Design of an intelligent prediction-based neural network controller for multi-scroll chaotic systems

An indispensable part of the precise control of multi-scroll chaotic systems, model identification has received increasing attention in recent years. Because of plant uncertainty and unmodeled dynamics, conventional control methods cannot guarantee a sufficiently high-performance for stabilizing multi-scroll chaotic systems. In an effort to tackle the matter better, we propose an intelligent controller called the adaptive neural network prediction-based controller (NN-PbC ). The specified neural network is trained with the system model, which is extracted from a time series. In actual practice, the data are divided into two sets. One set is used for training and the other set for testing. In fact, a generalized NN will perform well for both training and testing data. The prediction-based control method is then applied to the obtained neural network model to stabilize the multiple equilibrium points. The stability of the closed-loop system is proven. In addition, simulation examples on two typical multi-scroll chaotic systems are presented to demonstrate the effectiveness of the proposed controller.     

基于强化学习神经网络的车速跟踪控制

提出一种用于汽车排放试验中驾驶机器人对车速跟踪控制的新方法.该控制方法基于神经网络并结合强化学习的自适应能力,通过神经网络的在线学习对车速进行跟踪控制.利用试验汽车所获得的数据,首先开发出用于车速控制的神经网络模型.然后基于强化学习神经网络结构设计神经网络控制器以取得车速跟踪的自适应控制.在仿真研究中,使用神经网络车速控制模型替代实际汽车来训练初始控制器,并用开发与训练好的自学习神经网络控制器用于汽车车速跟踪控制.结果表明,所开发的神经网络控制器具有良好的车速跟踪性能,控制效果明显.     

基于自适应模糊神经网络控制器的网络控制系统

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。     

Iterative inversion of neural networks and its application toadaptive control

An iterative constrained inversion technique is used to find the control inputs to the plant. That is, rather than training a controller network and placing this network directly in the feedback or feedforward paths, the forward model of the plant is learned, and iterative inversion is performed on line to generate control commands. The control approach allows the controllers to respond online to changes in the plant dynamics. This approach also attempts to avoid the difficulty of analysis introduced by most current neural network controllers, which place the highly nonlinear neural network directly in the feedback path. A neural network-based model reference adaptive controller is also proposed for systems having significant dynamics between the control inputs and the observed (or desired) outputs and is demonstrated on a simple linear control system. These results are interpreted in terms of the need for a dither signal for on-line identification of dynamic systems.     

A harmonic balance approach to robust neural control of MIMO nonlinear processes applied to a distillation column

Robust stability and performance are the two most basic features of feedback control process. The harmonic balance analysis based on the describing function technique enables to analyze the stability of limit cycles arising from a closed loop control process operating over nonlinear plants. In this work a robust stability analysis based on the harmonic balance is presented and applied to a neural network controller in series with a dynamic multivariable nonlinear plant under generic Lur’e configuration. The neural controller is replaced by its sinusoidal input describing function while a linearized model is derived to represent the nonlinear plant dynamics. The uncertainty induced by the high harmonics effect for the neural controller, together with the neglected nonlinear dynamics due to plant linearization are incorporated in the robustness analysis as structured norm bounded uncertainties. Stability and robustness conditions for the neural closed loop control system are discussed using the harmonic balance equation together with the structured singular values of the uncertainty. The application to a multivariable binary distillation column under feedback neurocontrol illustrates the usefulness of the robustness approach here developed to predict the absence of limit cycles, which of course is subject to the usual restrictions of the describing function method.     

具有学习功能的多值模糊神经网络控制系统

对于具有非线性、大时滞、不确定性等特性的难以用精确数学模型描述的多变量复杂系统,靠传统控制理论难以获得理想的控制效果。基于模糊神经网络控制技术不依赖于被控对象精确的数学模型,且能根据被控对象参数的变化自适应调节控制规则和隶属函数参数的特性,进行了采用模糊神经网络控制器实现其控制的应用研究。采用典型的前向型模糊神经网络模型,给出了具有学习功能的多值模糊神经网络控制系统的一种设计方法。仿真实验证明,该系统能够获得较理想的控制效果。     

数据融合在选矿自动控制中的应用

针对多传感器测控系统应用于选矿自动控制中,导致信息量增加和信息处理难度增大的问题,提出了基于神经网络的信息融合模型,利用神经网络强大的并行运算能力和处理不确定信息的能力,对多传感器检测出的信息进行融合,通过融合,不但消除了信息中的不确定因素,又降低了信息的维数。将融合后的信息作为控制器的输入,从而使控制器的设计简单、可靠。     

Multi‐Variable Direct Self‐Organizing Fuzzy Neural Network Control for Wastewater Treatment Process

A multi‐variable direct self‐organizing fuzzy neural network control (M‐DSNNC) method is proposed for the multi‐variable control of the wastewater treatment process (WWTP). In this paper, the proposed control system is an essential multi‐variable control method for the WWTP. No exact plant model is required, which avoids the difficulty of establishing the mathematics model of WWTP. The M‐DSNNC system is comprised of a fuzzy neural network controller and a compensation controller. The fuzzy neural network is used for approximating the ideal control law under a general nonlinear system. Moreover, the neural network is designed in a self‐organizing mode to adapt the uncertainty environment. Simulation results, based on the international benchmark simulation model No.1 (BSM1), demonstrate that the control accuracy is improved under the proposed M‐DSNNC method, and the controller has a much stronger decoupling ability.     

