共查询到19条相似文献,搜索用时 109 毫秒
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针对一类具有不确定性、多重时延和状态未知的复杂非线性系统,把模糊T-S模型和RBF神经网络结合起来,提出了一种基于观测器的跟踪控制方案.首先,应用模糊T-S模型对非线性系统建模,设计观测器用来观测系统状态,并由线性矩阵不等式得到模糊模型的控制律;其次,构建了自适应RBF神经网络,应用自适应RBF神经网络作为补偿器来补偿建模误差和不确定非线性部分.证明了闭环系统满足期望的跟踪性能.示例仿真结果表明了该方案的有效性. 相似文献
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针对一类带有未知非线性函数的严格反馈非线性时滞系统,设计了一种自适应神经网络控制器.选择径向基函数神经网络逼近未知的非线性函数.所提出的控制方案能保证闭环系统的所有信号是全局一致最终有界的.证明了跟踪误差信号将收敛于一个小紧集内.仿真实例验证了所提出方法的有效性. 相似文献
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针对永磁同步电动机(PMSM)伺服系统中存在负载扰动和参数不确定问题,提出一种新型的基于预估器的自适应神经网络动态面控制算法.采用神经网络在线辨识系统的不确定项.在控制设计中引入神经网络预估器,并利用预估偏差学习神经网络的权值向量.通过引入动态面控制技术克服传统反步递推控制设计中存在的"复杂性爆炸"问题.所提控制算法可以在实现快速自适应的同时,避免了控制输入中可能存在的高频信号和控制输入漂移问题,进而提高PMSM伺服系统的位置跟踪控制性能.仿真结果验证了所提控制算法的有效性,并且与现有自适应神经网络控制方案相比,基于文章提出的自适应神经网络动态面控制方案的PMSM伺服系统具有更好的位置跟踪性能. 相似文献
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针对线性一阶和二阶异构多智能体系统,考虑到任意智能体可能发生的执行器故障以及受到外部干扰,研究了系统容错一致性控制设计问题.首先设计变增益扰动观测器,快速估计外部干扰;其次,利用一致性误差变量构造自适应积分滑模面,结合干扰观测器的估计值设计自适应滑模容错控制器.当异构多智能体系统存在执行器故障和外部扰动时,自适应滑模控制器可以保证智能体系统的位置和速度状态趋于一致.最后,利用Matlab仿真验证了所提方法的可行性与有效性. 相似文献
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针对一类不确定上界未知的非线性时滞系统,基于松散稳定性条件,讨论了系统的模糊自适应控制问题 .通过在Lyapunov泛函中引入参数,得到带调节因子的时滞相关稳定性条件.设计出基于观测器的自适应模糊控制器,观测增益矩阵和反馈增益矩阵可以通过求解线性矩阵不等式得到 .当调节因子取不同值时,观测增益矩阵和反馈增益矩阵也是不同的,因此,闭环系统的动态性能可以通过选取合适的调节因子来优化.最后通过一个实例验证了所给结论的有效性. 相似文献
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研究一类模糊时滞系统的指数稳定和基于观测器的模糊控制问题.在系统状态未知的情况下,通过设计系统的模糊观测器利用矩阵不等式分析的方法给出了系统指数稳定条件和基于观测器的动态输出反馈控制器设计方案.仿真结果说明了所提方法的有效性. 相似文献
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《Nonlinear Analysis: Hybrid Systems》2008,2(3):862-874
A neural network hybrid adaptive control framework for nonlinear uncertain hybrid dynamical systems is developed. The proposed hybrid adaptive control framework is Lyapunov-based and guarantees partial asymptotic stability of the closed-loop hybrid system; that is, asymptotic stability with respect to part of the closed-loop system states associated with the hybrid plant states. A numerical example is provided to demonstrate the efficacy of the proposed hybrid adaptive stabilization approach. 相似文献
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In this paper, a robust adaptive neural network synchronization controller is proposed for two chaotic systems with input time delay and uncertainty. The studied chaotic system may possess a wide class of nonlinear time-delayed input uncertainty. The radial basis function (RBF) neural network is used to approximate the unknown continuous bounded function item of the time delay uncertainty via appropriate weight value updated law. With the output of RBF neural network, a robust adaptive synchronization control scheme is presented for the time delay uncertain chaotic system. Finally, a simulation example is used to illustrate the effectiveness of the proposed synchronization control scheme. 相似文献
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针对一类 MIMO不确定非线性系统 ,基于一种修改的李亚普诺夫函数并利用 I型模糊系统的逼近能力 ,提出一种分散自适应模糊控制器设计的新方案。该方案不但能够避免现有的一些自适应模糊 /神经网络控制器设计中对控制增益一阶导数上界的要求 ,而且能够避免控制器的奇异问题。通过理论分析 ,证明闭环控制系统是全局稳定的 ,跟踪误差收敛到零。仿真结果表明了该方法的有效性。 相似文献
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In this paper, an adaptive fuzzy output tracking control approach is proposed for a class of single input and single output (SISO) uncertain pure-feedback switched nonlinear systems under arbitrary switchings. Fuzzy logic systems are used to identify the unknown nonlinear system. Under the framework of the backstepping control design and fuzzy adaptive control, a new adaptive fuzzy output tracking control method is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error remains an adjustable neighborhood of the origin. A numerical example is provided to illustrate the effectiveness of the proposed approach. 相似文献
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《Mathematical and Computer Modelling》2006,43(9-10):1136-1149
In this paper, two methods are reviewed and compared for designing reduced order controllers for distributed parameter systems. The first involves a reduction method known as LQG balanced truncation followed by MinMax control design and relies on the theory and properties of the distributed parameter system. The second is a neural network based adaptive output feedback synthesis approach, designed for the large scale discretized system and depends upon the relative degree of the regulated outputs. Both methods are applied to a problem concerning control of vibrations in a nonlinear structure with a bounded disturbance. 相似文献
