Modeling and Recognition of Driving Behavior Based on Stochastic Switched ARX Model

This paper presents the development of the modeling and recognition of human driving behavior based on a stochastic switched autoregressive exogenous (SS-ARX) model. First, a parameter estimation algorithm for the SS-ARX model with multiple measured input-output sequences is developed based on the expectation-maximization algorithm. This can be achieved by extending the parameter estimation technique for the conventional hidden Markov model. Second, the developed parameter estimation algorithm is applied to driving data with the focus being on driver's collision avoidance behavior. The driving data were collected using a driving simulator based on the cave automatic virtual environment, which is a stereoscopic immersive virtual reality system. Then, the parameter set for each driver is obtained, and certain driving characteristics are identified from the viewpoint of switched control mechanism. Finally, the performance of the SS-ARX model as a behavior recognizer is examined. The results show that the SS-ARX model holds remarkable potential to function as a behavior recognizer.     

Filtered multi-innovation-based iterative identification methods for multivariate equation-error ARMA systems

This paper focuses on the parameter estimation issues of multivariate equation-error autoregressive moving average systems. By applying the gradient search and the multi-innovation theory, we derive a multi-innovation gradient based iterative (MI-GI) algorithm. In order to improve the computational efficiency and the parameter estimation accuracy, a filtering and decomposition based gradient iterative (F-D-GI) algorithm is presented by using the data filtering technique and the decomposition technique. The key is to choose an appropriate filter to filter the input-output data and to transform an original system into several subsystems. Compared with the MI-GI algorithm, the F-D-GI algorithm can generate more accurate parameter estimates. Finally, an illustrative example is provided to indicate the effectiveness of the proposed algorithms.     

Filtering-based recursive least squares estimation approaches for multivariate equation-error systems by using the multiinnovation theory

This article researches the filtering-based parameter estimation issues for a class of multivariate control systems with colored noise. A filtering-based recursive generalized extended least squares algorithm is derived, in which the data filtering technique is used for transforming the original system into two subidentification systems and the least squares principle is used for estimating parameters of these two subsystems. Furthermore, in order to improve the parameter estimation accuracy, the multiinnovation theory is added for deducing a filtering-based multiinnovation recursive generalized extended least squares algorithm. The numerical example confirms that these two proposed algorithms are effective.     

Iterative identification methods for a class of bilinear systems by using the particle filtering technique

This article mainly studies the iterative parameter estimation problems of a class of nonlinear systems. Based on the auxiliary model identification idea, this article utilizes the estimated parameters to construct an auxiliary model, and uses its outputs to replace the unknown noise-free process outputs, and develops an auxiliary model least squares-based iterative (AM-LSI) identification algorithm. For further improving the parameter estimation accuracy, we use a particle filter to estimate the unknown noise-free process outputs, and derive a particle filtering least squares-based iterative (PF-LSI) identification algorithm. During each iteration, the AM-LSI and PF-LSI algorithms can make full use of the measured input–output data. The simulation results indicate that the proposed algorithms are effective for identifying the nonlinear systems, and can generate more accurate parameter estimates than the auxiliary model-based recursive least squares algorithm.     

Maximum likelihood-based adaptive differential evolution identification algorithm for multivariable systems in the state-space form

Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.     

Iterative parameter and order identification for fractional-order nonlinear finite impulse response systems using the key term separation

This article considers the parameter estimation for a fractional-order nonlinear finite impulse response system with colored noise. For the fractional-order systems, the challenge and difficulty are to identify the order and parameters of the systems simultaneously under colored noise disturbances. In order to reduce the problem of redundant parameter estimation, the output form of the system can be expressed by a linear combination of unknown parameters through the separation of the key term separation. A key term separation auxiliary model gradient-based iterative algorithm is derived by using the negative gradient search. Meanwhile, to achieve the higher estimation accuracy, we propose a key term separation auxiliary model multiinnovation gradient-based iterative algorithm by utilizing the multiinnovation theory. Finally, the simulation results test the effectiveness of the proposed algorithms.     

基于暂态分析和系统辨识的光伏并网逆变器建模

建立并网光伏发电系统的模型是研究其与电网交互过程的基础.光伏系统的暂态特性不仅受天气情况、电网工况等环境因素的影响,还取决于商用逆变器的内部结构以及控制策略,然而它们并不对外公开,这给系统的分析带来了困难.为了解决这个问题,该文提出一种基于暂态分析和系统辨识的光伏并网逆变器通用建模方法.将光伏逆变器视作内部结构以及控制算法未知的黑箱,通过先后施加扰动和测取响应并结合系统辨识的方法来建立其模型.以一个3kW的商用光伏逆变器为研究对象,搭建硬件在环实验系统来研究并验证这一建模方法.结果显示,在多种扰动对应的暂态过程中,模型的输出均准确地拟合了逆变器的实际输出,证明了这一建模方法的有效性.     