一类非线性多变量系统的多模型自适应控制

针对一类不确定的非线性多变量离散时间动态系统,提出了一种基于切换的多模型自适应控制方法.该控制方法的特点在于以下两个方面:首先,引入一个高阶差分算子使得非线性系统的非线性项的限制条件不再要求全局有界;其次,提出的控制方法由线性自适应控制器、神经网络非线性自适应控制器以及切换机构组成:线性控制器用来保证闭环系统的输入输出信号有界,神经网络非线性控制器用来改善闭环系统的性能,基于性能指标的切换机构在每一时刻选择性能指标较好的控制器对系统进行控制.理论分析和仿真实验说明了提出的多模型自适应控制方法的有效性.     

时滞系统的神经网络辨识与控制算法

针对时滞系统、应用神经网络的非线性逼近能力,采用神经网络实现内模控制中被控对象的正模型及内模控制器,用Lyapunov稳定性定理证明神经网络控制系统的稳定性。仿真结果说明神经网络内模控制方案的优越性。     

二级倒立摆的Sugeno型模糊神经网络控制

为了提高二级倒立摆系统实时控制的响应速度和稳定性,在设计Mamdani型模糊推理规则控制器控制倒立摆系统稳定的基础上,设计了一种更有效率的基于Sugeno型模糊推理规则的模糊神经网络控制器.该控制器使用BP神经网络和最小二乘法的混合算法进行参数训练.能够准确归纳输入输出量的模糊隶属度函数和模糊逻辑规则.通过与Mamdani型控制器的仿真对比及实际控制实验结果,表明该Sugeno型模糊神经网络控制器时二级倒立摆实验装置的控制具有良好的稳定性、快速性和较高的控制精度.     

基于神经网络的二容水箱广义预测控制

针对二容水箱的非线性特点,提出了一种基于神经网络的二容水箱广义预测控制策略。应用收敛速度较快,精确度较高的LM算法训练神经网络,推导了神经网络的导数方程,用迭代法求解输入u的雅克比矩阵和海森矩阵,继而用Newton-Raphson算法求解u,进行了对二容水箱机理模型与实际对象的控制实验,并就控制结果进行了对比分析。实验结果表明,神经网络广义预测控制(NGPC)方法在对实际的二容水箱进行控制时取得了较好的效果。     

具有未知死区输入非线性系统的迭代学习控制

针对一类具有死区输入非线性系统,提出一种实现有限作业区间轨迹跟踪控制的神经网络迭代学习算法.基于Lyapunov-like方法设计学习控制器,回避了常规迭代学习控制中受控系统非线性特性需满足全局Lipschitz连续条件的要求.为处理输入死区,利用神经网络逼近这种强非线性特性;同时,通过对神经网络逼近误差界的估计并在控制器中设置补偿作用以消除其影响,从而提高系统的跟踪性能.     

Development of a neural network based integrated control system of 120 ton/h capacity boiler

An integrated control system based on artificial neural network (ANN) is presented in this paper to control a 120 ton/h capacity boiler of the Zia Fertilizer Company Limited (ZFCL), Ashuganj, Bangladesh. The process inverse dynamic modelling technique is applied to design the proposed controller. A multilayer feed-forward neural network is trained to identify the unknown inverse dynamic model of the boiler plant by a well known learning algorithm called backpropagation. The training data were collected from the history file of ZFCL. A new software controller is then developed for integrated control system of the ZFCL boiler using the weights of the trained network. Both the training mode and running mode of the developed controller are presented in this paper. The controller output is also converted into electrical signal using pulse width control technique. The generated signal is used for on-line regulation of the control valve through the parallel port of the computer. The developed controller is tested by using the boiler input–output data that are not used during the training. The output response and performance of the developed controller is compared with those of the existing PID controller of the plant.     

