A fuzzy logic intelligent control system paradigm for an in-line-of-sight leader-following HMMWV

Cheok, Ka C.; Smid, G.E.; Kobayashi, Kazuyuki; Overholt, James L.; Lescoe, Paul

doi:10.1002/(sici)1097-4563(199706)14:6<407::aid-rob3>3.0.co;2-s

Cited by 10 publications

(6 citation statements)

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“…Given the inter-vehicle kinematics in error coordinates (20), and assuming that the linear and angular velocities of the lead vehicle (v A1 , 1 ) are unknown constant parameters and are not available for feedback control, the control objective is to asymptotically track the virtual reference point R 1r the lead vehicle with the reference point R 2f of the following vehicle.…”

Section: Fig (5) Desired Inter-vehicle Spacingmentioning

confidence: 99%

“…Since the dynamics of e has not been taken into account into the feedback control design, the next step in the stability analysis is to establish asymptotic convergence of e to the equilibrium state e ss which depends of lead vehicle linear and angular velocities (v A1 , 1 ), or equivalently the curvature radius of the path driven by the lead vehicle = 1 / 1 , and the distances L 1 and L 2 . We analyze the zero dynamics of e assuming that e x (t) 0; e y (t) 0 and v A1 0; 1 0 for all time.…”

Section: Adaptive Control Designmentioning

confidence: 99%

“…The research effort into vehicle platooning and automatic vehicle following is based on the fact that often multiple vehicles have the possibility of solving transportation problems more efficiently than a single vehicle. This scenario is very useful in the case of military convoys [1][2][3], convoys of commercial vehicles [4,5], personal vehicles [6] or in the public urban transport [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Adaptive Control of a Two-Vehicle Convoy

Petrov¹

2009

TOCSJ

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This paper describes the modeling of a two-vehicle convoy and the design of a vehicle following controller that tracks the trajectory of the vehicle ahead with prescribed inter-vehicle distance. Kinematic equations of the system are formulated applying standard robotic methodology. We consider autonomous vehicle following without any information obtained from road infrastructure or communicated from the lead vehicle. Assuming that the leader linear and angular velocities, as well the curvature radius of the path traveled by the lead vehicle, are unknown constant parameters, an adaptive tracking controller is proposed. With only the current inter-vehicle relative position and orientation available for feedback control, the control velocities of the following vehicle are computed using the leader velocity estimates obtained from the dynamic (adaptive) part of the proposed controller. For constant velocity maneuvers of the leader, at steady state, the two-vehicle convoy will travel concentric arcs of same radii with prescribed inter-vehicle spacing. Various simulation results demonstrating the performance of the controller are included.

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Section: Fig (5) Desired Inter-vehicle Spacingmentioning

confidence: 99%

Section: Adaptive Control Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nonlinear Adaptive Control of a Two-Vehicle Convoy

Petrov¹

2009

TOCSJ

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“…The relative distance between robots and is given by (12) and the line of sight angle is given by (13) For a convoy of robots, we have lines of sight and line of sight angles. Consider the relative velocity between two successive robots (14) can be decomposed into two components along and across the line of sight . The radial velocity denoted by is the component of along the line of sight; the tangential velocity denoted by is the component of across the line of sight.…”

Section: Robots' Model and Relative Kinematics Equationsmentioning

confidence: 99%

Modeling and Controlling a Robotic Convoy Using Guidance Laws Strategies

Belkhouche

2005

IEEE Trans. Syst., Man, Cybern. B

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Abstract-This paper deals with the problem of modeling and controlling a robotic convoy. Guidance laws techniques are used to provide a mathematical formulation of the problem. The guidance laws used for this purpose are the velocity pursuit, the deviated pursuit, and the proportional navigation. The velocity pursuit equations model the robot's path under various sensors based control laws. A systematic study of the tracking problem based on this technique is undertaken. These guidance laws are applied to derive decentralized control laws for the angular and linear velocities. For the angular velocity, the control law is directly derived from the guidance laws after considering the relative kinematics equations between successive robots. The second control law maintains the distance between successive robots constant by controlling the linear velocity. This control law is derived by considering the kinematics equations between successive robots under the considered guidance law. Properties of the method are discussed and proven. Simulation results confirm the validity of our approach, as well as the validity of the properties of the method.

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“…This scenario is very useful in the case of military convoys (Cheok, et al 1997;Brumitt, et al 1998;Hogg, et al 2001), convoys of commercial vehicles (Fritz, 1999) or in the public urban transport (Parent and Daviet, 1996). While the problem of automatic vehicle following in the case of forward driving has attracted a great deal of attention during the last decade (Lu, et al, 2004;White et al, 2001;Canudas-de-Wit and Doudi-Likho, 2000), to our knowledge, the problem of backward driving has seldom been addressed in the literature (Artus, et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control for Reversing a Two-Vehicle Platoon

Petrov

Parent

2006

IFAC Proceedings Volumes

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This paper presents an adaptive tracking controller for a two-vehicle convoy where the lead vehicle is driven in reverse. We assume that the lead vehicle linear and angular velocities are unknown constant parameters. We consider the problem of autonomous vehicle tracking without the use of road infrastructure or inter-vehicle communication. The only information the robot vehicle can use for feedback control is the relative position and orientation with respect to the lead vehicle obtained from onboard sensing. The control velocities of the ego-vehicle are computed using the leader velocity estimates obtained from the dynamic part of the proposed controller. The proposed adaptive control law achieves asymptotic stabilization of the closed-loop system in error coordinates. For constant velocity maneuvers of the leader, at steady state, the two-vehicle convoy will travel concentric arcs of same radii with prescribed inter-vehicle spacing. Simulation results are presented to illustrate the effectiveness of the proposed controller.

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A fuzzy logic intelligent control system paradigm for an in-line-of-sight leader-following HMMWV

Cited by 10 publications

References 0 publications

Nonlinear Adaptive Control of a Two-Vehicle Convoy

Nonlinear Adaptive Control of a Two-Vehicle Convoy

Modeling and Controlling a Robotic Convoy Using Guidance Laws Strategies

Adaptive Control for Reversing a Two-Vehicle Platoon

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