
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本文提出一种非奇异终端滑模funnel控制(NTSMFC)方法, 实现带有饱和输入电机伺服系统的指定性能跟踪控制. 根据中值定理, 非光滑饱和函数转化为放射形式, 并且应用一个简单的神经网络进行逼近和补偿. 为保证跟踪误差被限制在指定的界限内, 同时为避免构建复杂的barrier李雅普诺夫函数或逆函数, 本文采用一个新的限制变量. 然后, 构建非奇异终端滑模funnel控制器保证电机伺服系统的指定跟踪性能. 该方法无需事先已知输入饱和函数的界限等先验知识, 且基于李雅普诺夫函数设计可以保证位置跟踪误差的收敛性, 最后给出仿真对比实例证明了该方法的有效性. 相似文献
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多输入不确定系统的平滑非奇异终端滑模控制 总被引:1,自引:0,他引:1
研究多输入通道的参数摄动和外部扰动对控制系统输入输出和内部状态稳定性的影响. 采用两次模型变换实现输入输出和内部状态解耦, 运用Lyapunov 稳定定理建立系统收敛区域与不确定项范围的数学关系. 提出一种平滑非奇异终端滑模控制方法, 引入虚拟控制项以增加系统的相对阶, 利用鲁棒微分器合理提取微分信号, 实现系统的无抖振滑模控制. 仿真研究表明了所提出方法的有效性. 相似文献
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余晓华 《计算机光盘软件与应用》2014,(6):37-38
作为一种代替司机实现交通自动化的重要设备,列车自动运行(ATO)系统吸引了国内外铁路工作者的广泛关注。一个高效的高速列车控制方法能够满足人民对高速列车"安全、准时、舒适和快速"的需求[1]。与传统的线性滑模控制(LSM)相比,非奇异的终端滑模控制(NTSM)能保从任何初始状态到达平衡点的时间是有限的。NTSM稳态精度高,使它特别适用于高速、高精度控制[2]。本文采用非奇异终端滑模控制和线性滑模控制实现高速列车控制,仿真结果给出了验证分析。 相似文献
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针对城市轨道车辆行驶中出现轮对打滑的控制问题,使用一种非奇异终端滑模控制方法,基于城轨车辆永磁同步电机数学方程,建立永磁同步电机矢量控制模型,参考非奇异终端滑模控制策略,模拟列车控制系统应对打滑的控制步骤;在Matlab环境下建立仿真模型,仿真结果表明,与传统PID控制相比,采用非奇异终端滑模控制,牵引电机可以在更短的时间内到达给定值,且对于扰动具有鲁棒性,轨道车辆在轮对发生打滑的情况下可以更快更稳定地恢复到平稳运行区. 相似文献
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针对存在外部干扰、转动惯量矩阵不确定以及执行器故障的航天器姿态跟踪控制问题,本文提出了基于自适应快速非奇异终端滑模的有限时间收敛故障容错控制方案.通过引入能够避免奇异点,且具有有限时间收敛特性的快速非奇异终端滑模面,设计了满足多约束条件有限时间收敛的姿态跟踪容错控制律,利用参数自适应方法使控制器不依赖转动惯量和外部干扰的上界信息.Lyapunov稳定性分析表明:在存在外部干扰、转动惯量矩阵不确定以及执行器故障等约束条件下,本文设计的控制律能够保证闭环系统的快速收敛性,而且对执行器故障具有良好的容错性能.数值仿真校验了该控制律在姿态跟踪控制中的优良性能. 相似文献
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利用神经网络和滑模控制,研究带有饱和输入的一类非线性系统。为了便于问题分析,引入饱和约束模型输出与控制输入的差值这个变量,分5种情况讨论,求得神经网络权值的在线调节律,得到保证闭环系统稳定的控制律。利用Lyapunov函数,证明了闭环系统的稳定性;仿真实验说明了算法的有效性。 相似文献
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Nonsingular terminal sliding mode control of nonlinear second‐order systems with input saturation 下载免费PDF全文
This paper considers the nonsingular terminal sliding mode (TSM) controller design for a nonlinear second‐order system subject to input saturation. A new nonsingular TSM manifold is constructed by integrating the conventional nonsingular TSM manifold with a saturation function. When the bound of the uncertainty is known, based on the designed TSM manifold, a saturated controller can be designed directly for the nonlinear system. When the bound of the uncertainty is unknown, a disturbance observer is first employed to estimate the uncertainty, followed by constructing a composite controller consisting of a bounded feedback controller and a forward compensator. Theoretical analysis shows that under the proposed two control methods, the states of the closed‐loop system will both converge to zero in finite time. Simulation results demonstrate the effectiveness of the proposed methods. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
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In this article, a novel robust finite-time tracking control scheme is proposed for a class of uncertain nonlinear systems subject to the model uncertainty, external disturbance, and input saturation. A barrier function based disturbance observer (BFDO) with finite-time convergence performance is developed to estimate the non-smooth nonlinear compound disturbance, which includes the uncertainty, disturbance of system and input saturation. In addition, an adaptive continuous nonsingular terminal sliding mode controller, based on the barrier function and the estimate of the BFDO is developed. The Lyapunov stability and finite-time convergence of the proposed control scheme are proved. The effectiveness and performance advantage of the proposed control scheme is demonstrated by numerical simulations and comparison with existing works. 相似文献
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Shi Li 《International journal of systems science》2018,49(1):113-123
In this paper, an adaptive prescribed performance output-feedback control scheme is proposed for a class of switched nonlinear systems with input saturation. The MT-filters are employed to estimate the unmeasured states and the unknown functions are approximated by the radial basis function neural networks in controller design procedure. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error satisfies the prescribed performance. Finally, simulation results are given to illustrate the effectiveness of the proposed approach. 相似文献
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This work considers an input and output constraint control problem for pure‐feedback systems with nonaffine functions possibly being in‐differentiable. A locally semibounded and continuous condition for nonaffine functions is presented to guarantee the controllability, and the nonaffine system is transformed to an equivalent pseudoaffine one based on the mild condition. Combined with backstepping technique, a novel prescribed performance controller with new performance functions is constructed to circumvent high frequency chattering in control input. An auxiliary system with bounded compensation term is utilized in this paper, successfully avoiding the overrun of control input. The methodology achieves the desired transient and steady‐state performance and presents excellent robustness against the system uncertainty. Finally, two numerical simulations are performed to demonstrate the effectiveness of the proposed approach. 相似文献
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This paper investigates the robust control for the Euler‐Lagrange (EL) system with input saturation by using the integral sliding mode control and adaptive control. An integral sliding mode surface that is suitable for solving the problem of the input constraint is given based on the saturation function. By using the integral sliding mode surface, two robust antisaturation controllers are designed for the EL system with external disturbances. The first controller can deal with the external disturbances with known bounds, whereas the second one can compensate the external disturbances with unknown bounds by using the adaptive control. Finally, the effectiveness of the proposed controllers is demonstrated by strict theoretical analysis and numerical simulations. 相似文献
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This paper develops a sliding-mode neural network controller for a class of unknown nonlinear discrete-time systems using a recurrent neural network (RNN). The control scheme is based on a linearized expression of the nonlinear system using a linear neural network (LNN). The control law is proposed according to the discrete L yapunov theory. With a modified real-time recurrent learning algorithm, the RNN as an estimator is used to estimate the unknown part in the control law in on-line fashion. The stability of the control system is guaranteed owing to the on-line learning ability of the RNN algorithm. The proposed control scheme is applied to numerical problems and simulation results that it is very effective. 相似文献