混沌系统的神经网络预测控制

提出一种基于预测控制的神经网络控制方法，将模型未知时的混沌运动控制到不稳定的不动点（UFP）处，该控制系统不需要UFP的位置及其局性态等知识，它包括观测器、带反馈校正的神经网络在预测器和在线训练的神经网络控制器，其方法简便，收敛速度比现有同类方法快得多，同时还分析了控制系统的稳定性，并证明了神经网络控制器的收敛性，理论推导和仿真结果都表明了该方法的有效性。     

Neural network adaptive control for nonlinear nonnegative dynamical systems

Nonnegative and compartmental dynamical system models are derived from mass and energy balance considerations that involve dynamic states whose values are nonnegative. These models are widespread in engineering and life sciences and typically involve the exchange of nonnegative quantities between subsystems or compartments wherein each compartment is assumed to be kinetically homogeneous. In this paper, we develop a full-state feedback neural adaptive control framework for adaptive set-point regulation of nonlinear uncertain nonnegative and compartmental systems. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals corresponding to the physical system states and the neural network weighting gains. In addition, the neural adaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state-space for nonnegative initial conditions.     

基于神经网络的非线性系统预测函数控制

针对离散非线性系统,利用神经网络非线性激励函数的局部线性表示,提出一种可用于非线性过程的神经网络预测函数控制方法并给出了控制律的收敛性分析.该方法将复杂的神经网络非线性预测方程转化成直观而有效的线性形式,同时利用线性预测函数方法求得解析的控制律,避免了复杂的非线性优化求解,仿真结果表明了算法的有效性.     

Neural network compensation control for mechanical systems with disturbances

Two novel compensation schemes based on accelerometer measurements to attenuate the effect of external vibrations on mechanical systems are proposed in this paper. The first compensation algorithm exploits the neural network as the feedback-feedforward compensator whereas the second is the neural network feedforward compensator. Each compensation strategy includes a feedback controller and a neural network compensator with the help of a sensor to detect external vibrations. The feedback controller is employed to guarantee the stability of the mechanical systems, while the neural network is used to provide the required compensation input for trajectory tracking. Dynamics knowledge of the plant, disturbances and the sensor is not required. The stability of the proposed schemes is analyzed by the Lyapunov criterion. Simulation results show that the proposed controllers perform well for a hard disk drive system and a two-link manipulator.     

基于神经网络的非线性系统多步预测控制

针对离散非线性系统,利用非线性激励函数的局部线性表示,提出一种可用于非线性过程的神经网络多步预测控制方法,并给出了控制律的收敛性分析．该方法将非线性系统处理成简单的线性和非线性两部分,对复杂的非线性多步预测方程给出了直观而有效的线性形式,并用线性预测控制方法求得控制律,避免了复杂的非线性优化求解．仿真结果表明了该算法的有效性．     

基于自适应评价的非线性系统神经网络控制

针对一类非线性系统,提出了一种自适应评价方法．该方法可以控制系统输出对参考信号进行跟踪,其评价函数可直接解析求出．该方法只需一个动作网络用于产生控制动作,并且方法中的网络权值初始化可随机选取．使用Lyapunov方法对整个系统的动态性能进行分析,证明了在一定条件下此方法能保证闭环误差及网络权值一致最终有界．仿真结果与理论分析相一致,证明了所提出方法的有效性．     

Neural network control of nonlinear dynamic systems using hybrid algorithm

In this paper, a hybrid method is proposed to control a nonlinear dynamic system using feedforward neural network. This learning procedure uses different learning algorithm separately. The weights connecting the input and hidden layers are firstly adjusted by a self organized learning procedure, whereas the weights between hidden and output layers are trained by supervised learning algorithm, such as a gradient descent method. A comparison with backpropagation (BP) shows that the new algorithm can considerably reduce network training time.     

基于神经网络MIMO非线性系统自适应输出反馈控制

针对一类具有对象不确定和外部干扰的MIMO(多输入多输出)非线性系统提出了自适应鲁棒输出跟踪控制方案.使用了高斯径向基神经网络自适应补偿对象非线性,高增益观测器被用来估计不能直接测量的输出导数.此方法所设计的控制器不仅保证闭环系统稳定,而且所有状态有界以及跟踪误差一致终值有界.仿真结果充分表明了该方案的有效性和可行性.     

ATM communications network control by neural networks

A learning method that uses neural networks for service quality control in the asynchronous transfer mode (ATM) communications network is described. Because the precise characteristics of the source traffic are not known and the service quality requirements change over time, building an efficient network controller which can control the network traffic is a difficult task. The proposed ATM network controller uses backpropagation neural networks for learning the relations between the offered traffic and service quality. The neural network is adaptive and easy to implement. A training data selection method called the leaky pattern table method is proposed to learn precise relations. The performance of the proposed controller is evaluated by simulation of basic call admission models.     

