Design of an Interacting Multiple Model‐Cubature Kalman Filter Approach for Vehicle Sideslip Angle and Tire Forces Estimation

Hou, Shuai; Wen-bo, XU; Liu, Gang

doi:10.1155/2019/6087450

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…And the UKF algorithm introducing multi-model interactive IMM in the estimation of vehicle side slip angle, has a corresponding noise covariance matrix under different driving conditions, which is more adaptable and robust than the traditional constant matrix. The cubage Kalman filtering (CKF) has been introduce into the design of observers in some studies, such as the literature, 21 that an interactive multimode cubage Kalman filter (IMM-CKF) is proposed that is used for vehicle side slip angle and tire lateral force estimation. The multiple models are seven degrees of freedom vehicle models respectively based on linear tire models and nonlinear Dugoff tire models, that switch between normal driving conditions and extreme driving condition.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the state and parameter of full electric driven ground unmanned platform with variable configuration

Wang

Zou

2023

Proceedings of the Institution of Mechanical Engineers, Part D:

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A ground unmanned platform with variable configuration can better adapt to the field combat environment, which can adjust the wheelbase and wheel track to adapt to the ground. The power system of the platform is distributed drive form, with better controllability, higher drive efficiency, and faster system response. Changes of wheelbase and wheel track can influence the position of the platform’s centroid, which is critical for dynamic control and platform state acquisition. Based on the 9DOF nonlinear vehicle model, this paper introduces the relative position parameters of the vehicle centroid and the inertial measurement unit. And this paper performs coordinate transformation to accurately estimate the state parameters such as the position of the center of mass, the speed of the vehicle, and the centroid side-slip angle according to the measurement data, avoiding estimation error due to the change of centroid position. On the basis of fusing on-board multi-sensor information such as wheel hub torque, a square-root Unscented Kalman Filtering algorithm (SR-UKF) with high stability performance that can adapt to strong nonlinear system is proposed. In the Simulink/Trucksim joint platform, the double lane change experiments with different intermediate axis positions show that the estimation method can observe the driving status and parameters of the vehicle in real time, and the tracking effect is better.

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Section: Introductionmentioning

confidence: 99%

Estimation of the state and parameter of full electric driven ground unmanned platform with variable configuration

Wang

Zou

2023

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…An improved adaptive unscented Kalman filter was developed to estimate the longitudinal and lateral velocities with ESP sensors [6]. In references [7,8], the tire forces were estimated with the measurement vector combined with ESP sensors. In reference [9], the tire forces are estimated with the measurement of yaw rate, longitudinal acceleration, and lateral acceleration only, with a difficulty that the observer gains were necessary but inaccessible.…”

Section: Introductionmentioning

confidence: 99%

Multistage Estimators for the Distributed Drive Articulated Steering Vehicle

Gao

Wang

et al. 2020

Mathematical Problems in Engineering

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The distributed drive articulated steering vehicle (DDASV) has a broad application prospect in the field of special operations. It is essential to obtain accurate vehicle states for better effect of active control. DDASV dynamic model is presented. To improve robustness, an adaptive strong tracking algorithm is applied to the singular value decomposition unscented Kalman filter (SVDUKF). Divided by yaw rate sensors and the tire models, two multistage estimators are established for DDASVs. Stable steering condition is simulated to investigate the influence on the estimated accuracy about the sensors and tire models. The velocities and tire forces are the key parameters to be estimated. The performance of each estimator regarding the practicability and accuracy is compared. The results show that all estimators are practicable. However, the accuracy of the estimated velocities based on yaw rate sensors is better and the transient tire model can improve the accuracy of estimated lateral forces more effectively for the estimator established with yaw rate sensors.

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“…In Ref. [17][18][19], CKF is used as a sub model of IMM algorithm, which achieved good tracking effect. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Combination of IMM Algorithm and ASTRWCKF for Maneuvering Target Tracking

Jin

Guo

2020

IEEE Access

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In this paper, an improved interactive multiple model adaptive strong tracking random weighted cubature Kalman filter (IIMM-ASTRWCKF) algorithm is developed to overcome the low tracking accuracy and easy divergence when dealing with complex maneuvering situations. The algorithm is improved in two aspects: On the one hand, ASTRWCKF is used as the sub filter of IMM algorithm to filter different motion models. By introducing the random weight factor to replace the original weight factor, the accuracy of the algorithm is improved. At the same time, the adaptive strong tracking filter is added to update the prediction covariance matrix and noise covariance matrix for the stability of the algorithm. On the other hand, this algorithm proposes a new method to improve the probability conversion accuracy of IMM by adding time-varying factor to adjust Markov probability transfer matrix. Compared with the performance of IMM-CKF and in dealing with maneuvering problems, IIMM-ASTRWCKF algorithm has better tracking accuracy in solving maneuvering problem.

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Design of an Interacting Multiple Model‐Cubature Kalman Filter Approach for Vehicle Sideslip Angle and Tire Forces Estimation

Cited by 5 publications

References 28 publications

Estimation of the state and parameter of full electric driven ground unmanned platform with variable configuration

Estimation of the state and parameter of full electric driven ground unmanned platform with variable configuration

Multistage Estimators for the Distributed Drive Articulated Steering Vehicle

Combination of IMM Algorithm and ASTRWCKF for Maneuvering Target Tracking

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