Takagi–Sugeno Fuzzy Unknown Input Observers to Estimate Nonlinear Dynamics of Autonomous Ground Vehicles: Theory and Real-Time Verification

Nguyen, Anh‐Tu; Dinh, Truong Quang; Guerra, Thierry‐Marie; Pan, Juntao

doi:10.1109/tmech.2020.3049070

Cited by 53 publications

(30 citation statements)

References 41 publications

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“…Indeed, using Lemma 2 and the convexity property of the bounded sets for the gradients, inequalities (33)- (34) imply clearly (20) in Theorem 1. Moreover, condition (21) can also be rewritten as…”

Section: Proof the Proof Is Based On The Convex Combination Form Of The State-space Matrices A(h) B(h) C(h) E(h) F(h) G(h) J(h) And The Dmentioning

confidence: 96%

“…13 Moreover, from the viewpoint of estimation problem formulation, unmodeled dynamics, uncertain disturbances and faults in engineering systems, or attack signals in cyber-physical systems can be considered as unknown inputs. 14,15 As a result, designing TS fuzzy observers in the presence of UIs has been an active research topic in the areas of fault estimation and diagnosis, [16][17][18][19][20] fault-tolerant control. 21,22 TS fuzzy-model-based observer design can be classified into the two following categories: 9…”

Section: Introductionmentioning

confidence: 99%

“…13 The estimation errors of both the state and the UI can be minimized by optimizing a guaranteed 𝓁 ∞ -gain performance. Moreover, the proposed N-TS fuzzy observer design does not require any specific rank condition for UI decoupling as in References [20,21,44,49]. Hence, our systematic LMI-based observer design framework can be applied to a large class of descriptor systems with UIs and unmeasured nonlinearities.…”

Section: Introductionmentioning

confidence: 99%

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Takagi–Sugeno fuzzy observer design for nonlinear descriptor systems with unmeasured premise variables and unknown inputs

Nguyen

Campos

Guerra

et al. 2021

Intl J Robust & Nonlinear

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This article presents a new observer design framework for a class of nonlinear descriptor systems with unknown but bounded inputs. In the presence of unmeasured nonlinearities, that is, premise variables, designing nonlinear observers is known as particularly challenging. To solve this problem, we rewrite the nonlinear descriptor system in the form of a Takagi-Sugeno (TS) fuzzy model with nonlinear consequents. This model reformulation enables an effective use of the differential mean value theorem to deal with the mismatching terms involved in the estimation error dynamics. These nonlinear terms, issued from the unmeasured nonlinearities of the descriptor system, cause a major technical difficulty for TS fuzzy-model-based observer design. The descriptor form is treated through a singular redundancy representation. For observer design, we introduce into the Luenberger-like observer structure a virtual variable aiming at estimating the one-step ahead state. This variable introduction allows for free-structure decision variables involved in the observer design to further reduce the conservatism. Using Lyapunov-based arguments, the observer design is reformulated as an optimization problem under linear matrix inequalities with a single line search parameter. The estimation error bounds of both the state and the unknown input can be minimized by means of a guaranteed 𝓁 ∞ -gain performance level. The interests of the new 𝓁 ∞ TS fuzzy observer design are clearly illustrated with two physically motivated examples.

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Section: Proof the Proof Is Based On The Convex Combination Form Of The State-space Matrices A(h) B(h) C(h) E(h) F(h) G(h) J(h) And The Dmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Takagi–Sugeno fuzzy observer design for nonlinear descriptor systems with unmeasured premise variables and unknown inputs

Nguyen

Campos

Guerra

et al. 2021

Intl J Robust & Nonlinear

Self Cite

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show abstract

“…For this reason, most studies in the field of preview control mainly focus on simplified or few-degree-of-freedom models, which are constrained to avoid highly coupled dynamics [25,26]. However, the simultaneous inclusion of longitudinal and lateral control becomes unavoidable to improve vehicle performance in a wide range of operations especially for autonomous vehicles [27,28]. The major difficulty of DNN-based methods for longitudinal-lateral dynamics is the number of driving situations required for building a representative training dataset.…”

