State observer-based sliding mode control for semi-active hydro-pneumatic suspension

Ren, Hongbin; Chen, Sizhong; Zhao, Yuzhuang; Liu, Gang; Yang, Lin

doi:10.1080/00423114.2015.1122818

Cited by 47 publications

(27 citation statements)

References 12 publications

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“…Furthermore, a few time-varying parameters of the driver's NMS are difficult to be measured by physical sensors. The UKF observer based on the unscented transform (UT) theory and statistical linearization technique is implemented to observe the primary NMS parameters online, which reflect the evolution of driving skills [29].…”

Section: Posting Processmentioning

confidence: 99%

A Steering-Following Dynamic Model with Driver’s NMS Characteristic for Human-Vehicle Shared Control

Wei

Xing

et al. 2020

Applied Sciences

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For investigating driver characteristic as well as control authority allocation during the process of human–vehicle shared control (HVSC) for an autonomous vehicle (AV), a HVSC dynamic mode with a driver’s neuromuscular (NMS) state parameters was proposed in this paper. It takes into account the driver’s NMS characteristics such as stretch reflection and reflex stiffness. By designing a model predictive control (MPC) controller, the vehicle’s state feedback and driver’s state are incorporated to construct the HVSC dynamic model. For the validation of the model, a field experiment was conducted. The vehicle state signals are collected by V-BOX, and the driver’s state signals are obtained with the electromyography instrument. Subsequently, the hierarchical least square (HLS) parameter identification algorithm was implemented to identify the parameters of the model based on the experimental results. Moreover, the Unscented Kalman Filter (UKF) was utilized to estimate the important NMS parameters which cannot be measured directly. The experimental results showed that the model we proposed has excellent accuracy in characterizing the vehicle’s dynamic state and estimating the driver’s NMS parameter. This paper will serve as a theoretical basis for the new control strategy allocation between human and vehicle for L3 class AVs.

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Section: Posting Processmentioning

confidence: 99%

A Steering-Following Dynamic Model with Driver’s NMS Characteristic for Human-Vehicle Shared Control

Wei

Xing

et al. 2020

Applied Sciences

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“…The UKF methods propagate the statistic through the nonlinear state function by utilizing the sigma‐point approach at each sampling time step. The sigma‐point can be designed by the symmetrically sampling method,

χ_{k} = true{\begin{matrix} lefttrue \\ \overset{true¯}{boldx} k = 0 \\ \begin{matrix} \overset{x}{true} + {(\sqrt{((), L + σ) P^{x}})}_{k} & k = 1, 2, \dots, n, \end{matrix} \\ \begin{matrix} \overset{x}{true} - {(\sqrt{((), L + σ) P^{x}})}_{k - n} & k = n + 1, \dots, 2 n \end{matrix} \end{matrix}

where the sigma points χ k are designed as a set of 2 n +1 vector; x is system state vector in the observation algorithm, the mean of χ k is

\overset{x}{true}

, and the covariance of χ k is P x ; L is the length of the state vector x ; σ is scaling parameter for χ k ; it can be described by the following equation:

σ = α^{2} ((), L + κ) - L,

…”

Section: Ecm‐based Soc Estimationmentioning

confidence: 99%

“…algorithm, the mean of χ k is x, and the covariance of χ k is P x ; L is the length of the state vector x; σ is scaling parameter for χ k ; it can be described by the following equation 24 :…”

Section: Ecm-based Soc Estimationmentioning

confidence: 99%

A comparative study of lumped equivalent circuit models of a lithium battery for state of charge prediction

et al. 2019

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Summary Battery modeling plays an important role in remaining range prediction and battery management system development. An accurate and realistic battery model is essential to design an efficient electric storage system. The goal of this paper is to investigate the performance of different circuit topologies for diffusion process in the equivalent circuit models (ECMs). The theory of diffusion process approximation by using resistive‐capacitor (RC) networks is explained in frequency domain. The terminal voltage predictive capabilities of the ECMs are compared and validated with test data. The numerical simulation results show that model prediction accuracy and computation burdens increase along with the number of RC pairs. The ECM with three RC networks is the best choice in terms of the balance between accuracy and complexity for ternary lithium batteries. In addition, a novel method of combining unscented Kalman filter (UKF) algorithm with initial state of charge (SOC) acceleration convergence for SOC estimation is proposed. The results of urban dynamometer driving schedule (UDDS) show that ECM with three RC networks has the best comprehensive performance on calculation cost and SOC estimation accuracy.

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“…The Kalman filter algorithm generally uses feedback control to estimate process states [35]. The ECAS system is a nonlinear system, hence suitable for the extended Kalman filter (EKF) algorithm and unscented Kalman filter (UKF) algorithm.…”

Section: Unscented Kalman Filter Algorithmmentioning

confidence: 99%

Height Adjustment of Vehicles Based on a Static Equilibrium Position State Observation Algorithm

Gao¹,

Chen²,

Zhao³

et al. 2018

Energies

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In this paper, a static state observer algorithm based on the static equilibrium position is proposed, which can realize accurate control of electric vehicle height adjustment with existing road excitation. The existence of road excitation can lead to deflection variation of the electronically controlled air suspension (ECAS). The use of only dynamic deflection as the reference for the electric vehicle height adjustment will produce great errors. Therefore, this paper provides an observation algorithm, which can realize the accurate control of vehicle height. Firstly, the static equilibrium position equation of suspension is derived according to the theory of hydrodynamics and characteristics of pneumatic chamber. Secondly, a vehicle dynamics model with seven degrees of freedom (7-DOF) is established and the kinetic equations are discretized. Then, the unscented Kalman filter (UKF) algorithm is used to obtain the static equilibrium position of vehicle. According to the vehicle static equilibrium position obtained by UKF, the height of the vehicle is adjusted by using a fuzzy controller. The simulation and experimental results show that this proposed algorithm can realize the control of vehicle height with an accuracy of over 96%, which ensures the excellent driving performance of vehicles under different road conditions.

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State observer-based sliding mode control for semi-active hydro-pneumatic suspension

Cited by 47 publications

References 12 publications

A Steering-Following Dynamic Model with Driver’s NMS Characteristic for Human-Vehicle Shared Control

A Steering-Following Dynamic Model with Driver’s NMS Characteristic for Human-Vehicle Shared Control

A comparative study of lumped equivalent circuit models of a lithium battery for state of charge prediction

Height Adjustment of Vehicles Based on a Static Equilibrium Position State Observation Algorithm

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