不确定非线性系统的神经网络自适应H ∞跟踪控制

提出一种H∞与神经网络混合自适应控制系统设计的新方法．对于一类不确定非线性系统，首先运用线性微分包含(LDI)的方法，逼近模型中的非线性部分；然后在考虑外部扰动的情况下，设计忽略不确定项的H∞线性跟踪控制系统参考模型；最后将设计好的H∞线性跟踪控制器用于控制实际的非线性不确定系统，系统状态及其与参考模型的状态误差作为在线神经网络的输入，动态调节网络权值以消除整个系统的不确定项．仿真示例证实了该设计方法的有效性．     

Neural network control of a class of nonlinear systems with actuator saturation

A neural net (NN)-based actuator saturation compensation scheme for the nonlinear systems in Brunovsky canonical form is presented. The scheme that leads to stability, command following, and disturbance rejection is rigorously proved and verified using a general "pendulum type" and a robot manipulator dynamical systems. Online weights tuning law, the overall closed-loop system performance, and the boundedness of the NN weights are derived and guaranteed based on Lyapunov approach. The actuator saturation is assumed to be unknown and the saturation compensator is inserted into a feedforward path. Simulation results indicate that the proposed scheme can effectively compensate for the saturation nonlinearity in the presence of system uncertainty.     

Neural network solution for finite-horizon H-infinity constrained optimal control of nonlinear systems

In this paper, neural networks are used to approximately solve the finite-horizon constrained input H-infinity state feedback control problem. The method is based on solving a related Hamilton-Jacobi-Isaacs equation of the corresponding finite-horizon zero-sum game. The game value function is approximated by a neural network with time- varying weights. It is shown that the neural network approximation converges uniformly to the game-value function and the resulting almost optimal constrained feedback controller provides closed-loop stability and bounded L2 gain. The result is an almost optimal H-infinity feedback controller with time-varying coefficients that is solved a priori off-line. The effectiveness of the method is shown on the Rotational/Translational Actuator benchmark nonlinear control problem.     

Parallel communications for ATM network control and management

This paper describes an end-to-end parallel communications scheme based on a vector routing algorithm (VRA) for ATM network control and management. An information string is partitioned into m parts, which are then coded into k > m parts and sent out on k separate subchannels to the receiver. When m of the k parts are received correctly, the original information can be reconstructed. Two desirable effects are achieved in the context of ATM traffic control: (1) the burstiness of the source traffic can be smoothed out by the partition process; (2) the quality of service in terms of error, loss and delay can be controlled using the number of redundant routes k — m as a control parameter. Our results show that VRA is especially suitable for services with highly bursty traffic. We argue that several network management issues, including reliability, evolution and integration, security, and administration and billing can be addressed in a simple manner using the VRA framework.     

Neural network control of underwater vehicles

In this paper, recent research efforts in the field of the application of neural networks (NNs) for the control of (semi-)autonomous underwater vehicles are reviewed. Based on a literature review the authors propose a classification of approaches to control underwater vehicles using NNs and the presented articles are categorized according to the identified categories. Based on practical results as described in the discussed literature this paper presents a qualitative assessment regarding the performance of the control strategies. Per category, or control strategy, the major advantages and disadvantages are identified and discussed.     

基于神经网络的多媒体通信控制机制研究

在Internet网络的多媒体通信系统中需要解决QoS控制问题,如视频、音频等多媒体数据的同步、网络拥塞控制、多媒体数据传输的QoS协商控制、视频平滑,以及连续多媒体系统的CPU调度等.为了解决好这些控制问题,提出一种基于神经网络的多媒体通信控制机制,把人工智能与多媒体通信技术紧密结合起来,并在Internet网络环境下开发了实用的多媒体通信系统.运行结果表明,该系统效果优越.     

Neural network control of a gas turbine

In this paper we explore the practical use of neural networks for controlling complex non-linear systems. The system used to demonstrate this approach is a simulation of a gas turbine engine typical of those used to power commercial aircraft. The novelty of the work lies in the requirement for multiple controllers which are used to maintain system variables in safe operating regions as well as governing the engine thrust.     

Neural network based iterative learning predictive control design for mechatronic systems with isolated nonlinearity

The paper presents a new nonlinear predictive control design for a kind of nonlinear mechatronic drive systems, which leads to the improvement of regulatory capacity for both reference input tracking and load disturbance rejection. The nonlinear system is first treated into an equal linear time-variant system plus a nonlinear part using a neural network, then an iterative learning linear predictive controller is developed with a similar structure of PI optimal regulator and with setpoint feed forward control. Because the overall control law is a linear one, this design gives a direct and also effective multi-step prediction method and avoids the complicated nonlinear optimization. The control law is also an accurate one compared with traditional linearized method. Besides, changes of the system state variables are considered in the objective function with control performance superior to conventional state space predictive control designs which only consider the predicted output errors. The proposed method is compared with conventional state space predictive control method and classical PI optimal control method. Tracking performance, robustness and disturbance rejection are enlightened.     

Neural network approach for identification of Hammerstein systems

In this paper, four different on-line gradient-based learning algorithms for training neural network Hammerstein models are presented in a unified framework. These algorithms, namely the backpropagation for series–parallel models, the backpropagation, sensitivity method, and truncated backpropagation through time algorithm (BPTT) for parallel models are derived, analysed, and compared. For the truncated BPTT, it is shown that determination of the number of unfolding time steps, necessary to calculate the gradient with an assumed degree of accuracy, can be made on the basis of impulse response functions of sensitivity models. The algorithms are shown to differ in their computational complexity, gradient approximation accuracy, and convergence rates. Numerical examples are also included to compare the performance of the algorithms.