静态无功补偿器鲁棒控制的一种新自适应逆推方法

针对一般的参数反馈型非线性系统提出了一种扩展自适应逆推方法。该方法不仅保留系统的非线性特性和对未知参数的实时在线估计,而且突破了经典的确定性等价性原理。将该方法应用到含有未知参数带有静态无功补偿器 (SVC)的单机无穷大系统。将这种新自适应机制引入电力系统,得到了带有SVC单机无穷大系统的自适应控制律。仿真结果表明,该方法在提高系统稳定性和参数估计方面优于传统的逆推方法,为工程应用提供了一种有效的选择。另外, 该文中的算法可以应用到其他控制系统。     

Combined estimation of the parameters and states for a multivariable state-space system in presence of colored noise

This article addresses the combined estimation issues of parameters and states for multivariable systems in the state-space form disturbed by colored noises. By utilizing the Kalman filtering principle and the coupling identification concept, we derive a Kalman filtering based partially coupled recursive generalized extended least squares (KF-PC-RGELS) algorithm to jointly estimate the parameters and the states. Using the past and the current data in parameter estimation, we propose a Kalman filtering based multi-innovation partially coupled recursive generalized extended least-squares algorithm to enhance the parameter estimation accuracy of the KF-PC-RGELS algorithm. Finally, a simulation example is provided to test and compare the performance of the proposed algorithms.     

A new model reference adaptive control for linear time‐varying systems

Recent results on the adaptive control of linear time‐varying systems have considered mostly the case in which the range or rate of parameter variations is small. In this paper, a new state feed‐back model reference adaptive control is developed for systems with bounded arbitrary parameter variations. The important feature of the proposed adaptive control is an uncertainty estimation algorithm, which guarantees almost zero tracking error. Note that the conventional parameter estimation algorithm in the adaptive control guarantees only bounded tracking error. Copyright © 2000 John Wiley & Sons, Ltd.     

An indirect adaptive pole‐placement control for MIMO discrete‐time stochastic systems

In this paper, an indirect adaptive pole‐placement control scheme for multi‐input multi‐output (MIMO) discrete‐time stochastic systems is developed. This control scheme combines a recursive least squares (RLS) estimation algorithm with pole‐placement control design to produce a control law with self‐tuning capability. A parametric model with a priori prediction outputs is adopted for modelling the controlled system. Then, a RLS estimation algorithm which applies the a posteriori prediction errors is employed to identify the parameters of the model. It is shown that the implementation of the estimation algorithm including a time‐varying inverse logarithm step size mechanism has an almost sure convergence. Further, an equivalent stochastic closed‐loop system is used here for constructing near supermartingales, allowing that the proposed control scheme facilitates the establishment of the adaptive pole‐placement control and prevents the closed‐loop control system from occurring unstable pole‐zero cancellation. An analysis is provided that this control scheme guarantees parameter estimation convergence and system stability in the mean squares sense almost surely. Simulation studies are also presented to validate the theoretical findings. Copyright © 2005 John Wiley & Sons, Ltd.     

Least‐squares parameter estimation for systems with irregularly missing data

This paper considers the problems of parameter identification and output estimation with possibly irregularly missing output data, using output error models. By means of an auxiliary model (or reference model) approach, we present a recursive least‐squares algorithm to estimate the parameters of missing data systems, and establish convergence properties for the parameter and missing output estimation in the stochastic framework. The basic idea is to replace the unmeasurable inner variables with the output of an auxiliary model. Finally, we test the effectiveness of the algorithm with an example system. Copyright © 2009 John Wiley & Sons, Ltd.     

The design of a block-regularized parameter estimator by algorithmic engineering

Hierarchical signal flow graphs (HSFGs) are used to illustrate the computations and data flow required for the block-regularized parameter estimation algorithm. Block regularization protects the underlying recursive least squares (RLS) parameter estimation from numerical difficulties which can occur if the input data are not persistently exciting or the behaviour of the underlying model is unknown. Hierarchical signal flow graphs provide a very concise representation of the algorithm and a relatively simple approach to the design of efficient parallel architectures. The design of a two-dimensional systolic array is demonstrated in the paper. © 1997 John Wiley & Sons, Ltd.     

Adaptive non-linear speed control of induction motors

In this paper we present two adaptive non-linear speed control algorithms for induction motors. They employ input-output linearization techniques for the motor model in the stator fixed reference frame. The first control algorithm directly tracks speed and rotor flux. The second is designed using torque and rotor flux tracking and is extended for speed control. A key point is that this algorithm ensures exact current limitation in the known parameter case. The adaptation based on Lyapunov design can cope with rotor and stator resistance variations. Significant simulations using sampled controllers are presented emphasizing adaptation and current limitation effects.     

