电动车用Ni/MH电池组剩余容量的非线性自回归滑动平均预测

准确的蓄电池荷电状态(SOC)决定了电动汽车剩余的行驶里程数.为准确评估电动车用Ni/MH电池组荷电状态(SOC)值,本文提出了一种非线性自回归滑动平均(NARMAX)模型的系统辨识方法.文中使用联邦城市行驶工况(FUDS)的试验数据,采用NARMAX模型线性简化逼近的辨识方法,对蓄电池SOC建立了多输入变量的模型,并使用这个模型进行实时预测;预测结果与试验结果进行了比较.结果表明,该方法是简单、有效的.预测的最大相对误差为1%.     

非线性NARMAX模型的ARMAX模型全局构造

给出一种复杂的、模型未知的非线性系统的全局线性化方法。该方法用时变的ARMAX模型近似描述一个非线性NARMAX模型。讨论了这一线性化方法的有界性并给出了相应线性化构造方法。仿真结果说明了该线性化方法的有效性。     

Computational system identification for Bayesian NARMAX modelling

In this contribution we derive a computational Bayesian approach to NARMAX model identification. The identification algorithm exploits continuing advances in computational processing power to numerically obtain posterior distributions for both model structure and parameters via sampling methods. The main advantage of this approach over other NARMAX identification algorithms is that for the first time model uncertainty is characterised as a byproduct of the identification procedure. The algorithm is based on the reversible jump Markov chain Monte Carlo (RJMCMC) procedure. Key features of the approach are (i) sampling of unselected model terms for testing for inclusion in the model (the birth move), which encourages global searching of the model term space, (ii) sampling of previously selected model terms for testing for exclusion from the model—a naturally incorporated pruning step (the death move), which leads to model parsimony, and (iii) estimation of model and parameter distributions, which are naturally generated in the Bayesian framework. We present a numerical example to demonstrate the algorithm and a comparison with a forward regression method: the results show that the RJMCMC approach is competitive and gives useful additional information regarding uncertainty in both model parameters and structure.     

A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input–output direct feed-through term

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input–output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection.     

IDENTIFICATION AND NON LINEAR POLE PLACEMENT SELF TUNING PID CONTROL OF A JACKETED WELL MIXED REACTOR

This paper describes the application of Non Linear Self Tuning PID (NLSTPID) system with the intention of controlling the temperature of a cooling jacketed polymer reactor containing toluene and styrene mixture. The use of polynomial Nonlinear AutoRegressive Moving Average with eXternal input (NARMAX) model related with tank temperature and heat input for nonlinear control was emphasised. The first part of the paper presents an identification algorithm for the construction of polynomial NARMAX and AutoRegressive Moving Average with external input (ARMAX) models. A Pseudo Random Binary Sequence (P.R.B.S) signal was utilised as a forcing function in order to determine the parameters of the models. Levenberg Marquardt algorithm was used to estimate the relevant parameters of NARMAX model. Similar work was carried out for ARMAX model using Bierman, Kalman and Least Square Estimation algorithms. The time response of the tank temperature obtained from computer simulation, identified models and experimental data to a unit step change in manipulated variable were compared. Next, linear and non linear models were used with STPID algorithm to demonstrate the performance of the available control in response to disturbances. All theoretical works were compared with experimental data.     

An enhanced linear Kalman filter (EnLKF) algorithm for parameter estimation of nonlinear rational models

In this study, an enhanced Kalman Filter formulation for linear in the parameters models with inherent correlated errors is proposed to build up a new framework for nonlinear rational model parameter estimation. The mechanism of linear Kalman filter (LKF) with point data processing is adopted to develop a new recursive algorithm. The novelty of the enhanced linear Kalman filter (EnLKF in short and distinguished from extended Kalman filter (EKF)) is that it is not formulated from the routes of extended Kalman Filters (to approximate nonlinear models by linear approximation around operating points through Taylor expansion) and also it includes LKF as its subset while linear models have no correlated errors in regressor terms. No matter linear or nonlinear models in representing a system from measured data, it is very common to have correlated errors between measurement noise and regression terms, the EnLKF provides a general solution for unbiased model parameter estimation without extra cost to convert model structure. The associated convergence is analysed to provide a quantitative indicator for applications and reference for further research. Three simulated examples are selected to bench-test the performance of the algorithm. In addition, the style of conducting numerical simulation studies provides a user-friendly step by step procedure for the readers/users with interest in their ad hoc applications. It should be noted that this approach is fundamentally different from those using linearisation to approximate nonlinear models and then conduct state/parameter estimate.     

Constrained MIMO dynamic discrete-time modeling exploiting optimal experimental design

This article presents a new multiple input, multiple output (MIMO) constrained discrete-time modeling (DTM) approach for dynamic block-oriented processes that does not require the nonlinear steady state characteristics to be known prior to model development. This approach uses an efficient statistical experimental design to provide design points for sequential step tests. The DTM is developed from this data in two stages. In the first stage, the ultimate response (steady state) model is determined from just the ultimate response data of the sequential step tests. In the second stage, the dynamic parameters are estimated under the constraint of the fitted ultimate response model obtained in the first stage. The constrained formulation is given for MIMO Hammerstein and Wiener block-oriented systems. Comparison of the proposed constrained DTM method is made with unconstrained DTM and constrained continuous-time modeling (CTM). Prediction accuracy of the proposed method is significantly better than unconstrained DTM and comparable to constrained CTM for the process studied.     

APPLICATION OF MULTIVARIABLE NONLINEAR ADAPTIVE GENERIC MODEL CONTROL TO A PACKED DISTILLATION COLUMN

Nonlinear adaptive generic model control and self-tuning PID control systems were applied to control the top and bottom product temperature of a packed distillation column separating methanol-water mixture. In the first control algorithm, an adaptive generic model control (AGMC) structure was proposed for dual temperature control of the system. In the second control algorithm, nonlinear self tuning PID (NLSTPID) control based on pole-placement technique was used to control the same system. For NLSTPID control purposes pseudo random binary sequence (PRBS) signal and recursive identification algorithm were used to estimate the relevant parameters of a polynomial NARMAX model. In this work, real-time application has been carried out. In both dynamic and control studies, perturbations in feed composition were utilized as the disturbance, and the reboiler heat duty and the reflux ratio were selected as the manipulated variables. The control performances have been obtained by using ISE and, in general, AGMC results were better than those of the STPID control algorithm.     

基于NARMAX模型和NOFRF结构损伤检测的实验研究

非线性输出频率响应函数是由Volterra级数发展而来的频域概念,可方便在频域对非线性系统进行分析,它是频率的一维函数.本文主要介绍了利用NARMAX模型以及NOFRF对结构进行损伤检测的方法,并利用实验研究证实了该损伤检测方法的可行性.另外,由于系统非线性特性可用来做结构损伤检测,且具有对系统状态比较敏感的优点,而基于NOFRF的损伤检测方法是利用非线性方法来分析系统的状态,该方法提取出的特征属于非线性特征,所以该损伤检测方法可以用来做结构损伤检测,且具有对系统状态比较敏感的优点.     

非线性NARMAX模型结构与参数一体化辨识的改进算法

本文针对现有辨识算法所存在的缺陷,作了如 下改进工作：提出了一种新的模型选项准则（[ERR]准则）,克服了原[err]准则易导致 冗余项被错误选入的缺陷,保证了模型结构的正确辨识;对原算法做了较大改进,克服了原 算法使用时遭遇的存储困难问题,极大程度地改善了辨识算法的数值稳定性．理论分析及仿 真结果均证明了改进算法的优越性及有效性．