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1.
网络控制系统中存在着时延、丢包、网络干扰等问题。针对网络控制系统中存在恶化系统的控制性能,甚至导致系统不稳定的因素,提出了一种基于自适应模糊神经网络控制器的网络控制系统,它能根据系统的实际输出与期望输出误差,利用自适应模糊控制和神经网络自学习的原理进行控制参数的自行调整,以符合控制系统的实际要求,同时,分析了网络延时,丢包率及网络干扰因素对系统性能的影响。利用TrueTime工具箱建立了包含自适应模糊神经网络控制器的网络控制系统的仿真模型,并将其分别与基于常规PID控制器的网络控制系统和基于模糊参数PID控制器的网络控制系统进行了比较。实验结果表明,在相同的网络环境下,基于自适应模糊神经网络控制器的网络控制系统的控制效果比基于常规的PID控制器和基于模糊参数PID控制器的要好,且具有较好的抗干扰能力和鲁棒性能。 相似文献
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Indirect adaptive control of nonlinear dynamic systems using self recurrent wavelet neural networks via adaptive learning rates 总被引:1,自引:0,他引:1
This paper proposes an indirect adaptive control method using self recurrent wavelet neural networks (SRWNNs) for dynamic systems. The architecture of the SRWNN is a modified model of the wavelet neural network (WNN). However, unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN can store the past information of wavelets. In the proposed control architecture, two SRWNNs are used as both an identifier and a controller. The SRWNN identifier approximates dynamic systems and provides the SRWNN controller with information about the system sensitivity. The gradient-descent method using adaptive learning rates (ALRs) is applied to train all weights of the SRWNN. The ALRs are derived from discrete Lyapunov stability theorem, which are applied to guarantee the convergence of the proposed control system. Finally, we perform some simulations to verify the effectiveness of the proposed control scheme. 相似文献
3.
An adaptive wavelet differential neural networks based identifier and its stability analysis 总被引:1,自引:0,他引:1
F. JahangiriAuthor Vitae A. Doustmohammadi Author VitaeM.B. Menhaj Author Vitae 《Neurocomputing》2012,77(1):12-19
In this paper, identification problem of a general class of nonlinear dynamic systems is fully considered using adaptive wavelet differential neural networks. In these networks, the activation functions are described by wavelets where parameters are tuned adaptively. The stability analysis of such identifiers is performed by means of Lyapunov analysis. Asymptotic convergence of the error and boundedness of the parameters are proven. To validate the approach, the neuro-identifier is applied to both the Van der pole oscillator and the twin-tanks plant. The simulation results show that the proposed neuro-identifier outperforms the sigmoid based differential neural network identifier. 相似文献
4.
This paper proposes a new type of nonlinear controllers and a large phase angle allowance design method based on the multi-objective optimal control system. With the proposed method, the performance of the system becomes better than that of the original system. Then, an example of the radar servo system is designed with a large phase angle allowance multi-objective optimal design method. Finally, the performance based on computer simulation demonstrates that the multi-objective optimal system is superior to linear optimal systems. 相似文献
5.
Chun-Fei Hsu 《Applied Soft Computing》2013,13(11):4392-4402
The structure of a neural network is determined by time-consuming trial-and-error tuning procedure in advance for the reason that it is difficult to consider the balance between the neuron number and the desired performance. To attack this problem, a self-evolving functional-linked wavelet neural network (SFWNN) is proposed. Without the need for preliminary knowledge, a self-evolving approach demonstrates that the properties of generating and pruning the hidden neurons automatically. Then, an adaptive self-evolving functional-linked wavelet neural control (ASFWNC) system which is composed of a neural controller and a supervisory compensator is proposed. The neural controller uses a SFWNN to online estimate an ideal controller and the supervisory compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. To investigate the capabilities of the proposed ASFWNC approach, it is applied to a chaotic system and a DC motor. The simulation and experimental results show that favorable control performance can be achieved by the proposed ASFWNC scheme. 相似文献
6.
