State Estimation of Drive-by-Wire Chassis Vehicle Based on Dual Unscented Particle Filter Algorithm

Accurate vehicle dynamic information plays an important role in vehicle driving safety.However,due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles,the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation.This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm,which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle.In the dual unscented particle filter algorithm,two unscented particle filter transfer information to each other,observe the vehicle state information and the tire force parameter information of the four wheels respectively,which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving.The performance with the dual unscented particle filter algorithm,which is analyzed in terms of the time-average square error,is superior of the unscented Kalman filter algorithm.The effectiveness of the algorithm is further verified by driving simulator test.In this paper,a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.

Combined Estimation of Vehicle Dynamic State and Inertial Parameter for Electric Vehicles Based on Dual Central Difference Kalman Filter Method

Distributed drive electric vehicles(DDEVs)possess great advantages in the viewpoint of fuel consumption,environment protection and traffic mobility.Whereas the effects of inertial parameter variation in DDEV control system become much more pronounced due to the drastic reduction of vehicle weights and body size,and inertial parameter has seldom been tackled and systematically estimated.This paper presents a dual central difference Kalman filter(DCDKF)where two Kalman filters run in parallel to simultaneously estimate vehicle different dynamic states and inertial parameters,such as vehicle sideslip angle,vehicle mass,vehicle yaw moment of inertia,the distance from the front axle to centre of gravity.The proposed estimation method only integrates and utilizes real-time measurements of hub torque information and other in-vehicle sensors from standard DDEVs.The four-wheel nonlinear vehicle dynamics estimation model considering payload variations,Pacejka tire model,wheel and motor dynamics model is developed,the observability of the DCDKF observer is analysed and derived via Lie derivative and differential geometry theory.To address system nonlinearities in vehicle dynamics estimation,the DCDKF and dual extended Kalman filter(DEKF)are also investigated and compared.Simulation with various maneuvers are carried out to verify the effectiveness of the proposed method using Matlab/Simulink-CarsimR.The results show that the proposed DCDKF method can effectively estimate vehicle dynamic states and inertial parameters despite the existence of payload variations and variable driving conditions.This research provides a boot-strapping procedure which can performs optimal estimation to estimate simultaneously vehicle system state and inertial parameter with high accuracy and real-time ability.

Dual Extended Kalman Filter for Combined Estimation of Vehicle State and Road Friction

Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, many estimation methods have been put forward to solve such problems, in which Kalman filter becomes one of the most popular techniques. Nevertheless, the use of complicated model always leads to poor real-time estimation while the role of road friction coefficient is often ignored. For the purpose of enhancing the real time performance of the algorithm and pursuing precise estimation of vehicle states, a model-based estimator is proposed to conduct combined estimation of vehicle states and road friction coefficients. The estimator is designed based on a three-DOF vehicle model coupled with the Highway Safety Research Institute（HSRI） tire model; the dual extended Kalman filter （DEKF） technique is employed, which can be regarded as two extended Kalman filters operating and communicating simultaneously. Effectiveness of the estimation is firstly examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under three typical road adhesion conditions（high-friction, low-friction, and joint-friction）. On this basis, driving simulator experiments are carried out to further investigate the practical application of the estimator. Numerical results from CarSim and driving simulator both demonstrate that the estimator designed is capable of estimating the vehicle states and road friction coefficient with reasonable accuracy. The DEKF-based estimator proposed provides the essential information for the vehicle active control system with low expense and decent precision, and offers the possibility of real car application in future.

Vehicle State and Parameter Estimation Based on Dual Unscented Particle Filter Algorithm

Acquisition of real-time and accurate vehicle state and parameter information is critical to the research of vehicle dynamic control system.By studying the defects of the former Kalman filter based estimation method,a new estimating method is proposed.First the nonlinear vehicle dynamics system,containing inaccurate model parameters and constant noise,is established.Then a dual unscented particle filter(DUPF)algorithm is proposed.In the algorithm two unscented particle filters run in parallel,states estimation and parameters estimation update each other.The results of simulation and vehicle ground testing indicate that the DUPF algorithm has higher state estimation accuracy than unscented Kalman filter(UKF)and dual extended Kalman filter(DEKF),and it also has good capability to revise model parameters.

