Hybrid architecture for vehicle lateral collision avoidance

Ariola, M.; Tommasi, G. De; Tartaglione, Gaetano; Amato, Francesco

doi:10.1049/iet-cta.2017.1387

Cited by 11 publications

(3 citation statements)

References 26 publications

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“…For this reason FTS is a more practical concept than Lyapunov stability, that is, it can be used to verify than the state trajectories of the system remain inside a specified domain over the considered time‐interval. Indeed, FTS has been recently adopted for the solution of control problem related to different applications, such as the design of a collision avoidance system for a vehicle [2] and the design of a control system for a missile [3].…”

Section: Introductionmentioning

confidence: 99%

New conditions for finite‐time stability of impulsive dynamical systems via piecewise quadratic functions

Ambrosino

Ariola

Garone

et al. 2022

IET Control Theory & Appl

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In this paper, the use of time‐varying piecewise quadratic functions is investigated to characterize the finite‐time stability of state‐dependent impulsive dynamical linear systems. Finite‐time stability defines the behavior of a dynamic system over a bounded time interval. More precisely, a system is said to be finite‐time stable if, given a set of initial conditions, its state vector does not exit a predefined domain for a certain finite interval of time. This paper presents new sufficient conditions for finite‐time stability based on time‐varying piecewise quadratic functions. These conditions can be reformulated as a set of Linear Matrix Inequalities that can be efficiently solved through convex optimization solvers. Different numerical analysis are included in order to prove that the presented conditions are able to improve the results presented so far in the literature.

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Section: Introductionmentioning

confidence: 99%

New conditions for finite‐time stability of impulsive dynamical systems via piecewise quadratic functions

Ambrosino

Ariola

Garone

et al. 2022

IET Control Theory & Appl

Self Cite

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“…More recently, the FTS problem has been tackled for hybrid systems [15,16,17] as well as in the stochastic framework [18,19,20]. Such increasing interest in FTS and in the associated input-output notion (IO-FTS [21]) comes from the possibility of effectively adopt FTS concepts to enforce specific quantitative requirements on the transient of the closed-loop response of a control system [22,23].…”

Section: Introductionmentioning

confidence: 99%

Finite-Time stabilization of linear systems with unknown control direction via Extremum Seeking

Mele¹,

Tommasi²,

Pironti³

2021

Preprint

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In this paper the finite-time stabilization problem is solved for a linear time-varying system with unknown control direction by exploiting a modified version of the classical extremum seeking algorithm. We propose to use a suitable oscillatory input to modify the system dynamics, at least in an average sense, so as to satisfy a Differential Linear Matrix Inequality (DLMI) condition which in turns guarantees that the system's state remains inside a prescribed time varying hyper-ellipsoid in the state space. The finite-time stability (FTS) of the averaged dynamics implies the FTS of the original system, as the distance between the original and the averaged dynamics can be made arbitrarily small by choosing a sufficiently high value of the dithering frequency used by the extremum seeking algorithm. An estimate of the necessary minimum dithering/mixing frequency is provided, and the effectiveness of the proposed finite-time stabilization approach is analysed by means of numerical examples.

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“…Vehicle steering control is the basis for autonomous or semi‐autonomous driving. For a vehicle, there is still a large room for the fully autonomous steering control system to improve its safety through collision avoidance [3] or lane‐keeping system [4]. However, there are at least two main problems with fully autonomous driving.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning of an unknown road path through cooperative driving of vehicles

Yang

Huang

et al. 2020

IET Intelligent Trans Sys

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This study proposes a method for a vehicle controller to learn human driving behaviours through iterative interactions. In particular, the vehicle controller and the human driver jointly control a vehicle along a path only known to the human driver. Through repeated cooperative driving, the vehicle controller estimates the hidden desired path of the driver by minimising the control input. Eventually, semi‐autonomous driving is realised since the vehicle controller is able to automatically track the target path and release the human driver from the driving task. The iterative learning of the human target path on the basis of the proposed algorithm is in the spatial domain, and is effective in the presence of uncertain human driving speeds. The validity of the proposed method is proved by rigorous analysis and demonstrated by numerical simulations.

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Hybrid architecture for vehicle lateral collision avoidance

Cited by 11 publications

References 26 publications

New conditions for finite‐time stability of impulsive dynamical systems via piecewise quadratic functions

New conditions for finite‐time stability of impulsive dynamical systems via piecewise quadratic functions

Finite-Time stabilization of linear systems with unknown control direction via Extremum Seeking

Iterative learning of an unknown road path through cooperative driving of vehicles

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