As to the nonlinear and time-varying problems of the energy consumption model, this paper proposes an adaptive hybrid modeling method. Firstly, the recursive least squares algorithm with adaptive forgetting factor bas...
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As to the nonlinear and time-varying problems of the energy consumption model, this paper proposes an adaptive hybrid modeling method. Firstly, the recursive least squares algorithm with adaptive forgetting factor based on fuzzy algorithm and recursive least squares algorithm is used to identify the simplified mechanism energy consumption model, which solves the data saturation phenomenon and the weights of the "old and new" data during the online identification process and guarantees the adaptability of the mechanism model. Secondly, because there is a deviation between the identified model and the simplified mechanism energy consumption model, the deviation compensation model of mechanism model is established through kernel partial leastsquaresalgorithm and the model updating strategy with sliding window, which is used to update the deviation compensation model, and then the adaptive hybrid model is established by combining with the mechanism model identified online and updated deviation compensation model. Finally, the effectiveness, generalization and adaptability of the model are verified by the actual operating data of a single working condition and variable working conditions. And comparing with the mechanism model and the data model, The comparison results show that the adaptive hybrid model has higher calculation accuracy with adaptation.
A new adaptive control algorithm for unknown nonlinear plants is presented. The paper first describes a modified neural network(MNN) as well as the associated learning algorithm. The learning algorithm converges consi...
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A new adaptive control algorithm for unknown nonlinear plants is presented. The paper first describes a modified neural network(MNN) as well as the associated learning algorithm. The learning algorithm converges considerably faster because of the introduction of recursiveleastsquares(RLS) algorithm. And then designs adaptive pole placement controller based on the modified neural network. Simulation results show that the proposed control algorithm can effectively control a class of highly nonlinear plants.
作者：
Li, NaLi, Shi-huaSoutheast Univ
Sch Automat Key Lab Measurement & Control Complex Syst Engn Minist Educ Nanjing 210096 Jiangsu Peoples R China
In actual servo system, the transmission mechanism is not an ideal rigid body, and mechanical resonance easily occurs. In this paper, the transmission mechanism of servo system is equivalent to a torsion spring, and t...
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ISBN:
(纸本)9781479970162
In actual servo system, the transmission mechanism is not an ideal rigid body, and mechanical resonance easily occurs. In this paper, the transmission mechanism of servo system is equivalent to a torsion spring, and the entire flexible connection servo system is simplified into a motor-spring-load two-mass system, then the mathematical model of the flexible connection servo system is established through theoretical analysis. Since the mechanical resonant frequency of the system will change with time and environment, the parameters of notch filter must be adjusted online with the resonant frequency. In this paper, according to the application requirements of mechanical resonance online suppressing, the mechanical resonance online suppressing algorithm based on adaptive IIR notch filter is proposed. Using a recursive least squares algorithm, resonant frequency of the system is online estimated, and then the frequency parameters of notch filter are adjusted online. The simulation results show the effectiveness of this method.
Models play a crucial role in explaining internal processes, estimating states, and managing lithium-ion batteries. Electrochemical models can effectively illustrate the battery's mechanism;however, their complexi...
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Models play a crucial role in explaining internal processes, estimating states, and managing lithium-ion batteries. Electrochemical models can effectively illustrate the battery's mechanism;however, their complexity renders them unsuitable for onboard use in electric vehicles. On the other hand, equivalent circuit models (ECMs) utilize a simple set of circuit elements to simulate voltage-current characteristics. This approach is less complex and easier to implement. However, most ECMs do not currently account for the nonlinear impact of operating conditions on battery impedance, making it difficult to obtain accurate wideband impedance characteristics of the battery when used in online applications. This article delves into the intrinsic mechanism of batteries and discusses the influence of nonstationary conditions on impedance. An ECM designed for non-steady state conditions is presented. Online adaptive adjustment of model parameters is achieved using the forgetting factor recursiveleastsquares (FFRLS) algorithm and varied parameters approach (VPA) algorithm. Experimental results demonstrate the impressive performance of the model and parameter identification method, enabling the accurate acquisition of online impedance.
This paper deals with the problem of identification of fractional-order Wiener systems. This model consists of a linear fractional-order state space system in series with a static nonlinear block. This problem poses d...
