Experimental Investigations on Performance Comparison of EKF and ANN Based Controllers with UKF Based Controller for Autonomous Hybrid Systems with Uncertainties

Vellayikot, Shijoh; Vaidyan, M. V.

doi:10.5370/jeet.2016.11.5.1492

Cited by 2 publications

(2 citation statements)

References 23 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e ISR-UKF avoids the problem of having a negative-definite Cholesky factor [16], making it greater than the traditional unscented Kalman filter [17,18] on computational stability. e rest of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

A Novel Real-Time Center of Gravity Estimation Method for Wheel Loaders with Front/Rear-Axle-Independent Electric Driving

Yang

Wang

Han

2021

Journal of Control Science and Engineering

View full text Add to dashboard Cite

Estimation of the center of gravity (CG) is the basis for intelligent control of the front-and-rear-axis-independent electric driving wheel loaders (FREWLs). This paper presents a novel real-time method for estimating the CG of FREWLs, which is suitable for driving and spading conditions on bumpy roads. A FREWL dynamical model is proposed to set up the state-space model. The CG estimator is used to estimate the longitudinal tire force using the state-space model and the improved square-root unscented Kalman filter (ISR-UKF) algorithm. The simulation and experimental results indicate that this method is suitable for FREWL dynamics and operational characteristics, and the estimated value of CG basically converges to the reference value. Finally, the probable reasons for error occurring in two experiments and the practical challenges of this method are discussed. The research in this paper establishes a partial theoretical basis for intelligent control of construction machinery.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Real-Time Center of Gravity Estimation Method for Wheel Loaders with Front/Rear-Axle-Independent Electric Driving

Yang

Wang

Han

2021

Journal of Control Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Haykin [15] first used Kalman filtering and battery model for parameter estimation in his book Kalman Filtering and Neural Networks. Since then, many scholars have improved and optimized the SOC estimation of Kalman filtering [16]. Ambient temperature has a negative influence on the parameters of the battery equivalent circuit model.…”

mentioning

confidence: 99%

Evaluation of LFP Battery SOC Estimation Using Auxiliary Particle Filter

Liu

et al. 2019

Energies

View full text Add to dashboard Cite

State of charge (SOC) estimation of lithium batteries is one of the most important unresolved problems in the field of electric vehicles. Due to the changeable working environment and numerous interference sources on vehicles, it is more difficult to estimate the SOC of batteries. Particle filter is not restricted by the Gaussian distribution of process noise and observation noise, so it is more suitable for the application of SOC estimation. Three main works are completed in this paper by taken LFP (lithium iron phosphate) battery as the research object. Firstly, the first-order equivalent circuit model is adapted in order to reduce the computational complexity of the algorithm. The accuracy of the model is improved by identifying the parameters of the models under different SOC and minimum quadratic fitting of the identification results. The simulation on MATLAB/Simulink shows that the average voltage error between the model simulation and test data was less than 24.3 mV. Secondly, the standard particle filter algorithm based on SIR (sequential importance resampling) is combined with the battery model on the MATLAB platform, and the estimating formula in recursive form is deduced. The test data show that the error of the standard particle filter algorithm is less than 4% and RMSE (root mean square error) is 0.0254. Thirdly, in order to improve estimation accuracy, the auxiliary particle filter algorithm is developed by redesigning the importance density function. The comparative experimental results of the same condition show that the maximum error can be reduced to less than 3.5% and RMSE is decreased to 0.0163, which shows that the auxiliary particle filter algorithm has higher estimation accuracy.The intrinsic parameter measurement method is to select suitable battery parameters that can directly characterize the SOC for direct measurement, and then estimate the battery SOC through these parameters. The commonly used intrinsic parameters include open circuit voltage (OCV), impedance spectrum [7], electromotive force (EMF), and internal resistance (IR). The advantage of this method is that the battery SOC can be directly obtained through the mapping relationship between the SOC and the battery intrinsic parameter, and the disadvantage is that the estimation accuracy relies excessively on the accuracy of the mapping relationship between SOC and intrinsic parameters of battery. Holger [8] summarized the SOC estimation method based on the impedance spectrum. Waag [9] confirmed that the impedance parameter of a Li-ion battery changes negatively as the battery ages.Computer intelligence methods found in literature mainly include the neural network model, genetic algorithm and particles swarm optimization. The advantage of these methods is that they do not require a thorough understanding of battery internal mechanism, but they do require large amounts of data for the machine learning process. Sheikhan et al. [10] used an artificial neural network to realize feedback correction of the traditional time-integration m...

show abstract

Experimental Investigations on Performance Comparison of EKF and ANN Based Controllers with UKF Based Controller for Autonomous Hybrid Systems with Uncertainties

Cited by 2 publications

References 23 publications

A Novel Real-Time Center of Gravity Estimation Method for Wheel Loaders with Front/Rear-Axle-Independent Electric Driving

A Novel Real-Time Center of Gravity Estimation Method for Wheel Loaders with Front/Rear-Axle-Independent Electric Driving

Evaluation of LFP Battery SOC Estimation Using Auxiliary Particle Filter

Contact Info

Product

Resources

About