Section: Introductionmentioning

confidence: 99%

Deep-Neural-Network-Based Modelling of Longitudinal-Lateral Dynamics to Predict the Vehicle States for Autonomous Driving

Nie

Min

Pan

et al. 2022

Sensors

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Multibody models built in commercial software packages, e.g., ADAMS, can be used for accurate vehicle dynamics, but computational efficiency and numerical stability are very challenging in complex driving environments. These issues can be addressed by using data-driven models, owing to their robust generalization and computational speed. In this study, we develop a deep neural network (DNN) based model to predict longitudinal-lateral dynamics of an autonomous vehicle. Dynamic simulations of the autonomous vehicle are performed based on a semirecursive multibody method for data acquisition. The data are used to train and test the DNN model. The DNN inputs include the torque applied on wheels and the vehicle’s initial speed that imitates a double lane change maneuver. The DNN outputs include the longitudinal driving distance, the lateral driving distance, the final longitudinal velocities, the final lateral velocities, and the yaw angle. The predicted vehicle states based on the DNN model are compared with the multibody model results. The accuracy of the DNN model is investigated in detail in terms of error functions. The DNN model is verified within the framework of a commercial software package CarSim. The results demonstrate that the DNN model predicts accurate vehicle states in real time. It can be used for real-time simulation and preview control in autonomous vehicles for enhanced transportation safety.

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“…In most studies, lateral and longitudinal control problems have been analyzed in a decoupled way. However, there are strong couplings between the two dynamics [2,15]. As a consequence, the simultaneous inclusion of lateral and longitudinal control becomes unavoidable for improving vehicle performance in a large operating range.…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network-based Nonlinear Dynamics Model for the Prediction of Lateral-longitudinal Vehicle Dynamics

Pan

Nie

Dai

et al. 2021

Preprint

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The vehicle multibody model can be used for accurate coupling dynamics, but it has higher computational complexity. Numerical stability during integration is also very challenging, especially in complicated driving situations. This issue can be substantially alleviated by using a data-driven nonlinear dynamics model owing to its computational speed and robust generalization. In this work, we propose a deep neural network (DNN)-based modeling approach for predicting lateral-longitudinal vehicle dynamics. Dynamic simulations of vehicle systems are performed based on a semirecursive multibody formulation for data acquisition. The data are then used for training and testing the DNN model. The DNN inputs are the torque applied on wheels and the initial vehicle speed that imitates a double lane change maneuver with acceleration and deceleration. The DNN outputs are the longitudinal driving distance, lateral driving distance, final longitudinal velocities, final lateral velocities, and yaw angle. The dynamic responses obtained from the DNN model are compared with the multibody results. Furthermore, the accuracy of the DNN model is investigated in terms of error functions. The DNN model is finally verified via the results of a commercial software package. The results show that the DNN vehicle dynamics model predicts accurate dynamic responses in real time. The DNN model can be used for real-time simulation and preview control in autonomous vehicles.

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Takagi–Sugeno Fuzzy Unknown Input Observers to Estimate Nonlinear Dynamics of Autonomous Ground Vehicles: Theory and Real-Time Verification

Cited by 53 publications

References 41 publications

Takagi–Sugeno fuzzy observer design for nonlinear descriptor systems with unmeasured premise variables and unknown inputs

Takagi–Sugeno fuzzy observer design for nonlinear descriptor systems with unmeasured premise variables and unknown inputs

Deep-Neural-Network-Based Modelling of Longitudinal-Lateral Dynamics to Predict the Vehicle States for Autonomous Driving

A Deep Neural Network-based Nonlinear Dynamics Model for the Prediction of Lateral-longitudinal Vehicle Dynamics

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