In this paper, an optimal adaptive H-infinity tracking control design method via wavelet network for a class of uncertain nonlinear systems with external disturbances is proposed to achieve H-infinity tracking performance. First, an alternate tracking error and a performance index with respect to the tracking error and the control effort are introduced in order to obtain better performance, especially, in reducing the cost of the control effort in the case of small attenuation levels. Next, H-infinity tracking performance, which attenuates the influence of both wavelet network approximation error and external disturbances on the modified tracking error, is formulated. Our results indicate that a small attenuation level does not lead to a large control signal. The proposed method insures an optimal trade-off between the amplitude of control signals and the performance of tracking errors. An example is given to illustrate the design efficiency. 相似文献
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自适应控制是一种提高系统鲁棒性的有效方法。模糊神经网络具有了模糊逻辑和神经网络两者的优点,结合模糊神经网络(Fuzzy Neural Network—FNN)自适应控制策略和通用模型控制(Common Model Control—CMC)方法,以此来实现被控对象的逆控制,提出了基于模糊神经网络的通用模型自适应控制(FNNC—CMAC)。此控制方法参考轨迹是一条典型二阶曲线,仿真结果验证了鲁棒性,与基于模糊神经网络的通用模型控制及基于模糊逻辑的通用模型自适应控制相比,其控制性能更好。 相似文献
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An adaptive learning algorithm for a wavelet neural network 总被引:2,自引:0,他引:2
Yevgeniy Bodyanskiy Nataliya Lamonova Iryna Pliss Olena Vynokurova 《Expert Systems》2005,22(5):235-240
Abstract: An optimal online learning algorithm of a wavelet neural network is proposed. The algorithm provides not only the tuning of synaptic weights in real time, but also the tuning of dilation and translation factors of daughter wavelets. The algorithm has both tracking and smoothing properties, so the wavelet networks trained with this algorithm can be efficiently used for prediction, filtering, compression and classification of various non-stationary noisy signals. 相似文献
11.
An adaptive neural network controller is developed to achieve output-tracking of a class of nonlinear systems. The global L2 stability of the closed-loop system is established. The proposed control design overcomes the limitation of the conventional adaptive neural control design where the modeling error brought by neural networks is assumed to be bounded over a compact set.Moreover,the generalized matching conditions are also relaxed in the proposed L2 control design as the gains for the external disturbances entering the system are allowed to have unknown upper bounds. 相似文献
12.
This paper aims to propose a stable fuzzy wavelet neural-based adaptive power system stabilizer (SFWNAPSS) for stabilizing the inter-area oscillations in multi-machine power systems. In the proposed approach, a self-recurrent Wavelet Neural Network (SRWNN) is applied with the aim of constructing a self-recurrent consequent part for each fuzzy rule of a Takagi-Sugeno-Kang (TSK) fuzzy model. All parameters of the consequent parts are updated online based on Direct Adaptive Control Theory (DACT) and employing a back-propagation-based approach. The stabilizer initialization is performed using an approach based on genetic algorithm (GA). A Lyapunov-based adaptive learning rates (LALRs) algorithm is also proposed in order to speed up the stabilization rate, as well as to guarantee the convergence of the proposed stabilizer. Therefore, due to having a stable powerful adaptation law, there is no requirement to use any identification process. Kundur's four-machine two-area benchmark power system and six-machine three-area power system are used with the aim of assessing the effectiveness of the proposed stabilizer. The results are promising and show that the inter-area oscillations are successfully damped by the SFWNAPSS. Furthermore, the superiority of the proposed stabilizer is demonstrated over the IEEE standard multi-band power system stabilizer (MB-PSS), and the conventional PSS. 相似文献
13.
《Advanced Robotics》2013,27(4):369-383
In this paper, we present a decentralized neural network (NN) adaptive technique for control of robot manipulators in the presence of unknown non-linear functions. Radial basis function NNs are used to approximate the non-linear functions to include the case of both parametric and dynamic uncertainty in each subsystem. The robustifying terms are added to the controllers to overcome the effects of the interconnections. The stability can be guaranteed by using a rigid proof. Finally, simulation is given to illustrate the effectiveness of the proposed algorithm. 相似文献
14.
