Advanced Vehicle State Monitoring: Evaluating Moving Horizon Estimators and Unscented Kalman Filter

Zhang, Wenliang; Wang, Zhenpo; Zou, Changfu; Drugge, Lars; Nybacka, Mikael

doi:10.1109/tvt.2019.2909590

Cited by 28 publications

(12 citation statements)

References 36 publications

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“…The resulting NLP problem is then solved numerically by the interior point method with IPOPT [42]. Furthermore, the direct multiple shooting is capable of dealing with strong nonlinear optimization problems [43] and is cost efficient in terms of computation time [44].…”

Section: Trajectory Librarymentioning

confidence: 99%

A Data-Driven Method Towards Minimizing Collision Severity for Highly Automated Vehicles

Parseh

Asplund

Svensson

et al. 2021

IEEE Trans. Intell. Veh.

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The deployment of autonomous vehicles on public roads calls for the development of methods that are reliably able to mitigate injury severity in case of unavoidable collisions. This study proposes a data-driven motion planning method capable of minimizing injury severity for vehicle occupants in unavoidable collisions. The method is based on establishing a metric that models the relationship between impact location and injury severity using real accident data, and subsequently including it in the cost function of a motion planning framework. The vehicle dynamics and associated constraints are considered through a precomputed trajectory library, which is generated by solving an optimal control problem. This allows for efficient computation as well as an accurate representation of the vehicle. The proposed motion planning approach is evaluated by simulation, and it is shown that the trajectory associated with the minimum cost mitigates the collision severity for occupants of passenger vehicles involved in the collision.

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Section: Trajectory Librarymentioning

confidence: 99%

A Data-Driven Method Towards Minimizing Collision Severity for Highly Automated Vehicles

Parseh

Asplund

Svensson

et al. 2021

IEEE Trans. Intell. Veh.

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show abstract

“…In order to ensure the numerical stability and reduce the computing time, QR decomposition is selected to solve the least square problem (33). Via QR decomposition, A can be expressed as…”

Section: Moving Horizon Estimator Designmentioning

confidence: 99%

“…where diagð�Þ represents diagonal matrix. 33,46,47 The covariance matrix of process noise is Figure 5 shows the time responses of system states. Affected by the flexible vibration, although the response speed of SMC without MHE is faster than that of MHE-SMC and MHE-HSMC, it forces FHV into a stable limit cycle with the same frequency shown in Figure 3(c), which verifies the effectiveness of the aeroservoelastic effect analysis.…”

Section: Simulation Analysismentioning

confidence: 99%

“…Furthermore, it is difficult for EKF and UKF to handle constrained systems, which is just the advantage of moving horizon estimation (MHE). 33 Different from the Kalman-filter-type estimators, MHE transforms the state estimation problem into a constrained quadratic optimization problem in terms of a window of the most recent measurements at each time step. By adopting the moving horizon strategy, MHE tends to be more precise and robust in the presence of nonlinearity, uncertainty and external disturbance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Moving-horizon-estimator-integrated adaptive hierarchical sliding mode control for flexible hypersonic vehicles considering aeroservoelastic effect

Chen¹,

Jing

Gao

2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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In order to solve the attitude control problem of flexible hypersonic vehicles with consideration of aeroservoelastic effect, uncertainty and external disturbance, a novel moving-horizon-estimator-integrated adaptive hierarchical sliding mode control scheme is presented in this paper. First, the measurement model considering flexibility is established and the influence of aeroservoelastic effect on system stability is analyzed. Then moving horizon estimator is developed to reconstruct full state information from sensor measurements, while sliding mode disturbance observer and gain adaptation law is proposed to enhance the robustness and attenuate the chattering. Via combining moving horizon estimator, sliding mode disturbance observer, gain adaptation law and baseline hierarchical sliding mode controller, the moving-horizon-estimator-integrated adaptive hierarchical sliding mode control scheme that is able to achieve the control objective of both precise attitude control and active flexible vibration suppression is developed. Finally, Lyapunov theory is used to prove the stability of the proposed control scheme, and the numerical simulations are carried out, which further verify the effectiveness of the proposed control scheme against aeroservoelastic effect, uncertainty and external disturbance.

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“…With the development and commercialization of Advanced Driver Assistant System (ADAS), intelligent vehicles equipped with them will be of safety. 1,2 For example, a collision-free evasion path could be planned when the dangers are imminent. Note that the decision-making mechanism requires reliable information about surrounding environment and vehicle dynamic states.…”

Section: Introductionmentioning

confidence: 99%

Adaptive estimations of tyre–road friction coefficient and body’s sideslip angle based on strong tracking and interactive multiple model theories

Ping

Cheng

Yue³

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Vehicle dynamic states and parameters, such as the tyre–road friction coefficient and body’s sideslip angle especially, are crucial for vehicle dynamics control with close-loop feedback laws. Autonomous vehicles also have strict demands on real-time knowledge of those information to make reliable decisions. With consideration of the cost saving, some estimation methods employing high-resolution vision and position devices are not for the production vehicles. Meanwhile, the bad adaptability of traditional Kalman filters to variable system structure restricts their practical applications. This paper introduces a cost-efficient estimation scheme using on-board sensors. Improved Strong Tracking Unscented Kalman filter is constructed to estimate the friction coefficient with fast convergence rate on time-variant road surfaces. On the basis of previous step, an estimator based on interactive multiple model is built to tolerant biased noise covariance matrices and observe body’s sideslip angle. After the vehicle modelling errors are considered, a Self-Correction Data Fusion algorithm is developed to integrate results of the estimator and direct integral method with error correction theory. Some simulations and experiments are also implemented, and their results verify the high accuracy and good robustness of the cooperative estimation scheme.

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Advanced Vehicle State Monitoring: Evaluating Moving Horizon Estimators and Unscented Kalman Filter

Cited by 28 publications

References 36 publications

A Data-Driven Method Towards Minimizing Collision Severity for Highly Automated Vehicles

A Data-Driven Method Towards Minimizing Collision Severity for Highly Automated Vehicles

Moving-horizon-estimator-integrated adaptive hierarchical sliding mode control for flexible hypersonic vehicles considering aeroservoelastic effect

Adaptive estimations of tyre–road friction coefficient and body’s sideslip angle based on strong tracking and interactive multiple model theories

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