机组非线性模型的多变量内模控制

大型火力发电机组的协调控制系统是一个复杂的多变量非线性控制系统。随着科学技术和生产的迅速发展,对复杂和不确定性系统实行自动控制的要求不断提高,为进一步改善控制品质,必须采用先进控制器。内模控制由于具有良好的跟踪性能和抗干扰能力,并对模型失配有一定的鲁棒性,使其在工业控制中获得了广泛的应用。为了克服非线性环节以及能量供需之间关联耦合作用对协调系统控制品质的影响,本文结合反馈线性化方法与多变量内模控制,对机组非线性模型设计出非线性内模控制器。     

基于专家规则的自选择式多模态控制方法的研究

目前模糊控制和PID控制等各种控制器对于非线性过程都各有其不足.文章提出了一种将模糊控制、PID控制和Bang-Bang控制相结合的、以专家规则为选择依据的自选择式多模态控制方法,在理论分析的基础上建立了多模态控制器的控制策略,并以非线性的切削加工过程为对象,进行仿真实验.结果表明,其超调量和调节时间明显优于模糊控制,超调量和控制器自身计算时间显著少于PID控制,而且此方法具有很好的鲁棒性.     

电液位置伺服系统的MPC控制仿真分析

电液位置伺服系统广泛应用在大负载、快速、精确反应的控制领域中,然而其存在时变非线性的特性,因此传统控制系统不能达到理想的控制效果。模型预测控制(Model Predictive Control, MPC)具有无需模型、鲁棒性强、抗扰动能力强等优点,较适合应用在时变非线性系统的控制中。分别采用PID控制、线性MPC、自适应MPC和非线性MPC 4种控制策略对电液位置伺服系统的控制性能进行仿真研究。结果表明：线性MPC、自适应MPC和非线性MPC都比PID控制性能好,非线性MPC控制精度较高、响应速度较快、抗扰动能力较强,自适应MPC控制精度、响应速度和鲁棒性次之。     

电液位置伺服控制系统的研究

电液位置伺服控制系统是一个非线性系统,且伺服阀具有死区非线性特性以及受零偏、泄漏等各种复杂因素的影响。一般采用的常规线性PID控制器难以协调快速性与超调性之间的矛盾。基于LabVIEW平台,针对电液伺服系统的非线性和不确定性等特性研究设计出非线性PID控制器,并与PID控制算法进行比较。实验结果表明,非线性PID控制器中的增益参数能够随控制误差而变化,使控制系统既响应快又无超调现象,抗干扰能力也优于传统的PID控制,改善了系统的性能。     

非线性控制技术在火电机组AGC优化中的应用

针对某电厂11号火力发电机组中的主蒸汽压力控制和AGC(自动发电控制)控制存在的不足,提出了基于非线性PID、非线性微分跟踪器和模糊控制策略相结合的非线性控制策略。工程实际应用结果表明提出的控制策略能够很好地解决机组的协调控制问题。     

磁悬浮控制力矩陀螺高速转子的高精度位置控制

在受到陀螺效应、动框架效应等影响后产生的磁力非线性问题是磁悬浮控制力矩陀螺(MSCMG)高速转子位置精度下降的主要因素。为解决以上问题,提高转子位置精度,本文分析了转子所受磁力的特性,建立了转子系统非线性动力学模型,提出了神经网络滑模控制方法。设计滑模控制律,采用径向基函数神经网络逼近控制律中的非线性模型,自适应算法根据误差在线调整神经网络的权值,同时可以保证整个系统的稳定性。仿真和实验结果表明,所提出方法的转子位置精度达到99%,稳态误差为0.000 2 mm。神经网络滑模控制可以实现MSCMG转子系统的高精度位置控制。     

基于遗传算法的有约束非线性预测控制

将神经网络辨识、遗传算法全局优化和预测控制思想有机结合,提出了一种新型控制器,用于带约束的非线性对象的控制.仿真结果表明此算法在非线性控制方面的优异性能.     

电液伺服系统的非线性控制

针对电液伺服系统非线性、参数时变的特点,为提高系统的性能,首先讨论了系统的非线性数学模型,利用逆系统解耦控制方法,将非线性系统转化成伪线性系统,进行线性控制;在此基础上,提出了一种模糊PID自适应非线性控制设计方案,与逆系统控制方法进行了仿真比较;结果表明,采用模糊PID控制,在系统参数变化、外界扰动的影响下,具有较好的自适应性和动态鲁棒性能。     

非线性油气悬架的动力学与控制研究进展

为了满足市场对汽车平顺性、舒适性等性能的要求，非线性悬架被广泛应用，行业内对非线性悬架系统的研究也从未间断。主要分析了非线性油气悬架在动力学和控制方面的研究进展，特别分析了非线性油气悬架非线性动力学与控制方面的研究；同时对分数阶理论在非线性油气悬架中的应用进行了介绍；分析了非线性油气悬架在动力学分析和控制方面的发展方向。     

