Abstract
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.
Highlights
Intelligent control is the main development trend for wheel loaders [1,2,3]
Because the operational characteristics of wheel loaders have obvious volatility and periodicity in the spading condition on a bumpy road, the center of gravity (CG) estimation model based on the dynamic model of the wheel loader is strongly nonlinear, and the Kalman filter and extended Kalman filter (EKF) are weak in dealing with strongly nonlinear problems, making it difficult to find realtime values under drastic changes in the operation stage
Driving Conditions on a Bumpy Road. e longitudinal speed, CG longitudinal location, and CG height simulation results are shown, respectively, in Figures 4(a)–4(c) when the front-and-rear-axis-independent electric driving wheel loaders (FREWLs) is driving on a bumpy road
Summary
Intelligent control is the main development trend for wheel loaders [1,2,3]. A method of estimating the center of gravity (CG) is a core technique for intelligent control of the longitudinal motion of machinery. To avoid lateral/yaw/roll excitations, Huang and Wang [11] estimated the real-time CG position for lightweight vehicles based on a combined adaptive Kalman filter-extended Kalman filter approach. Due to the limitations of Kalman filter methods, recursive least-squares methods, and extended Kalman filter (EKF) methods, their ability to estimate strongly nonlinear problems is weak so that it is difficult to apply them to wheel loaders in complex operating conditions. Because the operational characteristics of wheel loaders have obvious volatility and periodicity in the spading condition on a bumpy road, the CG estimation model based on the dynamic model of the wheel loader is strongly nonlinear, and the Kalman filter and EKF are weak in dealing with strongly nonlinear problems, making it difficult to find realtime values under drastic changes in the operation stage. Is paper proposes a novel CG estimation method for front/rear-axle-independent electric driving wheel loaders (FREWLs). E subsequent section uses the superscript “^” to denote estimated parameters
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