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ISBN:
(数字)9781728135960
ISBN:
(纸本)9781728135977
This paper deals with the problem of identification of fractional-order Wiener systems. This model consists of a linear fractional-order state space system in series with a static nonlinear block. This problem poses different difficulties, because, it consists of estimating the system parameters and the fractional order. In this work, a new identification algorithm is performed: firstly, the parameters of both the linear and the nonlinear subsystems are estimated based on the leastsquaresalgorithm and the states are updated based on the auxiliary model principle using the estimated parameters, then, the system order will be estimated using the Levenberg-Marquardt algorithm. Finally, a numerical simulation is offered in the extent to evaluate the effectiveness of the presented method.
This paper studies an algorithm of discrete wavelet transform domain adaptive *** first,the received signals through the channel are transformed in wavelet domain,then the least Mean squares(LMS) algorithm is used to ...
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This paper studies an algorithm of discrete wavelet transform domain adaptive *** first,the received signals through the channel are transformed in wavelet domain,then the least Mean squares(LMS) algorithm is used to complete the linear equalization in the same *** simulation results show us that the wavelet transform domain adaptive equalization algorithm may offer better performance and higher convergence rate than the standard LMS linear algorithm.
This paper discuss model and algorithm of confirming standard value of communication equipment indected,including confirming methods of two or more detection data,at last we attempt to use leastsquaresalgorithm and ...
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This paper discuss model and algorithm of confirming standard value of communication equipment indected,including confirming methods of two or more detection data,at last we attempt to use leastsquaresalgorithm and recursive least squares algorithm to confirm detecion standard value,this have definite action for advancing omputation precision and reducing operation load,analogue compution indiacate this model and algorithm have comparative practicability
This article presents a new methodology for estimation of vehicle's vertical forces in order to enhance road safety. Direct measurement of vertical forces requires a complex and expensive experimental set-up, whic...
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ISBN:
(纸本)9781467365970
This article presents a new methodology for estimation of vehicle's vertical forces in order to enhance road safety. Direct measurement of vertical forces requires a complex and expensive experimental set-up, which is not acceptable for ordinary passenger cars. The main contribution of this article is providing a reliable estimator of vertical tire forces by using currently available low-cost sensors. The first advantage of the proposed method is that we modified the vehicle model to take into account the roll and pitch dynamics, which makes our estimator stay robust during sharp turning or at inclined road. The other advantage is that we proposed a process to identify the vehicle parameters, instead of regarding them as known constants. This could enable our estimator to stay reliable even when the parameters are wrongly configured. The parameter identification process is based on recursiveleastsquares (RLS) algorithm. The state observers are based on Kalman filter. The estimation process is applied and compared to real experimental data obtained in real conditions. Experimental results validate and prove the feasibility of this approach.
In this paper,a new blind adaptive equalization algorithm under noisy environment is *** consider a practical case where the noise of each transmission channel is *** oversampling the channel output at twice the symbo...
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In this paper,a new blind adaptive equalization algorithm under noisy environment is *** consider a practical case where the noise of each transmission channel is *** oversampling the channel output at twice the symbol rate,a single-input double-output channel can be *** apply the recursive-least-squares（RLS） to tackle the blind equalization *** the noise-induced bias,RLS algorithm is *** order to eliminate the bias,we present a bias-compensated RLS（BCRLS） algorithm that can estimate the unknown additive noise online and the noise-induced bias can be therefore *** unbiased estimate of the channel characteristics obtained can be used for channel *** results are presented to demonstrate the performance of the proposed algorithm.
This paper proposes a variable forgetting factor QRD-based recursive least squares algorithm with bias compensation (VFF-QR-RLS-BC) for system identification with input noise. The new algorithm is based on the least s...
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ISBN:
(纸本)9781479953424
This paper proposes a variable forgetting factor QRD-based recursive least squares algorithm with bias compensation (VFF-QR-RLS-BC) for system identification with input noise. The new algorithm is based on the least square estimation with bias compensation framework and it employs a variable forgetting factor to improve the tracking speed and a QRD-based implementation for recursively solving the LS problem with bias compensation. Simulation results show that the proposed method can obtain improved convergence rate in sudden system change environment and satisfactory performance under stationary environment.
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