一种稳定的机器人神经网络控制器研究

研究一种稳定的机器人神经网络（ＮＮ）控制器，提出了由神经网络控制器和监督控制器构成的控制方案，给出了控制器的设计方法及ＮＮ学习自适应律，并基于Ｌｙａｐｕｎｏｖ方法证明了控制系统的稳定性和ＮＮ参数收敛性，仿真结果表明该控制方案具有良好的鲁棒性和参数收敛性，从而证明控制器的有效性。     

基于遗传算法的神经网络自适应控制器的研究

提出了一种基于遗传算法的神经网络自适应控制方法。该方法是针对ＢＰ算法训练神经网络控制系统时收敛速度慢、动态特性不够理想等不足，用改进的遗传算法来优化神经网络辨识器与控制器的参数，以提高控制系统的性能，仿真实验表明该控制器对于非线性、时变、滞后等对象都具有很好的控制精度、鲁棒性和动态特性。     

基于复合正交神经网络的灰色PID控制

结合传统反馈控制方法和灰色预测控制的预测控制器已在控制系统中获得了成功的应用。由于复合正交神经网络具有学习算法简单、收敛速度快,有逼近线性或非线性函数的优良特性。与灰色预测方法相比,神经网络预测精度高,且误差可控,如果把神经网络作为灰色预测器,建立一种灰色预测控制,那么就会在控制系统中获得良好的控制性能。为此,提出一种结合传统的PID控制和神经网络灰色预测补偿的灰色PID控制器,可对系统进行在线灰色估计和控制,由复合正交神经网络对不确定部分建立的灰色预测模型,可根据系统的参数变化来自动调节预测补偿值,使系统响应具有适应性。仿真结果表明,与传统的PID控制方法相比,该控制器可获得更为优良的动态性能和鲁棒性。     

多纸种造纸机定量控制的神经网络方法

用有限个时不变模型描述多纸种造纸机不确定系统，用神经网络方法实现了该纸机的定量控制。该方法以经过训练的神经网络控制器代替常规控制器，可同时达到对多个模型的控制，进而达到对不确定系统的控制。     

多纸种造纸机定量控制的神经网络方法

用有限个时不变模型描述多纸种造纸机不确定系统，用神经网络方法实现了该纸机的定量控制。该方法以经过训练的神经网络控制器代替常规控制器，可同时达到对多个模型的控制，进而达到对不确定系统的控制。     

神经网络在细纱机中的应用

介绍了一种基于PLC和DSP的细纱杌控制系统.该系统针对细纱机控制系统的非线性与传统PID控制方法的不足,提出了一种改进型基于RBF神经网络在线辨识的单神经元PID自适应控制方法.该方法构造了一个RBF网络对系统进行在线辨识,建立起在线参考模型,由单神经元控制器完成控制器参数的学习,从而实现控制器参数的在线调整.仿真试验结果表明.该控制器控制精度高,动态性能好,其控制效果优于传统的PID控制器.     

基于自组织模糊CMAC神经网络的不确定系统的H∞鲁棒自适应控制

针对一类不确定系统，提出一种基于自组织模糊小脑模型（SOFCMAC）神经网络的H∞鲁棒自适应控制方法，通过设计标称系统的H∞控制器，并采用SOFCMAC神经网络在线对消系统的建模不确定性产生的误差，可保证不确定闭环稳定并具有H∞性能，证明了SOFCMAC神经网络H∞鲁棒自适应控制系统的稳定性，仿真算例表明了该方法的有效性。     

一种混合神经网络飞行控制方法研究

讨论了一种基于神经网络控制的飞行控制方法。针对复杂非线性系统难以建立精确模型的特点,利用神经网络的任意非线性逼近能力进行控制器设计,首先应用神经网络在线辨识对象逆模型,进行控制系统反馈线性化;接着利用circle theorem（圆定理）设计线性PID鲁棒控制器,控制系统输出跟随系统输入,然后应用神经网路自适应逆方法设计混合控制器,最后以F-8飞机纵向飞行控制模态为研究对象进行仿真。仿真结果表明,该控制方法具有较强的自适应和抗干扰能力。     

基于VB的模糊神经网络控制技术研究

本文将模糊逻辑推理与神经网络控制技术融合，设计了模糊神经网络控制器，仿真结果验证了控制器性能良好，并获取控制器的优化参数。基于VB技术创建了ACTTVEX控件，将该控件嵌入组态软件中进行双水槽液位控制系统的实时控制，结果表明控制及时，系统稳定性得到提高，具有广阔的推广应用前景。     

