任意切换下高能随机系统的神经网络预设控制

针对一类不确定高能随机非线性系统,开展自适应神经网络backstepping控制研究,并保证在任意切换信号下的预设跟踪性能.该高能系统假定系统动态和任意切换信号未知.首先,利用预设性能控制,保证跟踪控制性能;其次,RBF神经网络用来克服未知系统动态,仅用到单一自适应更新参数,从而克服过参数问题;最后,基于公共的Lyapunov稳定性理论提出自适应神经网络控制策略,并减少了学习参数.最终结果表明所设计的公共控制器能保证所有闭环信号半全局最终一致有界,并能在任意切换下保证预设的跟踪性能.仿真结果进一步表明所提出方法的有效性.     

一种基于模糊径向基函数神经网络的自学习控制器

提出了一种新型的基于模糊径向基函数 (RBF)的神经网络学习控制器 ,并应用于电液伺服系统 .由于RBF网络和模糊推理系统具有函数等价性 ,采用模糊经验值方法选取网络中心值和基函数数目 .与一般的神经网络自学习控制器不同 ,以系统动态误差作为网络输入量 ,RBF神经网络控制器学习的是整个系统的动态逆过程 ,因而控制性能明显提高 .对电液位置伺服系统的仿真和实验结果表明 ,该控制方案可以有效提高系统的控制精度和自适应能力     

基于RBF神经网络滑模控制的车辆横向控制研究

针对车辆横向控制系统中滑模控制器存在的抖振现象对转向机械结构带来的损耗问题,提出了一种基于RBF神经网络的滑模控制算法。利用RBF神经网络较强的自学习能力实时在线调节滑模控制器的切换项增益参数,增强系统的抗干扰能力与动态性能。将车辆实际参数代入仿真数学模型中,在Simulink仿真环境中进行对比仿真实验,仿真结果表明:该控制算法跟踪性能好,能够有效降低滑模控制器的抖振,满足车辆横向控制要求。     

基于神经网络的PMSM自适应滑模控制

结合滑模控制和神经网络各自的优点,对永磁同步电机(PMSM)提出了一种基于神经网络的PMSM自适应滑模控制方案.首先设计了带积分操作的滑模变结构位置控制器,通过递归神经网络的在线学习来实时估计系统参数变化和外部负载扰动等不确定性的界限,减小滑模控制器的控制量.进而,在滑模控制器中又引入饱和函数取代符号函数,进一步减弱"抖振"现象.理论分析和实验仿真对比研究的结果表明所提出方法具有优越的动态性能和鲁棒性.     

神经网络在PID控制器参数优化中的研究

研究PID控制器参数优化问题.工业过程控制要求稳定性,跟踪特性均应实时快速.由于PID控制效果取决于比例、积分和微分3个参数取值,传统PID参数采用试凑方式进行优化,往往费时且难以满足实时控制效果,导致控制精度不高.为了提高PID控制精度,改善系统性能,提出一种神经网络的PID参数优化方法.方法将PID控制器输入作为神经网络输入,最优PID控制性能作为神经网络的输出,通过神经网络的联想记忆能力和自学习适应能力,在控制过程中动态调整PID参数(比例、积分、微分),从而实现PID控制器参数实时优化,获得最佳PID控制效果.仿真结果表明,应用神经网络的PID参数优化方法提高了PID控制精度和系统响应速度,具有较强的自适应性和鲁棒性.     

线性时变参数系统切换H_∞输出反馈控制

考虑时变参数系统的切换H∞控翻问题.提出了由参数触发的切换策略,由此在最小驻冒时间的限制下,将线性时变参数系统分解为若干具有范数有界不确定性的子系统.利用多Lyapunov函数方法分别设计各子系统的输出动态反馈控制器.使在切换策略驱动下构成的闭环系统满足H∞控制性能.仿真算例完整地实现了理论方法,并验证了其有效性.     