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A novel self-organizing wavelet cerebellar model articulation controller (CMAC) is proposed. This self-organizing wavelet CMAC (SOWC) can be viewed as a generalization of a self-organizing neural network and of a conventional CMAC, and it has better generalizing, faster learning and faster recall than a self-organizing neural network and a conventional CMAC. The proposed SOWC has the advantages of structure learning and parameter learning simultaneously. The structure learning possesses the ability of on-line generation and elimination of layers to achieve optimal wavelet CMAC structure, and the parameter learning can adjust the interconnection weights of wavelet CMAC to achieve favorable approximation performance. Then a SOWC backstepping (SOWCB) control system is proposed for the nonlinear chaotic systems. This SOWCB control system is composed of a SOWC and a fuzzy compensator. The SOWC is used to mimic an ideal backstepping controller and the fuzzy compensator is designed to dispel the residual of approximation errors between the ideal backstepping controller and the SOWC. Moreover, the parameters of the SAWCB control system are on-line tuned by the derived adaptive laws in the Lyapunov sense, so that the stability of the feedback control system can be guaranteed. Finally, two application examples, a Duffing–Holmes chaotic system and a gyro chaotic system, are used to demonstrate the effectiveness of the proposed control method. The simulation results show that the proposed SAWCB control system can achieve favorable control performance and has better tracking performance than a fuzzy neural network control system and a conventional adaptive CMAC. 相似文献
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Adaptive fuzzy backstepping control for a class of MIMO switched nonlinear systems with unknown control directions
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In this article, an adaptive fuzzy output tracking control approach is proposed for a class of multiple‐input and multiple‐output uncertain switched nonlinear systems with unknown control directions and under arbitrary switchings. In the control design, fuzzy logic systems are used to identify the unknown switched nonlinear systems. A Nussbaum gain function is introduced into the control design and the unknown control direction problem is solved. Under the framework of the backstepping control design, fuzzy adaptive control and common Lyapunov function stability theory, a new adaptive fuzzy output tracking control method is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed‐loop system are bounded and the tracking error remains an adjustable neighborhood of the origin. A numerical example is provided to illustrate the effectiveness of the proposed approach. © 2015 Wiley Periodicals, Inc. Complexity 21: 155–166, 2016 相似文献
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A. Errachdi S. Slama M. Benrejeb 《Mathematical and Computer Modelling of Dynamical Systems: Methods, Tools and Applications in Engineering and Related Sciences》2020,26(2):144-168
ABSTRACTA new adaptive kernel principal component analysis (KPCA) for non-linear discrete system control is proposed. The proposed approach can be treated as a new proposition for data pre-processing techniques. Indeed, the input vector of neural network controller is pre-processed by the KPCA method. Then, the obtained reduced neural network controller is applied in the indirect adaptive control. The influence of the input data pre-processing on the accuracy of neural network controller results is discussed by using numerical examples of the cases of time-varying parameters of single-input single-output non-linear discrete system and multi-input multi-output system. It is concluded that, using the KPCA method, a significant reduction in the control error and the identification error is obtained. The lowest mean squared error and mean absolute error are shown that the KPCA neural network with the sigmoid kernel function is the best. 相似文献