基于模糊RBF神经网络的PID及其应用

针对传统的PID控制器参数固定而导致在控制中效果差的问题,提出一种基于模糊RBF神经网络智能PID控制器的设计方法。该方法结合了模糊控制的推理能力强与神经网络学习能力强的特点,将模糊控制与RBF神经网络相结合以在线调整PID控制器参数,整定出一组适合于控制对象的kp, ki, kd参数。将算法运用到电机控制系统的PID参数寻优中,仿真结果表明基于此算法设计的PID控制器改善了电机控制系统的动态性能和稳定性。     

Adaptive CMAC neural control of chaotic systems with a PI-type learning algorithm

The cerebellar model articulation controller (CMAC) has the advantages such as fast learning property, good generalization capability and information storing ability. Based on these advantages, this paper proposes an adaptive CMAC neural control (ACNC) system with a PI-type learning algorithm and applies it to control the chaotic systems. The ACNC system is composed of an adaptive CMAC and a compensation controller. Adaptive CMAC is used to mimic an ideal controller and the compensation controller is designed to dispel the approximation error between adaptive CMAC and ideal controller. Based on the Lyapunov stability theorems, the designed ACNC feedback control system is guaranteed to be uniformly ultimately bounded. Finally, the ACNC system is applied to control two chaotic systems, a Genesio chaotic system and a Duffing–Holmes chaotic system. Simulation results verify that the proposed ACNC system with a PI-type learning algorithm can achieve better control performance than other control methods.     

基于混沌优化的非线性预测控制器

针对非线性系统的控制问题,本文将神经网络辨识、混沌优化和预测控制思想有机结合,提出了一种新型非线性预测控制器.该控制器以神经网络作为预测模型,混沌优化算法作为滚动优化策略,避免了非线性预测控制中复杂的梯度计算和矩阵求逆问题.另外在训练神经网络过程中,采用了带混沌机制的自适应学习率的BP算法,以提高神经网络的收敛能力和收敛速度.仿真研究说明了该非线性预测控制器的有效性及实时性.     

Hénon混沌同步的自适应逆控制

基于自适应逆控制原理,在噪声存在的情况下,提出了一种实现混沌同步的自适应逆控制方法.为此首先简要介绍了控制方法结构,然后利用神经网络算法对被控对象模型进行辨识和训练发送端的控制器.仿真证明该方法能够很好的消除干扰,使得被噪声污染的混沌同步系统能够保持良好的同步.此外自适应逆控制是开环控制,具有很好的实施性.     

基于混沌变量的神经网络自适应控制系统的优化设计

基于混沌变量，提出一种神经网络自适应控制系统的优化设计方案。采用混沌状态变量优化神经网络辨识器和控制器权参数，实现混沌粗搜索和局部细搜索相结合，搜索出控制系统参数的全局最优值,具有全局性、快速性、并行性。仿真实验表明采用该方案对强非线性对象的控制具有精度高、超调小、响应快、调节时间短等优点。     

Hermite-neural-network-based adaptive control for a coupled nonlinear chaotic system

A TSK-type Hermite neural network (THNN) is studied in this paper. Since the output weights of the THNN use a functional-type form, it provides powerful representation, high learning performance and good generalization capability. Then, a Hermite-neural-network-based adaptive control (HNNAC) system which is composed of a neural controller and a robust compensator is proposed. The neural controller utilizes a THNN to online approximate an ideal controller, and the robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability. Moreover, a proportional-integral (PI)-type learning algorithm is derived to speed up the convergence of the tracking error. Finally, the proposed HNNAC system is applied to synchronize a coupled nonlinear chaotic system. In the simulation study, it shows that the proposed HNNAC system can achieve favorable synchronization performance without requiring a preliminary offline tuning.

     

Development of PI training algorithms for neuro-wavelet control on the synchronization of uncertain chaotic systems

This paper investigates a neuro-wavelet control (NWC) system to address the problem of synchronization control of uncertain chaotic systems. In this NWC system, a wavelet neural network (WNN) controller is the principal tracking controller designed to mimic the perfect control law and an auxiliary compensation controller is used to recover the residual approximation error so that the favorable synchronization can be achieved. Moreover, the proportional-integral (PI) training algorithms of the control system are derived from the Lyapunov stability theorem, which are utilized to update the adjustable parameters of WNN controller on-line for further assuring system stability and obtaining a fast convergence. In addition, to relax the requirement of unknown uncertainty bound, a bound estimation law is derived to estimate the uncertainty bound. Finally, some numerical simulations are presented to illustrate the effectiveness of the proposed control strategy. The simulation results demonstrate that the proposed NWC with PI training algorithms can synchronize the chaotic systems more accurately than the other control strategies.     

