Control of a ground vehicle using quadratic programming based control allocation techniques

Plumlee, John H.; Bevly, David M.; Hodel, A.S.

doi:10.23919/acc.2004.1384055

Cited by 69 publications

(44 citation statements)

References 16 publications

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“…Similarly, one investigation developed an offline optimization procedure to efficiently distribute torque and select the transmission state in a dual motor electric drivetrain [8]. Typically, the control allocation methods are based on minimizing a low order function such as a quadratic cost function [9], or alternatively, can be based on a set of rules [10].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Energy Efficiency of Fully Electric Vehicles via the Minimization of Motor Power Losses

Pennycott

Novellis

Gruber

et al. 2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Abstract-Individually-controlled powertrains of fully electric vehicles present an opportunity to enhance the steady-state and transient cornering response of a car via continuously-acting controllers and enable various "driving modes" to be available. This study investigates the associated potential for energy savings through the minimization of power losses from the motor units via wheel torque allocation. Power losses in straight-ahead driving and a ramp steer maneuver for different motor types and under different wheel torque allocation schemes are analyzed in an offline simulation approach. Significant reductions in motor power losses are achieved for two motor types using an optimization scheme based on look-up tables of motor loss data. Energy loss minimization cannot be achieved through a direct quadratic approximation of the power losses.

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Section: Introductionmentioning

confidence: 99%

Enhancing the Energy Efficiency of Fully Electric Vehicles via the Minimization of Motor Power Losses

Pennycott

Novellis

Gruber

et al. 2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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“…However, in order to avoid the online computational burden of nonlinear programming, several simplified approaches have been proposed. The effector mapping from longitudi-nal tire slips and slip angles are linearized in (Wang, Solis & Longoria 2007) and an accelerated fixed-point iteration algorithm is studies as a computationally efficient alternative to quadratic programming, (Plumlee, Bevly & Hodel 2004). A commonly used control allocation objective is to minimize friction forces, for example the adhesion potential characterized using friction ellipse models for each individual tyre, (Knobel, Pruckner & Bünte 2006).…”

Section: Yaw Stability Controlmentioning

confidence: 99%

Control allocation—A survey

2013

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The control algorithm hierarchy of motion control for over-actuated mechanical systems with a redundant set of effectors and actuators commonly includes three levels. First, a high-level motion control algorithm commands a vector of virtual control efforts (i.e. forces and moments) in order to meet the overall motion control objectives. Second, a control allocation algorithm coordinates the different effectors such that they together produce the desired virtual control efforts, if possible. Third, low-level control algorithms may be used to control each individual effector via its actuators. Control allocation offers the advantage of a modular design where the high-level motion control algorithm can be designed without detailed knowledge about the effectors and actuators. Important issues such as input saturation and rate constraints, actuator and effector fault tolerance, and meeting secondary objectives such as power efficiency and tear-and-wear minimization are handled within the control allocation algorithm. The objective of the present paper is to survey control allocation algorithms, motivated by the rapidly growing range of applications that have expanded from the aerospace and maritime industries, where control allocation has its roots, to automotive, mechatronics, and other industries. The survey classifies the different algorithms according to two main classes based on the use of linear or nonlinear models, respectively. The presence of physical constraints (e.g input saturation and rate constraints), operational constraints and secondary objectives makes optimization-based design a powerful approach. The simplest formulations allow explicit solutions to be computed using numerical linear algebra in combination with some logic and engineering solutions, while the more challenging formulations with nonlinear models or complex constraints and objectives call for iterative numerical optimization procedures. Experiences using the different methods in aerospace, maritime, automotive and other application areas are discussed. The paper ends with some perspectives on new applications and theoretical challenges.

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“…Additionally, because of such advantages, the LQC algorithm has been applied to various areas such as ground-vehicles, missions, aircraft, and spacecraft Lovren and Tomic, 1994;Plumlee and Bevly, 2004;Psiaki, 2001;Ridgely et al, 1987).…”

Section: Introductionmentioning

confidence: 99%

Collision Avoidance Using Linear Quadratic Control in Satellite Formation Flying

Mok¹,

Choi²,

Bang³

2010

International Journal of Aeronautical and Space Sciences

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This paper proposes a linear system control algorithm with collision avoidance in multiple satellites. Consideration of collision avoidance is augmented by adding a weighting term in the cost function of the original tracking problem in linear quadratic control (LQC). Because the proposed algorithm relies on a similar solution procedure to the original LQC, its inherent advantages, including gain-robustness and optimality, are preserved. To confirm and visualize the derived algorithm, a simple example of two-vehicle motion in the two-dimensional plane is illustrated. In addition, the proposed collision avoidance control is applied to satellite formation flying, and verified by numerical simulations.

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Control of a ground vehicle using quadratic programming based control allocation techniques

Cited by 69 publications

References 16 publications

Enhancing the Energy Efficiency of Fully Electric Vehicles via the Minimization of Motor Power Losses

Enhancing the Energy Efficiency of Fully Electric Vehicles via the Minimization of Motor Power Losses

Control allocation—A survey

Collision Avoidance Using Linear Quadratic Control in Satellite Formation Flying

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