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Reliable vehicle motion states are critical for the precise control performed by vehicle active safety systems. This paper investigates a robust estimation strategy for vehicle motion states by feat of the application of the extended set-membership filter (ESMF). In this strategy, a system noise source is only limited as unknown but bounded, rather...
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... is conducive to simplifying the calculation formulas for state variables [28] with minor effect on the overall idea of vehicle dynamic behavior. Figure 4 According to Newton's second law and rotational equilibrium, the vehicle's lateral motion and vertical rotation can be expressed by the following equations: ...
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This paper presents a general solution for computing the multi-object posterior for sets of trajectories from a sequence of multi-object (unlabelled) filtering densities and a multi-object dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multi-object filters that do not explicitly estimate...
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... In practice, to better study the steering stability performance of vehicles, some state parameters are expensive to measure or are susceptible to environmental noise during measurement, resulting in low measurement accuracy, such as speed, sideslip angle, etc. Therefore, a large amount of observation theory has been applied to vehicle state estimation [5][6][7]. The most commonly employed methods are extended Kalman filtering (EKF), SMO, and other nonlinear observers. ...
This paper suggests a hierarchical coordination control strategy to enhance the stability of distributed drive electric bus. First, an observer based on sliding mode observer (SMO) and adaptive neural fuzzy inference system (ANFIS) was designed to estimate the vehicle state parameters. Then the upper layer of the strategy primarily focuses on coordinating active front steering (AFS) and direct yaw moment control (DYC). The phase plane method is utilized in this layer to provide an assessment basis for the switching control safety of AFS and DYC. The lower layer of the strategy designs an integral terminal sliding mode controller (ITSMC) and a non-singular fast terminal sliding mode controller (NFTSMC) to obtain the optimal additional front wheel steering angle to improve handling performance. A fuzzy sliding mode controller (FSMC) is also proposed to obtain additional yaw moment to ameliorate yaw stability. Finally, the strategy proposed in this paper is subjected to simulation testing and compared with the performance of AFS and DYC systems. The proposed strategy is also evaluated for tracking errors in sideslip angle and yaw rate under two conditions. The results demonstrate that the proposed strategy can effectively adapt to various extreme environments and improve the maneuvering and yaw stability of the bus.
... A design method for obtaining interval observers that provide guaranteed lower and upper bounds of the state and unknown inputs is applied to vehicle lateral dynamic estimation to show the eectiveness of the algorithms. Also, in (Chen et al., 2020), an extended set-membership lter applied to the vehicle's longitudinal velocity, lateral velocity, and sideslip angle provides not only higher accuracy, but also can provide a 100% hard boundary which contains the real values of the vehicle states (compared to the Unscented Kalman Filter UKF-based approaches). ...
Le problème du filtrage appliqué au diagnostic automobile est étudié dans ce travail de thèse, pour les systèmes dynamiques linéaires ou nonlinéaires, à temps discret, en contexte d'incertitudes mixtes, c'est-à-dire que les incertitudes peuvent être stochastiques ou bornées (dans des intervalles). Ce contexte permet de combiner deux approches bien connues du filtrage : les approches stochastique et ensembliste. Au travers de cette thèse, nous montrons qu'elles se complètent plutôt qu'elles se concurrencent. Deux modèles issus de l'automobile sont utilisés dans les applications tout-au-long de la thèse. Il s'agit des modèles de véhicule à bicyclette et de suspension. Des méthodes mixtes de filtrage sont tout d'abord développées et présentées dans ce travail : Optimal Upper Bound Interval Kalman Filter (OUBIKF) et Reinforced Likelihood Box Particle Filter (RLBPF), l'un est dédié aux systèmes linéaires et l'autre aux systèmes nonlinéaires. Le premier se base sur le filtre de Kalman intervalle et l'améliore en utilisant les propriétés développées et la stratégie d'optimisation des bornes supérieures de toutes les matrices de covariances admissibles appartenant à une matrice d'intervalle donnée. Le second propose un schéma général de filtre particulaire ensembliste et développe une méthodologie de renforcement du calcul de la vraisemblance, l'étape cruciale du schéma, pour améliorer la performance du filtre. La deuxième partie de cette thèse est dédiée à la détection de défauts. Les filtres précédents sont utilisés et combinés à une méthode de test d'hypothèse basée X^2 avec les degrés de liberté adaptatifs, à savoir Adaptive Degrees of Freedom X^2-statistic (ADFC), pour traiter la détection de défauts dans les systèmes linéaires ou nonlinéaires. Il s'agit d'une méthode de détection de défaut passive renforcée par la technique de seuil adaptatif dans l'étape de décision. Cette méthode permet la détection de défauts additifs, simples ou multiples, sur les capteurs. Dans la dernière partie de ce travail, une méthodologie de diagnostic actif est développée, à savoir ADFC-based Active Fault Diagnosis (AFD) utilisant des signaux auxiliaires. Cette méthodologie, étude préliminaire à l'approche active, se limite à la détection de défaut simple. Cependant, ses contributions sont multiples : isolement (localisation) et identification (estimation) du défaut, réduction de fausses alarmes et amélioration de l'estimation de l'état en renvoyant le défaut estimé comme un signal de retour au filtre utilisé. Nos futures recherches se concentrent tout particulièrement sur cette approche.
... A design method for obtaining interval observers that provide guaranteed lower and upper bounds of the state and unknown inputs is applied to vehicle lateral dynamic estimation to show the eectiveness of the algorithms. Also, in (Chen et al., 2020), an extended set-membership lter applied to the vehicle's longitudinal velocity, lateral velocity, and sideslip angle provides not only higher accuracy, but also can provide a 100% hard boundary which contains the real values of the vehicle states (compared to the Unscented Kalman Filter UKF-based approaches). ...
... Paper [21] proposes a robust estimation strategy for vehicle motion states by applying the extended set-membership filter. A calculation scheme with a simple structure is proposed to acquire the longitudinal and lateral tire forces with acceptable accuracy. ...
The optimization of motion and trajectory planning is an effective and usually costless approach to improve the performance of dynamic systems, such as robots, mechatronic systems, automatic machines and multibody systems [...]
Autonomous driving is producing various novel achievements in integrating different control technologies with machine learning to control intelligent vehicles, i.e., learning-based control. Yet, few work formally consider the confidence of learning-based models and their residual model uncertainties. As a result, existing methods lack safety guarantees in real-world applications. To address this issue, this work proposes a Linear-Bayesian-Regression-based Model Predictive Control (MPC) method for vehicles with unknown dynamics and subject to possibly unbounded disturbances. Model parameters can be obtained by using posterior calculations via prior estimations and systems’ historical data. The expected value of resulting posterior distribution is chosen as linear model's parameters for controller design. Furthermore, the corresponding posterior distribution can be used to derive an upper bound for the variance of residual model uncertainties. In analogy to the concept of Tube-MPC, this work introduces a dynamic feedback gain which is combined with the standard nominal MPC method. It derives provable security guarantees with respect to state and input chance constraints for complex unknown dynamical systems in the form of probabilities. A convex-reformulation-based optimization method is proposed to enable efficient computation of control laws and ensure the recursive feasibility. Simulations of a Vander Pol's oscillator's control and a vehicle's tracking control illustrate the design procedure and effectiveness of the proposed method.