FPGA‐based adaptive dynamic sliding‐mode neural control for a brushless DC motor

In the adaptive neural control design, since the number of hidden neurons is finite for real‐time applications, the approximation errors introduced by the neural network cannot be inevitable. To ensure the stability of the adaptive neural control system, a switching compensator is designed to dispel the approximation error. However, it will lead to substantial chattering in the control effort. In this paper, an adaptive dynamic sliding‐mode neural control (ADSNC) system composed of a neural controller and a fuzzy compensator is proposed to tackle this problem. The neural controller, using a radial basis function neural network, is the main controller and the fuzzy compensator is designed to eliminate the approximation error introduced by the neural controller. Moreover, a proportional‐integral‐type adaptation learning algorithm is developed based on the Lyapunov function; thus not only the system stability can be guaranteed but also the convergence of the tracking error and controller parameters can speed up. Finally, the proposed ADSNC system is implemented based on a field programmable gate array chip for low‐cost and high‐performance industrial applications and is applied to control a brushless DC (BLDC) motor to show its effectiveness. The experimental results demonstrate the proposed ADSNC scheme can achieve favorable control performance without encountering chattering phenomena. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Design of an adaptive self-organizing fuzzy neural network controller for uncertain nonlinear chaotic systems

Though the control performances of the fuzzy neural network controller are acceptable in many previous published papers, the applications are only parameter learning in which the parameters of fuzzy rules are adjusted but the number of fuzzy rules should be determined by some trials. In this paper, a Takagi–Sugeno-Kang (TSK)-type self-organizing fuzzy neural network (TSK-SOFNN) is studied. The learning algorithm of the proposed TSK-SOFNN not only automatically generates and prunes the fuzzy rules of TSK-SOFNN but also adjusts the parameters of existing fuzzy rules in TSK-SOFNN. Then, an adaptive self-organizing fuzzy neural network controller (ASOFNNC) system composed of a neural controller and a smooth compensator is proposed. The neural controller using the TSK-SOFNN is designed to approximate an ideal controller, and the smooth compensator is designed to dispel the approximation error between the ideal controller and the neural controller. Moreover, a proportional-integral (PI) type parameter tuning mechanism is derived based on the Lyapunov stability theory, thus not only the system stability can be achieved but also the convergence of tracking error can be speeded up. Finally, the proposed ASOFNNC system is applied to a chaotic system. The simulation results verify the system stabilization, favorable tracking performance, and no chattering phenomena can be achieved using the proposed ASOFNNC system.     

Adaptive neural complementary sliding-mode control via functional-linked wavelet neural network

Chaos control can be applied in the vast areas of physics and engineering systems, but the parameters of chaotic system are inevitably perturbed by external inartificial factors and cannot be exactly known. This paper proposes an adaptive neural complementary sliding-mode control (ANCSC) system, which is composed of a neural controller and a robust compensator, for a chaotic system. The neural controller uses a functional-linked wavelet neural network (FWNN) to approximate an ideal complementary sliding-mode controller. Since the output weights of FWNN are equipped with a functional-linked type form, the FWNN offers good learning accuracy. The robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Without requiring preliminary offline learning, the parameter learning algorithm can online tune the controller parameters of the proposed ANCSC system to ensure system stable. Finally, it shows by the simulation results that favorable control performance can be achieved for a chaotic system by the proposed ANCSC scheme.     

一种模糊神经网络控制器参数的混沌优化设计

通过模糊控制与神经网络相串联的方式构成模糊神经网络系统，然后提出一种基于模拟退火策略的混沌优化算法，将该算法引入模糊神经网络参数域中进行优化，实现混沌粗搜索与细搜索相结合优化目的，体现出具有更强的模糊神经网络参数全局最优解的搜索能力。采用该控制器对一个非线性对象进行控制。仿真实验表明，该方法能有效地实现模糊神经网络控制器参数优化，控制具有无振荡、超调小、调节时间短等优点，算法结构简单，容易实现。