一类非线性系统的模糊滑模控制

针对一类非线性不确定系统,提出了一种模糊滑模控制器的设计方法。采用模糊神经网络设计系统控制器,基于梯度符号变化的局部学习率自适应BP算法,对模糊神经网络的隶属函数中心、宽度和网络权值进行调节,使滑模等效控制的模糊系统后件参数是自适应的;在系统存在外部干扰并有界时,利用李亚普诺夫方法证明了系统控制的稳定性。通过Matlab仿真分析,表明了所设计的模糊滑模控制器不但具有鲁棒性,而且具有良好的跟踪性能。     

基于模糊神经网络的变换器自适应控制方法

提出了一种新型的基于模糊神经网络自适应PI调节电流控制电压型PWM变换器方法.结合了模糊神经网络控制与PI控制器,根据三相电流比较产生的三相电流误差和电流误差变化率,自动调整P、I参数,提高了电流的控制精度和变换器的动态性能.采用MATLAB/Simulink对常规PI控制器和模糊神经网络自适应PI控制器进行了仿真对比.仿真结果表明了采用模糊神经网络自适应PI控制器,其系统输出的误差及误差变化要小,系统的跟踪精度得以提高,动态性能得到改善.     

具有零动态的SISO仿射非线性系统的神经网络自适应跟踪控制

针对具有零动态的SISO仿射非线性系统提出了一种神经网络直接自适应跟踪控制方法.采用梯度下降算法最小化未知理想控制器与神经网络控制器的误差代价函数以获得参数自适应律,控制器中无需另加鲁棒控制项.基于Lyapunov稳定性定理证明了在该控制器的作用下能保证输出跟踪误差及相应闭环系统的所有状态最终一致有界及神经网络参数的收敛性.仿真结果验证了该文方法的有效性.     

基于神经网络与多模型的非线性自适应广义预测解耦控制

针对一类非线性多变量离散时间动态系统,提出了基于神经网络与多模型的非线性自适应广义预测解耦控制方法.该控制方法由线性鲁棒广义预测解耦控制器和神经网络非线性广义预测解耦控制器以及切换机构组成.线性鲁棒广义预测解耦控制器用于保证闭环系统输入输出信号有界,神经网络非线性广义预测解耦控制器能够改善系统性能.切换策略通过对上述两种控制器的切换,保证系统稳定的同时,改善系统性能.同时本文给出了所提自适应解耦控制方法的稳定性和收敛性分析.最后,通过仿真实例验证了该方法的有效性.     

一类工业运行过程多模型自适应控制方法

针对一类动态未知的工业运行过程,提出一种基于神经网络补偿和多模型切换的自适应控制方法.为充分考虑底层跟踪误差对整个运行过程优化和控制的影响,将底层极点配置控制系统和上层运行层动态模型相结合,作为运行过程动态模型.针对参数未知的运行过程动态模型,设计由线性鲁棒自适应控制器、基于神经网络补偿的非线性自适应控制器以及切换机制组成的多模型自适应控制算法.采用带死区的递推最小二乘算法在线辨识控制器参数,克服了投影算法收敛速度慢、对参数初值灵敏的局限.理论分析和仿真实验结果表明了所提方法的有效性.     

基于改进模糊神经网络的PID参数自整定

针对传统PID整定控制效果差且单纯神经网络整定存在参数学习和调整困难等问题,提出了一种基于改进模糊神经网络的PID参数整定方法。在该方法中,PID控制器的控制参数采用基于Mamdani模型的模糊神经网络进行自适应整定,模糊神经网络参数采用混沌遗传算法离线粗调和BP算法在线细调的方式进行学习和调整,仿真结果表明该整定策略动态响应快、误差控制精度高且网络中各节点及参数物理意义明确。最后分别从模糊规则数的变化及适应度函数的选取两方面提出两种优化方案,仿真结果表明增加模糊规则数或采用不同的适应度函数都有利于进一步减小控制误差。     

神经元自适应PID控制器在机组控制中应用

针对机组控制系统动态范围宽、要求响应速度快、超调量小及参数时变的特性,研究了一种基于神经元的自适应PID控制器。利用神经网络的在线调整功能,自动调整PID控制参数,以适应被控过程的参数变化和克服扰动的影响。仿真和实验结果表明,该算法简洁实用,控制效果优于传统的PID控制。     

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     

Neural network controller with flexible structure based on feedback-error-learning approach

