共被期刊论文引用了4次
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| 1 | Backstepping sliding mode control for uncertain strict-feedback nonlinear systems using neural-network-based adaptive gain scheduling显示文摘A neural-network-based adaptive gain scheduling backstepping sliding mode control(NNAGS-BSMC) approach for a class of uncertain strict-feedback nonlinear system is proposed.First, the control problem of uncertain strict-feedback nonlinear systems is formulated. Second, the detailed design of NNAGSBSMC is described. The sliding mode control(SMC) law is designed to track a referenced output via backstepping technique.To decrease chattering result from SMC, a radial basis function neural network(RBFNN) is employed to construct the NNAGSBSMC to facilitate adaptive gain scheduling, in which the gains are scheduled adaptively via neural network(NN), with sliding surface and its differential as NN inputs and the gains as NN outputs. Finally, the verification example is given to show the effectiveness and robustness of the proposed approach. Contrasting simulation results indicate that the NNAGS-BSMC decreases the chattering effectively and has better control performance against the BSMC. | YANG Yueneng YAN Ye | 2018 | Journal of Systems Engineering and Electronics2018，29，3： | 12 |
| 2 | 机械臂的自适应神经滑模控制方法研究显示文摘针对具有建模误差和不确定干扰的机械臂轨迹跟踪控制,提出了一种改进的自适应神经滑模变结构控制方法。该方法在传统神经滑模变结构控制的基础上,设计了一种对神经网络学习误差的自适应补偿控制,以补偿神经网络在初始阶段可能出现的误差,增强了系统的稳定性,实现了滑模变结构控制的切换控制增益对建模误差和不确定干扰的自动跟踪,削弱了抖振。利用李亚普诺夫定理证明了控制系统的稳定性,并利用仿真实验比较和分析了所提出方法与传统神经滑模变结构控制的运行结果。仿真结果表明该方法有效地削弱了滑模变结构控制的抖振,并具有更好的控制性能。 | 穆效江 | 2015 | 控制工程2015，22，4： | 8 |
| 3 | Decoupled nonsingular terminal sliding mode control for affine nonlinear systems显示文摘A decoupled nonsingular terminal sliding mode control(DNTSMC) approach is proposed to address the tracking control problem of affine nonlinear systems.A nonsingular terminal sliding mode control(NTSMC) method is presented,in which the nonsingular terminal sliding surface is defined as a special nonsingular terminal function and the convergence time of the system states can be specified.The affine nonlinear system is firstly decoupled into linear subsystems via feedback linearization.Then,a nonsingular terminal sliding surface is defined and the NTSMC method is applied to each subsystem separately to ensure the finite time convergence of the closed-loop system.The verification example is given to demonstrate the effectiveness and robustness of the proposed approach.The proposed approach exhibits a considerable advantage in terms of faster tracking error convergence and less chattering compared with the conventional sliding mode control(CSMC). | Yueneng Yang Ye Yan | 2016 | Journal of Systems Engineering and Electronics2016，27，1： | 1 |
| 4 | 单神经元算法在水电协调控制中的应用显示文摘新能源的发展导致电力消纳问题和矛盾,引起电网频率波动,对电网的安全极为不利,为此对水电的快速负荷响应进行研究,并以此作为互补电源来弥补新能源发展所带来的缺陷,具有一定的意义。鉴于机组中含有非线性环节以及导叶、水头对功率的非线性特性,常规算法已不能满足要求。本文在常规算法的基础上引入单神经元算法,并通过PSO算法对单神经元中的比例系数进行优化,完成了AGC和一次调频的协调控制,并加快了响应速度,缩短了稳态时间,为机组的安全运行提供了保障。 | 吕金花 苏适 刘友宽 孙建平 | 2017 | 云南电力技术2017，45，5： | 0 |
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