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International audience; Autonomous driving has the ability to reshape mobility and transportation by reducing road accidents, traffic jams, and air pollution. This can yield energy efficiency, convenience, and more productivity as significant driving time will be gained and used in other activities instead. Autonomous vehicles are complex systems consisting of several modules that perform perception, decision-making, planning, and control. Control is essential for achieving automatic driving; it is basically divided into longitudinal control that handles speed tracking and lateral control which ensures accurate steering. The latter is primordial in path tracking applications and recent research has witnessed a huge leap in this field. The aim of this paper is to provide a technical survey of the latest research on the lateral control of autonomous vehicles as well as to highlight technical challenges and limits for further developments. © 2022 The Authors. Asian Journal of Control published by John Wiley & Sons Australia, Ltd on behalf of Chinese Automatic Control Society.

International audience; This paper presents an Unknown Input Observer (UIO) for a class of Lipschitz nonlinearsystems affected by disturbances/faults and having slow internal dynamics after the decoupling ofthe Unknown Input (UI). Thanks to the Asymptotic Decoupling notion, the constraint related to theinternal dynamics is bypassed. The nonlinear UIO presents time varying dynamics that allows toobtain the Asymptotic Decoupling notion. It will be shown that the proposed approach allows toenhance the convergence rate of the UIO. The convergence enhancement of the state estimation erroris demonstrated via a Lyapunov analysis and the established conditions are expressed in terms of LinearMatrix Inequalities. Finally, simulations will be given in order to illustrate the proposed approach withsome comparisons to existing techniques.

International audience; The main control tasks in autonomous vehicles are steering (lateral) and speed (longitudinal) control. PID controllers are widely used in the industry because of their simplicity and good performance, but they are difficult to tune and need additional adaptation to control nonlinear systems with varying parameters. In this paper, the longitudinal control task is addressed by implementing adaptive PID control using two different approaches: Genetic Algorithms (GA-PID) and then Neural Networks (NN-PID) respectively. The vehicle nonlinear longitudinal dynamics are modeled using Powertrain blockset library. Finally, simulations are performed to assess and compare the performance of the two controllers subject to external disturbances.

International audience; This paper concerns the problem of attitude/altitude control of a quadrotor. The main contribution consists of developing a simple Linear Parameter Varying (LPV) model which includes the motor dynamics, weight, and moment of inertia variations. In addition, a robust LPV H∞ state-feedback controller is proposed. It allows us to perform both a reference trajectory tracking and disturbance rejection for attitude/altitude control of a mass-varying quadcopter. First, an augmented state which includes the integration of the trajectory errors for improving tracking control is computed. Next, to penalize the control inputs of the attitude/altitude system, weight functions are also added to the previous augmented system. Then, an H∞ state-feedback controller is designed by solving a set of Linear Matrix Inequalities (LMI) obtained from the Bounded Real Lemma and LMI region characterization. Simulations are conducted for several types of disturbances (sine, impulse, step, and random) and variations (slow and abrupt) of mass and moments of inertia. The reference path (sine, impulse, and step) is well-followed showing the ability of the design method to handle different performance objectives.

International audience; This work is devoted to the problem of guaranteed set-membership state estimation based on zonotopes for discrete-time switched systems with bounded uncertainties and unknown inputs. The additive uncertainties in this contribution are treated using the so called W i-Radius. The size of the zonotope which contains the real system state is decreased at each sample time. The complete solution procedure is formulated as a LMI-constrained optimization problem. The proposed design is applied to the robust state estimation of vehicle lateral dynamics state. Simulation results based on real data demonstrate the performance of the proposed method.