针对一类可控标准型基础上添加非线性模型误差与故障项的MIMO非线性系统,结合反推技术,提出了神经网络自适应控制方案,对模型误差与故障项进行在线估计。文中鲁棒项用于补偿逼近模型误差,当检测出系统故障时,通过调整各步骤的虚拟控制量来补偿故障项,消除故障项对系统的影响。通过理论证明实现了提出的控制方法使得各残差信号一致有界,并最终收敛到一个小的邻域内。实例仿真表明该方案的可行性。 相似文献
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常规小脑模型关节控制器(CMAC)神经网络采用线性均匀量化,稳态控制精度与量化级数相关,增加量化级数可提高稳态精度但会导致内存空间和计算量的增加.本文提出一种可采用幂函数、高斯、分段3种非线性量化方法的非线性量CMAC神经网络,并分析了非线性量化CMAC的收敛性,解释了非线性量化提高稳态精度的本质.面向一阶惯性环节、二阶系统、一阶时变系统及二阶时变系统,分别跟踪方波、斜坡、正弦波、三角波和加速度等输入信号,仿真验证了非线性量化CMAC神经网络控制器的有效性,给出了不同非线性量化方法的适用性.结果表明,非线性量化CMAC参数容易设定,物理意义清晰,与常规CMAC对比,其快速性和控制精度显著提高,可以有效解决实际复杂非线性时变系统的控制. 相似文献
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通过对影响黄金价格变动的主要因素的研究,提出一种基于小波神经网络的黄金价格预测模型。给出了具体的网络学习算法,并结合算法对黄金价格进行预测。为验证模型有效性,进行了对比测试。分析结果表明,小波神经网络模型比传统的BP神经网络模型具有收敛速度快、预测精度高的特点。 相似文献
17.
In this note, the authors study the tracking problem for uncertain nonlinear time-delay systems with unknown non-smooth hysteresis described by the generalised Prandtl–Ishlinskii (P-I) model. A minimal learning parameters (MLP)-based adaptive neural algorithm is developed by fusion of the Lyapunov–Krasovskii functional, dynamic surface control technique and MLP approach without constructing a hysteresis inverse. Unlike the existing results, the main innovation can be summarised as that the proposed algorithm requires less knowledge of the plant and independent of the P-I hysteresis operator, i.e. the hysteresis effect is unknown for the control design. Thus, the outstanding advantage of the corresponding scheme is that the control law is with a concise form and easy to implement in practice due to less computational burden. The proposed controller guarantees that the tracking error converges to a small neighbourhood of zero and all states of the closed-loop system are stabilised. A simulation example demonstrates the effectiveness of the proposed scheme. 相似文献
18.
论述了小波神经网络的系统结构及算法,并根据齿轮振动信号的频域变化特征,提取特征向量作为输入,利用小波神经网络建立特征向量与故障模式之间的映射关系,建立了基于该算法的齿轮故障诊断模型。仿真结果表明:与传统的BP神经网络相比,该模型显著缩短了训练时间。该小波神经网络进行机械故障诊断是有效的。 相似文献
19.
Adaptive output feedback control for nonlinear time-delay systems using neural network 总被引:6,自引:0,他引:6
This paper extends the adaptive neural network (NN) control approaches to a class of unknown output feedback nonlinear time-delay systems. An adaptive output feedback NN tracking controller is designed by backstepping technique. NNs are used to approximate unknown functions dependent on time delay, Delay-dependent filters are introduced for state estimation. The domination method is used to deal with the smooth time-delay basis functions. The adaptive bounding technique is employed to estimate the upper bound of the NN approximation errors. Based on Lyapunov- Krasovskii functional, the semi-global uniform ultimate boundedness of all the signals in the closed-loop system is proved, The feasibility is investigated by two illustrative simulation examples. 相似文献