电液伺服系统在非线性干扰下的神经网络控制

液压伺服系统除了非线性动力学特性外 ,还包含有不确定参数和不确定的非线性干扰。为解决这一类非系统系统的控制问题 ,提出了PID 复合正交神经网络 (CONN)的并行控制法 ,并对带有非线性干扰的电液位置控制伺服系统作了仿真研究。仿真结果表明 ,CONN实现的前馈控制对给定位置的跟踪具有良好的动态特性 ,对系统的非线性干扰具有较强的鲁棒性。该方法简单、方便 ,具有实际应用价值     

Force control of a piezoelectric actuator based on a statistical system model and dynamic compensation

Piezoelectric actuators present a promising option if actuation of small-sized robot joints is considered. They develop forces of several newtons at velocities in the cm/s range while their dimensions and weight remain small as compared to electric motors. This work presents an approach for modelling a class of contemporary, non-resonant translatory piezoelectric actuators. The proposed modelling approach results in three motor models of increasing complexity independently of the low-level piezoelectric properties of the driving elements. The basic model establishes a static relation between motor velocity and drive frequency for a free-moving motor. The second model is a non-linear extension of the first model which introduces external load forces. The final model introduces time-dependent aspects by employing system identification techniques. The final model is used to develop a force compensation mechanism which restores linearity in the motor operation even in the loaded case. Based on the linearised model, standard control design techniques are applicable to design an explicit force controller. Limits on the performance of the controller are derived. The actual performance of the controller is evaluated both in simulations and experiments by pulling on tendons of different elasticity.     

Model predictive control of non-linear systems over networks with data quantization and packet loss

This paper studies the approach of model predictive control (MPC) for the non-linear systems under networked environment where both data quantization and packet loss may occur. The non-linear controlled plant in the networked control system (NCS) is represented by a Tagaki-Sugeno (T-S) model. The sensed data and control signal are quantized in both links and described as sector bound uncertainties by applying sector bound approach. Then, the quantized data are transmitted in the communication networks and may suffer from the effect of packet losses, which are modeled as Bernoulli process. A fuzzy predictive controller which guarantees the stability of the closed-loop system is obtained by solving a set of linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed method.     

Segment-to-segment contact elements for modelling joint interfaces in finite element analysis

This paper presents an efficient approach to model contact interfaces of joints in finite element analysis (FEA) with segment-to-segment contact elements like thin layer or zero thickness elements. These elements originate from geomechanics and have been applied recently in modal analysis as an efficient way to define the contact stiffness of fixed joints for model updating. A big advantage of these elements is that no global contact search algorithm is employed as used in master–slave contacts. Contact search algorithms are not necessary for modelling contact interfaces of fixed joints since the interfaces are always in contact and restricted to small relative movements, which saves much computing time. We first give an introduction into the theory of segment-to-segment contact elements leading to zero thickness and thin layer elements. As a new application of zero thickness elements, we demonstrate the implementation of a structural contact damping model, derived from a Masing model, as non-linear constitutive laws for the contact element. This damping model takes into account the non-linear influence of frictional microslip in the contact interface of fixed joints. With this model we simulate the non-linear response of a bolted structure. This approach constitutes a new way to simulate multi-degree-of-freedom systems with structural joints and predict modal damping properties.     

Model updating of locally non-linear systems based on multi-harmonic extended constitutive relation error

Improving the fidelity of numerical simulations using available test data is an important activity in the overall process of model verification and validation. While model updating or calibration of linear elastodynamic behaviors has been extensively studied for both academic and industrial applications over the past three decades, methodologies capable of treating non-linear dynamics remain relatively immature. The authors propose a novel strategy for updating an important subclass of non-linear models characterized by globally linear stiffness and damping behaviors in the presence of local non-linear effects. The approach combines two well-known methods for structural dynamic analysis. The first is the multi-harmonic balance (MHB) method for solving the non-linear equations of motion of a mechanical system under periodic excitation. This approach has the advantage of being much faster than time domain integration procedures while allowing a wide range of non-linear effects to be taken into account. The second method is the extended constitutive relation error (ECRE) that has been used in the past for error localization and updating of linear elastodynamic models. The proposed updating strategy will be illustrated using academic examples.     