International audience; This paper presents a new set-membership estimation methodology for uncertain switched LPV discrete-time systems subject to unknown inputs, unmeasurable time-varying parameters and measurement noise. The proposed approach provides a guaranteed interval that is constructed as the sum of punctual state estimation and its corresponding estimation error limits. First, a punctual switched unknown input observer, robust against unknown inputs and time-varying uncertainties, is constructed. The proposed switched observer design is based on the solution of an optimization problem in terms of LMIs. Then, an outer-approximation of the enclosure set of state estimation error is computed using the admissible bounds of state and uncertainties. Application to vehicle state estimation is provided to show the design procedure and the flexibility of the proposed scheme. Comparison to real data demonstrates the accuracy and effectiveness of the obtained results.

This paper is devoted to the state and input estimation of a linear time varying system in the presence of an unknown input (UI) in both state and measurement equations, and affected by Gaussian noises. The classical rank condition used in this kind of approach is relaxed in order to be able to be used in a wider range of systems. A state observer, that is an unbiased estimator with minimum error variance, is proposed. Then a UI observer is constructed, in order to be a best linear unbiased estimator, it follows a unique construction whether the direct feedthrough matrix is null or not. In a sense the proposed approach, generalizes and unifies the existing ones. Besides, a stability result is given for linear time invariant systems, which is a novelty for unbiased minimum variance observers relaxing the classical rank condition.

The objective of this study is the analysis of dynamic systems represented by multi-model with variable parameters. Changes in these parameters are unknown but bounded. Since it is not possible to estimate these parameters over time, the simulation of such systems requires to consider all possible values taken by these parameters. More precisely, the goal is to determine, at any moment, the smallest set containing all the possible values of the state vector simultaneously compatible with the state equations and with a priori known bounds of the uncertain parameters. This set will be characterized by two trajectories corresponding to the lower and upper limits of the state at every moment. This characterization can be realized by a direct simulation of the system, given the bounds of its parameters. It can also be implemented with a Luenberger type observer, fed with the system measurements.

International audience; This study addresses the problem of guaranteed state and unknown input (UI) estimation for a class of linearparameter varying systems affected by UIs and other bounded perturbations. The context of interval observer in the cooperativeframework is adopted. Thus, lower and upper bounds of the state and UI are provided. No assumption is made on the UI. Theapproach is based on a rank condition allowing to decouple the UI from the state estimation errors (so that the state estimationis guaranteed, regardless of the variation of the UI). The convergence of the proposed interval observer is studied by Lyapunovtheory and stability conditions are expressed in terms of linear matrix inequalities which ease the design of the gain matrices ofthe observer. Finally, illustrative examples are given for discussion and comparison with respect to the existing results.

International audience; This paper proposes state and unknown input (UI) observers for linear parameter varying (LPV) systems affected by UI and perturbations in both state and measurement equations. The estimation is done by minimizing the L2 transfer between the perturbations and the state estimation error (H∞-observation). The originality of the paper is to provide a generalization of existing works, specially by relaxing an assumption on systems matrices, widely used for UI decoupling. After giving the main results, some examples illustrate the theoretical contributions and give comparisons to former publications.

International audience; This paper proposes an Unknown Input Extended Kalman Filter (UIEKF) for stochastic non linear systems affected by Gaussian noises and Unknown Inputs (UI) in both state and measurement equations. The proposed approach is based on a total decoupling of the UI, in spite of the presence of nonlinearities in the measurement equation. The UI is decoupled under some structural constraints, and a state estimator is provided. Besides an UI estimator is also proposed. Finally, the proposed filter is applied on a classical navigation example, illustrating its advantages.

International audience; This work considers observer design for nonlinear systems by using Takagi-Sugeno (TS) models combined to the interconnected systems formalism. The approach is based, on a TS models of interconnected parts obtained from an adequate decomposition of the initial nonlinear system into subsystems , and then transforming each one into Takagi-Sugeno's representation. The observer design conditions for this new representation are expressed as Linear Matrix Inequality (LMI) constraints obtained from a common quadratic Lyapunov functions for stability analysis. The proposed observer, Luenberger-like structure , aims to reduce the number of LMIs and then reduces the conservatism related to the huge number of verticies in the polytope. Numerical examples show the effectiveness of the proposed approach.