Recursive Extended Least Research Articles

A Novel Multi-Constraint Peak Power Prediction Method Combined with Online Model Parameter Identification and State-of-Charge Co-Estimation of Lithium-Ion Batteries

The accuracy of the peak power is influenced by the accurate battery model, the results of the parameter identification, and the state of charge (SOC). First, to accurately predict the peak power of lithium-ion batteries, this paper proposes an improved Thevenin model to describe the operating state of lithium-ion batteries by introducing model noise into the Thevenin model. Second, to achieve accurate online parameter identification, a Forgetting Factor Recursive Extended Least Squares (FFRELS) method is proposed to identify the parameters of the improved model. To optimize the effect of noise on SOC estimation, an improved adaptive extended Kalman filtering (AEKF) algorithm is proposed. Finally, to obtain higher accuracy of peak power estimation, a multi-constrained peak power prediction method based on state-recursive estimation is used in this paper. Experimental results show that the maximum error of the FFRELS algorithm under different working conditions is 34.35 mV, and the SOC estimation error of the improved AEKF algorithm is less than 0.53%. The improved multi-constraint peak power estimation algorithm has high estimation accuracy under two complex working conditions, and can accurately predict the power input and output capability of the battery.

A Wave Peak Frequency Tracking Method Based on Two-Stage Recursive Extended Least Squares Identification Algorithm

This paper proposes the wave peak frequency tracking methods based on the least squares identification algorithm. The wave disturbance model is transformed into an autoregressive moving average (ARMA) model and a recursive extended least squares (RELS) algorithm is derived to identify the model parameters by using the auxiliary model identification idea. Furthermore, a two-stage recursive extended least squares (2S-RELS) algorithm is presented to improve the convergence speed by using the hierarchical identification principle. A ship heading control system with the wave peak frequency tracker is built to evaluate the effectiveness of the proposed algorithms. Finally, simulation results show that the proposed algorithms can estimate the wave peak frequency accurately and the 2S-RELS algorithm can improve the convergence speed effectively.

Joint Multi-innovation Recursive Extended Least Squares Parameter and State Estimation for a Class of State-space Systems

The relationship between the parameters and the states of state-space systems is nonlinear, which makes the identification problems of state-space systems complicated. This paper considers the joint parameter and state estimation issues for a class of state-space systems in the observer canonical form with the process noises and the observation noises. By means of the least squares principle and the Kalman filtering, we derive a Kalman filtering based recursive extended least squares algorithm. For purpose of achieving the higher estimation accuracy, a Kalman filtering based multi-innovation recursive extended least squares algorithm is proposed by utilizing a range of available data and more information at each recursion. Finally, the effectiveness of the proposed algorithms is validated through a simulation example.

Improved Discrete Techniques of Time‐Delay and Order Estimation for Large‐Scale Interconnected Nonlinear Systems

The selection of a suitable model structure is an essential step in system modeling. Model structure is defined by determining the class, the time delay, and the model order. This paper proposes improved structural estimation procedures for large‐scale interconnected nonlinear systems which are composed of a set of interconnected Single‐Input Single‐Output (SISO) Hammerstein structures and described by discrete‐time stochastic models with unknown time‐varying parameters. An extensive Determinant Report (DR) algorithm is developed to determine the order of the process. An improved discrete‐time technique based on Recursive Extended Least Squares with Varying Time (RELSVT) delay method is proposed to estimate the time delays of the considered system. The developed theoretical analysis and simulation results prove the validity and performance of the proposed algorithms.

Prediction of Network Utilization Based on ARMA Model and RELS

This paper focused on predictive algorithm of network utilization for networked control system (NCS). Auto-Regressive and Moving Average (ARMA) model was presented for general network utilization, which with fixed constant and known white noise. ARMA model parameters are estimated using parameter estimation algorithm of Recursive Extended Least Squares (RELS). Finally, a simulation example was given to realize RELS of ARMA model. Predictive output of network utilization can be obtained and converge to real state.

Assessment of Model Predictive and Adaptive Glucose Control Strategies for People with Type 1 Diabetes

This paper addresses overnight blood glucose stabilization in people with type 1 diabetes using a Model Predictive Controller (MPC). We use a control strategy based on an adaptive ARMAX model in which we use a Recursive Extended Least Squares (RELS) method to estimate parameters of the stochastic part. We compare this model structure with an autoregressive integrated moving average with exogenous input (ARIMAX) structure, and with an autoregressive moving average with exogenous input (ARMAX) model, i.e. without an integrator. Additionally, safety layers improve the controller robustness and reduce the risk of hypoglycemia. We test our control strategies on a virtual clinic of 100 randomly generated patients with a representative inter-subject variability. This virtual clinic is based on the Hovorka model. We consider the case where only half of the meal bolus is administered at mealtime, and the case where the insulin sensitivity varies during the night. The simulation results demonstrate that the adaptive control strategy can reduce the risks of hypoglycemia and hyperglycemia during the night.

Identification Method for Multichannel Multisensor ARMA Signal with Colored Measurement Noises

For the multisensor multichannel ARMA signal with ARMA colored measurement noises and unknown model parameters and noise variances, this paper presents a kind of multi-stage identification method. At the first stage, the on-line information fusion estimator for the unknown model parameters is presented based on the Recursive Instrumental Variable (RIV) algorithm and the Recursive Extended Least Squares (RELS) algorithm, which is realized by computing the average of local estimators for model parameter. At the second stage, the on-line information fusion estimator for the unknown variances is obtained using the correlation method, which is realized by computing the average of the local estimators for noise variances. At the third stage, the information fusion parameter estimator of MA model is presented using the correlation method and the dead zone Gevers-Wouters and LS algorithms.

Recursive identification of Hammerstein ARMAX model with general discontinuous nonlinearity

This paper deals with the recursive parameter identification of the Hammerstein ARMAX (HARMAX) model with General model of discontinuous and asymmetric Nonlinearity (GNL) containing hysteresis, saturation, preload and dead-zone. This model contains different parameters the choice of which may generate several different nonlinearities. The linear and nonlinear structures are known, the linear block parameters are unknown. However the nonlinear block contains nine parameters only one of them is known. The estimation of the nonlinearity parameters, the internal variables relative to the nonlinearity as well as the linear model parameters are ensured by Recursive Extended Least Squares algorithm (RELS). The provided model is validated by simulation results. Key words: Identification, discontinuous general nonlinearity, HARMAX model, parameter estimation, RELS algorithm.

Modelling and Validation of Automotive Engine Fuelled with Palm Oil Biodiesel

This paper presents the Black-box modelling of automotive diesel engine fuelled with palm oil biodiesel (Palm Oil Methyl Ester). The aim of the work described in this paper is to obtain linear dynamic model of the palm oil biodiesel from test data. Assuming a discrete time form for the system model, an Autoregressive Moving Average with eXogenous input (ARMAX) model structures was selected in this work. A Pseudo-Random Binary Sequence (PRBS) with maximum lengths sequence of 31 has been used as the test input signal to the engine at the speed range around 2100 rpm. The input and output signals were interfaced to the plant via Matlab programming. Recursive estimation algorithm, Recursive Extended Least Squares (RELS), is used to estimate the model parameters. Finally, model validation was done by comparing the output predicted by the model against the measured output and also by error analysis. The mathematical model developed in this study present an analysis and simulation tools of engine dynamic system that forms the foundation for a systematic approach to the analysis, simulation and synthesis of the automotive palm oil biodiesel engine control systems. The model derived in this work is intended for the development of self-tuning engine speed controller in future work. Index Terms—Identification; Modeling; Biodiesel; Palm oil biodiesel