In practice, the back-propagation algorithm often runs very slowly, and the question naturally arises as to whether there are necessarily intrinsic computation and difficulties with training neural networks, or better training algorithms might exist. Two important issues will be investigated in this framework. One establishes a flexible structure, to construct very simple neural network for multi-input/output systems. The other issue is how to obtain the learning algorthm to achieve good performance in the training phase. In this paper, the feedforward neural network with flexible bipolar sigmoid functions (FBSFs) are investigated to learn the inverse model of the system. The FBSF has changeable shape by changing the values of its parameter according to the desired trajectory or the teaching signal. The proposed neural network is trained to learn the inverse dynamic model by using back-propagation learning algorithms. In these learning algorithms, not only the connection weights but also the sigmoid function parameters (SFPs) are adjustable. The feedback-error-learning is used as a learning method for the feedforward controller. In this case, the output of a feedback controller is fed to the neural network model. The suggested method is applied to a two-link robotic manipulator control system which is configured as a direct controller for the system to demonstrate the capability of our scheme. Also, the advantages of the proposed structure over other traditional neural network structures are discussed.     

基于动态模型库的多模型切换控制

针对含有有界扰动和模型参数跳变的离散时间系统,提出基于动态模型库的多模型切换控制方法.在模型参数范围未知情况下,利用在线学习的多模型自适应控制算法自动建立多模型,并对模型库中的子模型进行优化.采用具有积分特性的指标函数作为切换准则.在每一采样时刻根据其最小值来选择与实际系统最接近的模型,并将基于此模型的控制器切换为当前控制器.文中证明了该算法能够保证闭环系统的稳定性和跟踪误差的渐近收敛性.计算机仿真结果表明该算法的有效性.     

Prediction and identification using wavelet-based recurrent fuzzy neural networks

This paper presents a wavelet-based recurrent fuzzy neural network (WRFNN) for prediction and identification of nonlinear dynamic systems. The proposed WRFNN model combines the traditional Takagi-Sugeno-Kang (TSK) fuzzy model and the wavelet neural networks (WNN). This paper adopts the nonorthogonal and compactly supported functions as wavelet neural network bases. Temporal relations embedded in the network are caused by adding some feedback connections representing the memory units into the second layer of the feedforward wavelet-based fuzzy neural networks (WFNN). An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the degree measure to obtain the number of fuzzy rules and wavelet functions. Meanwhile, the parameter learning is based on the gradient descent method for adjusting the shape of the membership function and the connection weights of WNN. Finally, computer simulations have demonstrated that the proposed WRFNN model requires fewer adjustable parameters and obtains a smaller rms error than other methods.     

基于模糊神经网络水下机器人直接自适应控制

提出了基于广义动态模糊神经网络的水下机器人直接自适应控制方法, 该控制方法既不需要预先知道模糊神经结构, 也不需要预先的训练阶段, 完全通过在线自适应学习算法构建水下机器人的逆动力学模型. 首先, 本文提出了基于这种网络结构的水下机器人直接自适应控制器, 然后, 利用 Lyapunov 稳定理论, 证明了基于该控制器的水下机器人控制系统闭环稳定性, 最后, 采用某水下机器人模型仿真验证了该控制方法的有效性.     

Adaptive Impedance Control for Upper-Limb Rehabilitation Robot Using Evolutionary Dynamic Recurrent Fuzzy Neural Network

Control system implementation is one of the major difficulties in rehabilitation robot design. A newly developed adaptive impedance controller based on evolutionary dynamic fuzzy neural network (EDRFNN) is presented, where the desired impedance between robot and impaired limb can be regulated in real time according to the impaired limb??s physical recovery condition. Firstly, the impaired limb??s damping and stiffness parameters for evaluating its physical recovery condition are online estimated by using a slide average least squares (SALS)identification algorithm. Then, hybrid learning algorithms for EDRFNN impedance controller are proposed, which comprise genetic algorithm (GA), hybrid evolutionary programming (HEP) and dynamic back-propagation (BP) learning algorithm. GA and HEP are used to off-line optimize DRFNN parameters so as to get suboptimal impedance control parameters. Dynamic BP learning algorithm is further online fine-tuned based on the error gradient descent method. Moreover, the convergence of a closed loop system is proven using the discrete-type Lyapunov function to guarantee the global convergence of tracking error. Finally, simulation results show that the proposed controller provides good dynamic control performance and robustness with regard to the change of the impaired limb??s physical condition.