Utilization of Actuators to Improve Vehicle Stability at the Limit: From Hydraulic Brakes Toward Electric Propulsion

Jonasson, Mats; Andreasson, Johan; Solyom, Stefan; Jacobson, Bengt J H; Trigell, Annika Stensson

doi:10.1115/1.4003800

Cited by 28 publications

(14 citation statements)

References 10 publications

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“…Electrically powered ground vehicles may have in-wheel electric motors that combine drive and regenerative brake functions, possibly in combination with friction brakes, and the trend is towards highly over-actuated vehicles, (Valásek 2003), with extended stability, (Jonasson, Andreasson, Solyom, Jacobson & Trigell 2011). Control allocation can be used to coordinate the electric motors in individual combinations of drive/brake mode while optimizing energy efficiency.…”

Section: Electrical Propulsionmentioning

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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Section: Electrical Propulsionmentioning

confidence: 99%

Control allocation—A survey

2013

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“…According to the two degrees of freedom vehicle model and under the assumption of vehicle small side slip angle and side slip angle rate, the vehicle yaw rate is bounded as [20],…”

Section: Reference Generationmentioning

confidence: 99%

“…The higher-level control algorithms which integrate AFS and DYC for enhancing maneuvering performance and reducing accidents were studied recently in [13][14][15][16]. Methods of assigning the global control efforts to the lower-level actuators can be categorised to rule based [14,15,17] and optimization based control allocation [16,[18][19][20][21]. The main advantages of control allocation compared with the rule-based methods are the capability of considering multiple control objectives and constraints of actuators, and maximizing the potential of tyre forces and reconfigurability during the failure of some actuators.…”

Section: Introductionmentioning

confidence: 99%

Coordinated Active Steering and Four‐Wheel Independently Driving/Braking Control with Control Allocation

Wang¹,

Wang²,

Jing³

et al. 2015

Asian Journal of Control

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This paper presents a hierarchical coordinated control algorithm for integrating active front steering and four‐wheel independently driving control. In the higher‐level controller, an adaptive siding mode control law and a coordination law for adjusting the yaw rate and slip angle control priorities are applied to determine the desired front wheel steering angle and external yaw moment. In the lower‐level controller, a control allocation algorithm with actuators and tyre forces constraint considerations is designed to assign the desired yaw moment to the front wheel steering system and four wheels. The weighting factors of tracking errors and control inputs in the cost function are online updated according to the vehicle stability index. Simulation results with a high‐fidelity, CarSim, full‐vehicle model show that handling and stability of the vehicle can be improved with the proposed controller. What is more, control of sideslip angle can be more focused when the vehicle is hitting the manoeuvring limitation.

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“…However, the involvement of unintended braking forces may deteriorate drivers' comfort while slowing down vehicle speed. In contrast, effective torque vectoring can be introduced for FWIA EVs to enhance vehicle dynamics stability under a diverse range of driving conditions, through which the driving and/or braking torque (electric and/or friction brake) can be sourced from each wheel [5][6][7]. In this way, a desired yaw moment can be generated due to the torque difference between left and right sides [8].…”

Section: Introductionmentioning

confidence: 99%

Vehicle Stability Enhancement through Hierarchical Control for a Four-Wheel-Independently-Actuated Electric Vehicle

Wang

Zhang

et al. 2017

Energies

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In this paper, an optimal control strategy for a four-wheel-independently-actuated electric vehicle (FWIA EV) is proposed to improve vehicle dynamics stability and handling performance. The proposed scheme has a hierarchical structure composed of an upper and a lower controller. The desired longitudinal and lateral forces and yaw moment are determined based on the sliding-mode control (SMC) scheme in the upper controller, which takes the longitudinal and lateral velocity and the yaw rate as control variables. In the lower controller, an optimization algorithm is adopted to allocate the driving/braking torques to each in-wheel motor. A cost function with adjustable weight coefficients is specially designed by taking the motor power capability and the tire workload into consideration. The simulation and hardware-in-loop experimental results show that the proposed control strategy exhibits superior performance in comparison to commonly-used rule-based control strategies, and has the capability of online implementation.

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Utilization of Actuators to Improve Vehicle Stability at the Limit: From Hydraulic Brakes Toward Electric Propulsion

Cited by 28 publications

References 10 publications

Control allocation—A survey

Control allocation—A survey

Coordinated Active Steering and Four‐Wheel Independently Driving/Braking Control with Control Allocation

Vehicle Stability Enhancement through Hierarchical Control for a Four-Wheel-Independently-Actuated Electric Vehicle

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