A dual channel perturbation particle filter algorithm based on GPU acceleration

The particle filter（PF） algorithm is one of the most commonly used algorithms for maneuvering target tracking. The traditional PF maps from multi-dimensional information to onedimensional information during particle weight calculation, and the incorrect transmission of information leads to the fact that the particle prediction information does not match the weight information, and its essence is the reduction of the information entropy of the useful information. To solve this problem, a dual channel independent filtering method is proposed based on the idea of equalization mapping. Firstly, the particle prediction performance is described by particle manipulations of different dimensions, and the accuracy of particle prediction is improved. The improvement of particle degradation of this algorithm is analyzed in the aspects of particle weight and effective particle number. Secondly, according to the problem of lack of particle samples, the new particles are generated based on the filtering results, and the particle diversity is increased. Finally, the introduction of the graphics processing unit（GPU） parallel computing the platform, the “channel-level” and “particlelevel” parallel computing the program are designed to accelerate the algorithm. The simulation results show that the algorithm has the advantages of better filtering precision, higher particle efficiency and faster calculation speed compared with the traditional algorithm of the CPU platform.

Rudder Roll Damping Autopilot Using Dual Extended Kalman Filter–Trained Neural Networks for Ships in Waves

The roll motions of ships advancing in heavy seas have severe impacts on the safety of crews,vessels,and cargoes;thus,it must be damped.This study presents the design of a rudder roll damping autopilot by utilizing the dual extended Kalman filter(DEKF)trained radial basis function neural networks(RBFNN)for the surface vessels.The autopilot system constitutes the roll reduction controller and the yaw motion controller implemented in parallel.After analyzing the advantages of the DEKF-trained RBFNN control method theoretically,the ship’s nonlinear model with environmental disturbances was employed to verify the performance of the proposed stabilization system.Different sailing scenarios were conducted to investigate the motion responses of the ship in waves.The results demonstrate that the DEKF RBFNN based control system is efficient and practical in reducing roll motions and following the path for the ship sailing in waves only through rudder actions.

Removal of PCDDs/Fs from municipal solid waste incineration by entrained-flow adsorption technology

Entrained flow adsorption using activated carbon as the adsorbent is widely adopted for PCDDs/Fs-abatement in municipal solid waste incineration (MSWI) process. The effects of operating parameters including flue gas temperature, feeding rate of activated carbon, polychlorinated dibenzodioxins and polychlorinated dibenzofurans (PCDDs/Fs) concentration at the inlet of the air pollution control device (APCD), filter materials, pressure drop on PCDDs/Fs removal efficiency are reviewed and commented upon in this paper. Evaluation on the various mechanistic models for entrained flow adsorption is carried out based on the computational simulation in terms of the actual operating condition and theoretical analysis. Finally, an advancement of en- trained flow adsorption in combination of dual bag filter is introduced.

Predicting effect factors of dual bag filter system for PCDD/Fs removal from hazardous waste incineration flue gas

The dual bag filter(DBF)system is a new polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans(PCDD/Fs)emission control technology that has more efficient(PCDD/Fs)removal performance,a higher activated carbon utilization rate and less activated carbon consumption compared with the traditional single bag filter system.Moreover,few studies have been relevant to the mechanism of the PCDD/Fs removal process in the DBF system,and the selection of operating conditions of the DBF system lacks an academic basis.This study established a PCDD/Fs removal efficiency model of activated carbon injection combined bag filter(ACI+DBF)system for hazardous waste incineration flue gas and predicted the crucial effect factors.New adsorption coefficients k_(1)=532,145 Nm^(3)/(mol s)and k_(2)=45 Nm^(3)/(mol s),and the relationship expression between the number of available adsorption positions of recycled AC(AAC′)and cycle times(n)are proposed in the model.The results verify that the model error was below 5%.In addition,the PCDD/Fs removal efficiency model predicts that in a certain range,the PCDD/Fs removal efficiency increases with increasing activated carbon injection concentration.The best cycle number of activated carbon was less than 3,and the ratio of circulating activated carbon to fresh activated carbon in second bag filter(SBF)should be controlled at 7–8.

Removal of PCDD/Fs and PCBs from flue gas using a pilot gas cleaning system

A 100 Nm3 /hr capacity pilot scale dual bag filter （DBF） system was tested on the flue gas from an actual hazardous waste incinerator （HWI）, the removal efficiency of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans （PCDD/Fs） and polychlorinated biphenyls （PCBs） was also studied. The first filter collected most of the fly ash and associated chlorinated organic; then activated carbon （AC） was injected and used to collect phase chlorinated organic from the gas. Concentrations of PCDD/Fs and PCBs after the DBF system were 0.07 and 0.01 ng TEQ/Nm3 , respectively, which were both far below the national emission standard. Comparing with the original single bag filter system, the PCDD/Fs concentration dropped a lot from 0.36 to 0.07 ng TEQ/Nm3 . Increasing AC feeding rate enhanced their collection efficiency, yet reduced the AC utilization efficiency, and it still needs further study to select an appropriate feeding rate in the system. These results will be useful for industrial application and assist in controlling emissions of PCDD/Fs and other persistent organic pollutions from stationary sources in China.