一种广义模糊神经网络等效滑模同步伺服系统

针对永磁同步伺服系统参数摄动、非线性及不确定因素等问题,提出了一种新型模糊神经网络等效滑模控制方案。利用一维输入的径向基神经网络与等效滑模复合控制器,实现了PMSM系统的快速跟踪控制。该方案对负载扰动、系统参数变化等具有很强的自适应性和鲁棒性,并且综合了模糊控制、神经网络的优点,是一种较理想的智能控制策略。在MATLAB环境下的仿真结果表明控制器具有良好的动静态品质,并且工程实现方便,为提高PMSM伺服系统性能提供了一个有效途径。     

输出非对称死区的非严格反馈非线性系统控制

本文研究了具有输出非对称死区和状态含未知控制方向的非严格反馈非线性系统, 设计了稳定的自适应 神经网络控制器. 首先, 针对输出非对称死区的问题, 本文采用死区逆的方法, 构造光滑模型逼近原死区模型. 其 次, 在控制器设计过程中, 基于障碍Lyapunov函数的构造, 动态面控制和反步法, 设计出自适应控制信号, 虚拟控制 信号和实际控制信号. 通过稳定性分析, 证明所设计的神经网络控制器可以保证闭环系统内所有信号是半全局一致 最终有界. 最后, 通过MATLAB数值仿真, 说明所设计控制器的有效性.     

Adaptive RTRL based neurocontroller for damping subsynchronous oscillations using TCSC

Modern interconnected electrical power systems are complex and require perfect planning, design and operation. Hence the recent trends towards restructuring and deregulation of electric power supply has put great emphasis on the system operation and control. Flexible AC transmission system (FACTS) devices such as thyristor controlled series capacitor (TCSC) are capable of controlling power flow, improving transient stability and mitigating subsynchronous resonance (SSR). In this paper an adaptive neurocontroller is designed for controlling the firing angle of TCSC to damp subsynchronous oscillations. This control scheme is suitable for non-linear system control, where the exact linearised mathematical model of the system is not required. The proposed controller design is based on real time recurrent learning (RTRL) algorithm in which the neural network (NN) is trained in real time. This control scheme requires two sets of neural networks. The first set is a recurrent neural network (RNN) which is a fully connected dynamic neural network with all the system outputs fed back to the input through a delay. This neural network acts as a neuroidentifier to provide a dynamic model of the system to evaluate and update the weights connected to the neurons. The second set of neural network is the neurocontroller which is used to generate the required control signals to the thyristors in TCSC. This is a single layer neural network. Performance of the system with proposed neurocontroller is compared with two linearised controllers, a conventional controller and with a discrete linear quadratic Gaussian (DLQG) compensator which is an optimal controller. The linear controllers are designed based on a linearised model of the IEEE first benchmark system for SSR studies in which a modular high bandwidth (six-samples per cycle) linear time-invariant discrete model of TCSC is interfaced with the rest of the system. In the proposed controller, since the response time is highly dependent on the number of states of the system, it is often desirable to approximate the system by its reduced model. By using standard Hankels norm approximation technique, the system order is reduced from 27 to 11th order by retaining the dominant dynamic characteristics of the system. To validate the proposed controller, computer simulation using MATLAB is performed and the simulation studies show that this controller can provide simultaneous damping of swing mode as well as torsional mode oscillations, which is difficult with a conventional controller. Moreover the fast response of the system can be used for real-time applications. The performance of the controller is tested for different operating conditions.     

神经网络预测控制及其在卫星姿态模拟系统中的应用

针对卫星姿态仿真系统非线性和时变性的特点，提出了一种基于最小方差优化算法的神经网络预测控制器，同时预测模型采用系统主特性与非线性补偿两部分网络叠加的结构，较好地利用了系统的已知信息。实验证明该控制方案鲁棒性强，具有较好的动态控制性能，适于非线性系统的自适应调节。     

A novel neural approximate inverse control for unknown nonlinear discrete dynamical systems.

A novel neural approximate inverse control is proposed for general unknown single-input-single-output (SISO) and multi-input-multi-output (MIMO) nonlinear discrete dynamical systems. Based on an innovative input/output (I/O) approximation of neural network nonlinear models, the neural inverse control law can be derived directly and its implementation for an unknown process is straightforward. Only a general identification technique is involved in both model development and control design without extra training (online or offline) for the neural nonlinear inverse controller. With less approximation made on controller development, the control will be more robust to large variations in the operating region. The robustness of the stability and the performance of a closed-loop system can be rigorously established even if the nonlinear plant is of not well defined relative degree. Extensive simulations demonstrate the performance of the proposed neural inverse control.     

三级倒立摆的加权变量模糊神经网络控制

为了提高三级倒立摆系统控制的响应速度和稳定性,在设计Mamdani型摸糊推理规则控制器控制倒立摆系统稳定的基础上,设计了一种更有效率的基于Sugeno型模糊推理规则的模糊神经网络控制器。该控制器使用BP神经网络和最小二乘法的混合算法进行参数训练,能够准确归纳输入输出量的模糊隶属度函数和模糊逻辑规则。通过与Mamdani型控制器的仿真对比,表明该Sugeno型模糊神经网络控制器对三级倒立摆系统的控制具有良好的稳定性和快速性,以及较高的控制精度。