永磁直线电机的无模型学习自适应控制

将单入单出非线性离散时间系统的非参数模型学习自适应控制方法应用在永磁直线电机的速度和位置控制中。设计是无模型的,是直接基于称为伪偏导数的向量,此伪偏导数是通过一种新型参数估计算法,根据系统的输入输出信息在线导出的。仿真实验证明了该方法对具有不确知动态的非线性系统的有效性和稳定性。     

基于ABC-RFEKF算法的锂电池SOC估计

准确、可靠的荷电状态(SOC)估计可以为电池管理系统的安全高效使用提供保障。针对锂电池SOC估计精度不足的问题,提出人工蜂群算法(ABC)和随机森林优化EKF算法(RFEKF)分别实现电池模型的参数辨识和SOC估计。在建立双极化模型的基础上,为解决在线辨识初始误差累积的问题,采用ABC算法搜索最小模型电压误差下的全局最优阻抗参数值,实现模型参数的精确辨识。在获得精确的模型参数基础上,使用随机森林(RF)对SOC后验估计误差进行在线补偿,达到弥补传统EKF算法高阶项误差的目的,进而实现SOC高精度估计。联合半实物仿真系统和电池测试平台,在EPA城市动力工况下对SOC估计算法实现快速控制原型验证。结果表明：基于ABC-RFEKF的锂电池SOC估计算法各项误差指标均低于传统SOC估计算法,平均误差在1%左右,满足实际工程需求。     

基于广域量测数据和导纳参数在线辨识的受扰轨迹预测

基于相量测量单元(PMU)广域量测数据应用参数辨识理论提出电力系统故障后导纳参数在线辨识方法，研究了电力系统受扰轨迹快速积分预测新方法。该算法基于实时量测量精确构造实际故障后的系统动态方程，并快速积分求解系统受扰轨迹。其突出优点在于：参数辨识使用系统实时数据，可考虑复杂的连锁故障事件和不确定的系统拓扑和参数；预测基于故障后系统模型积分，能够反映系统物理本质，对于系统经典模型具有理想预测精度。该算法在多种测试系统中进行了数字仿真实验，并用动模实验数据进行了离线验证，结果表明算法合理有效。     

Separable multi-innovation stochastic gradient estimation algorithm for the nonlinear dynamic responses of systems

This article is concerned with the parameter identification problem of nonlinear dynamic responses for the linear time-invariant system by means of an impulse excitation signal and discrete observation data. Using the impulse signal as the input, the impulse response experiment is carried out and the dynamical moving sampling is designed to generate the measured data for deriving new identification algorithms. By applying the moving window data that contain the dynamical information of the system to be identified, an objective function with respect to the parameters of the systems is constructed according to the impulse response. In accordance with different functional relations between the system parameters and the system output response, the unknown parameter vector of the system is separated into a linear parameter vector and a nonlinear parameter vector. Based on the separated parameter vectors, two subidentification models are constructed and a separable identification algorithm is presented through the gradient search to improve the accuracy. Moreover, for the purpose of enhancing the estimation accuracy and capturing the dynamical feature of the systems, the moving window data are employed to develop the separable identification algorithm. The performance of the proposed separable identification method is illustrated via a numerical example.     

Parameter estimation for coupled multivariable error models

An adaptive algorithm is developed for estimating the parameters of a linear multivariable error model with coupled dynamics, using estimation errors for coupling inputs. Coupled dynamics in an error equation lead to a new type of error models which have different regressors but the same parameter error. A total cost function is used to derive a desired adaptive law for updating the parameter estimates. As an application, this algorithm is employed for control and identification of multivariable systems with actuator uncertainties. Copyright © 1999 John Wiley & Sons, Ltd.     

Modeling and distributed adaptive fault-tolerant vibration control for bridge beam with single-parameter adaptive neural network

Modeling and vibration control of a bridge beam system are considered in this article. The beam bridge with both ends fixed can be regarded as an Euler-Bernoulli beam, which is a typical distributed parameter system. First, the partial differential equations (PDE) model of the bridge was established according to the Hamilton principle. Then, a reasonable distributed control law was designed on the PDE model to eliminate the elastic deformation and suppress the vibration of the bridge. At the same time, uncertainties related to system status were considered during the design of the closed-loop system. In addition, the possible actuator and sensor faults in the control system were analyzed. Single-parameter adaptive neural networks were used to estimate the effects of coupling terms for uncertainties and faults. The parameter estimation adaptive law was designed to replace the adjustment of neural network weights, which simplifies the algorithm and facilitates practical engineering applications. Finally, the feasibility of the control system was verified by simulation.