Modeling,simulation and experimental validation of flow rate of electro-hydraulic hitch control valve of agricultural tractor

Advances in non-linear control theory have made it possible to develop controllers for non-linear dynamic systems in their nature. The low-cost proportional control valves of the hydraulic control system are examples of such a system where there is no linearity due to the structure of the valves and the flaws in the spool. However, the non-linear analysis and regulation of these hydraulic systems cannot be done without a proper valve model. This paper presents a methodology for the development of an efficient unified model of a three-point hitch (TPH) electro-hydraulic proportional control valve control system for agricultural tractors by means of a parameter estimation technique. Modeling and simulation of the proportional control valve was performed using MATLAB Simulink software. Parameter estimation methodology was used to optimize the effective orifice opening of the solenoid valve to meet the flow characteristics available in the manufacturer's technical data sheet for the proportional control valve model parameters. Such unified Simulink models are useful for simulation, practical capability testing, and non-linear control design. Modeling and simulation of electro-hydraulic hitch (EHH) control valve were made and simulation results were compared with actual experimental results. The simulation results of the TPH lifting and lowering time were found to vary 4 and 12.5 %, respectively with the experimental results. This type of parameterized valve models facilitates their implementation in dynamic simulation models of complex hydraulic systems.     

基于多目标遗传算法的混合动力电动汽车控制策略优化

混合动力电动汽车是一个高度复杂的非线性系统,并且影响其控制策略的参数较多,要对这样的系统进行优化,常规的优化算法显得无能为力,模型的精确程度也直接影响了选取参数的可靠性.应用汽车动力性、排放性高级模拟分析软件AVL CRUISE,联合Matlab/Simulink软件,建立合动力电动城市客车整车动态性能仿真分析模型,以百公里油耗和排放指标为优化目标,运用多目标遗传优化算法,针对欧洲、日本及中国的城市公交循环工况对混合动力系统工作模式的选择和能量流的分配进行全局优化,减少了运算时间,获得一组可靠的可行解,精确地确定出控制逻辑参数.该解集在很大程度上同时提高了原车的燃料经济性和排放性能,并且为混合电动车的设计和控制提供了一个适宜的选择范围,设计者可以按不同的要求进行不同的方案选择.     

Identification of multi-degree of freedom non-linear systems using an extended modal space model

The identification of non-linear dynamic systems is an increasingly important area of research, with potential application in many industries. Current non-linear identification methodologies are, in general, mostly suited to small systems with few degrees of freedom (DOF) and few non-linearities. In order to develop a practical identification approach for real engineering structures, the capability of such methods must be significantly extended. In this paper, it is shown that such an extension can be achieved using multi-exciter techniques in order to excite specific modes or DOF of the system under investigation.A novel identification method for large non-linear systems is presented, based on the use of a multi-exciter arrangement using appropriated excitation applied in bursts. This proposed non-linear resonant decay method is applied to a simulated system with 5 DOF and an experimental clamped panel structure. The technique is essentially a derivative of the restoring force surface method and involves a non-linear curve fit performed in modal space. The effectiveness of the resulting reduced order model in representing the non-linear characteristics of the system is demonstrated. The potential of the approach for the identification of large continuous non-linear systems is also discussed.     

Adaptive Control of Neutralization Process using Neural Networks

Abstract

A neural network (NN) based adaptive interaction technique is proposed for controlling highly nonlinear neutralization processes. In this approach, the controller is decomposed into interconnected subsystems and adaptation occurs during the interactions. This approach is adaptive in structure and doesn't use an explicit model of the process in the design. The NN is used to establish the adaptive interaction technique for the development of a nonlinear pH controller, which calculates the necessary change in a manipulated variable to drive the system to the desired value. By applying this adaptive algorithm, the same adaptation as the back‐propagation algorithm is achieved without the need of backward propagating the error throughout a feedback network. This important property makes it possible to adapt the NN controller directly without a process model. This advantage reduces the computational complexity drastically in comparison to the well known back‐propagation algorithm based adaptive NN system and a model based system. The designed model‐free online adaptive controller was implemented to a laboratory scaled pH process in real time by use of a dSPACE 1104 interfacing card. The responses of pH and acid flow rate show good tracking for both the set point and load changes over the entire nonlinear region.     

切削颤振分析与模糊控制实验研究

切削颤振是影响金属切削加工生产率和加工质量的重要因素。建立了金属切削过程的二维颤振数学模型;对颤振的稳定性进行了线性分析和非线性分析;并以颤振位移作为反馈变量,建立了基于二维模糊控制器的颤振控制系统。实验结果表明,该分析与控制对降低加工表面粗糙度具有重要意义。     

A neural network model based predictive control approach: application to a semi-batch reactor

Neural networks can be considered to be new modelling tools in process control and especially in non-linear dynamical systems cases. Their ability to approximate non-linear functions has been very often demonstrated and tested by simulation and experimental studies. In this paper, a predictive control strategy of a semi-batch reactor based on neural network models is proposed. Results of a non-linear control of the reactant temperature of a semi-batch reactor are presented. The process identification is composed of an off-line phase that consists in training the network, and of an on-line phase that corresponds to the neural model adaptation so that it fits any modification of the process dynamics. Experimental results when using this method to control a semi-batch reactor are reported and show the great potential of this strategy in controlling non-linear processes.