Wheel slip control with torque blending using linear and nonlinear model predictive control

Basrah, Mohd Sofian; Siampis, Efstathios; Velenis, Efstathios; Cao, Dongpu; Longo, Stefano

doi:10.1080/00423114.2017.1318212

Cited by 69 publications

(27 citation statements)

References 27 publications

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“…The same authors state that "the simulation and test results demonstrated that the l1-optimal hybrid controller used in this problem can lead to about 20% reduction in peak slip amplitudes and corresponding spin duration when compared to best case linear MPC counterparts." Similarly, [17] shows the superiority of NMPC over linear MPC in terms of slip control performance. The necessity of "objective benchmarking technologies" in the field of ABS / TC was pointed out in the survey study in [19].…”

Section: Introductionmentioning

confidence: 90%

“…In this study a multi-parametric quadratic programming (mp-QP) formulation is adopted, as suggested in [21] and implemented in [24]. The mp-NLP in (13) and (14) is linearized around a predefined point ( 0 , ,0 ) by means of Taylor series expansion, such that the cost function is approximated with a quadratic function (15)- (16) and the constraints assume a linear formulation (17):…”

Section: B Off-line Solutionmentioning

confidence: 99%

“…The resulting computational load makes implicit NMPC difficult to implement in real automotive applications, if the required sampling frequency is high. In this respect [17] provides an example of real-time capable NMPC for an ABS with torque blending, including a comparison with a linear MPC approach. The results show that the computational time of the implicit NMPC, i.e., 3-4 ms on a desktop personal computer, is within the selected sampling interval of 5 ms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Explicit Nonlinear Model Predictive Control for Electric Vehicle Traction Control

Tavernini

Metzler

Gruber

et al. 2019

IEEE Trans. Contr. Syst. Technol.

108

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Abstract-This study presents a traction control system for electric vehicles with in-wheel motors, based on explicit non-linear model predictive control. The feedback law, available beforehand, is described in detail, together with its variation for different plant conditions. The explicit controller is implemented on a rapid control prototyping unit, which proves the real-time capability of the strategy, with computing times in the order of microseconds. These are significantly lower than the required sampling time for a traction control application. Hence, the explicit model predictive controller can run at the same frequency as a simple traction control system based on Proportional Integral (PI) technology. High-fidelity model simulations provide: i) a performance comparison of the proposed explicit non-linear model predictive controller with a benchmark PI-based traction controller with gain scheduling and anti-windup features; and ii) a performance comparison among two explicit and one implicit non-linear model predictive controllers based on different internal models, with and without consideration of transient tire behavior and load transfers. Experimental test results on an electric vehicle demonstrator are shown for one of the explicit non-linear model predictive controller formulations.Index Terms-traction control, wheel slip, model predictive control, PI control, electric vehicle, in-wheel motors.Sorniotti (email: a.sorniotti@surrey.ac.uk) are with the

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Section: Introductionmentioning

confidence: 90%

Section: B Off-line Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Explicit Nonlinear Model Predictive Control for Electric Vehicle Traction Control

Tavernini

Metzler

Gruber

et al. 2019

IEEE Trans. Contr. Syst. Technol.

108

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“…torque blending, and compares the NMPC simulation results with those from a linear MPC. In addition to the better performance of the nonlinear solution, [23] reports that the computational time for the NMPC is of 3-4 ms on a desktop personal computer, whereas the linear MPC requires ~1 ms. In [24] an implicit NMPC slip control strategy is assessed in simulation, and implemented on a quad-core 2.8 GHz dSPACE unit yielding a computational time of 4-5 ms. [25] shows that the implementation time step is more influential than the selected control technology (NMPC or PID) on the performance of a traction controller for an electric vehicle with in-wheel motors.…”

mentioning

confidence: 99%

“…Hence, it is desirable to have a controller capable of a rather smooth wheel slip control action, without the typical chattering issues of sliding mode controllers.  Because of its explicit nature, the specific ABS eNMPC of this study requires only a fraction of the computing time (i.e., < 0.1 ms on a 900 MHz dSPACE automotive platform) of the implicit solutions in [23,24], and therefore can run at much smaller time steps than an equivalent implicit NMPC.  In eNMPC the explicit solution is known in advance, which allows carrying out a systematic a-priori analysis of the control system performance, with benefits in terms of functional safety of the automotive system, with respect to implicit NMPC.…”

mentioning

confidence: 99%

An Explicit Nonlinear Model Predictive ABS Controller for Electro-Hydraulic Braking Systems

Tavernini

Vacca

Metzler

et al. 2020

IEEE Trans. Ind. Electron.

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This study addresses the development and Hardware-in-the-Loop (HiL) testing of an explicit nonlinear model predictive controller (eNMPC) for an anti-lock braking system (ABS) for passenger cars, actuated through an electro-hydraulic braking (EHB) unit. The control structure includes a compensation strategy to guard against performance degradation due to actuation dead times, identified through experimental tests. The eNMPC is run on an automotive rapid control prototyping unit, which shows its real-time capability with comfortable margin. A validated high-fidelity vehicle simulation model is used for the assessment of the ABS on a HiL rig equipped with the braking system hardware. The eNMPC is tested in 7 emergency braking scenarios, and its performance is benchmarked against a proportional integral derivative (PID) controller. The eNMPC results show: i) the control system robustness with respect to variations of tire-road friction condition and initial vehicle speed; and ii) a consistent and significant improvement of the stopping distance and wheel slip reference tracking, with respect to the vehicle with the PID ABS.

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Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

Saiteja

Ashok

Wagh

et al. 2022

Intl J of Energy Research

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Summary Vehicle technology advances and the world shifts towards E‐mobility, improving the performance of electric vehicles (EVs) and HEVs, which are becoming the prominent focus. One of the prime topics of EV development is increasing the driving range, which is the fundamental requirement. Apart from increasing the battery capacity, retrieving the wasted energy during conventional braking, also called as regenerative braking, is a hot topic. In this context, numerous control architectures and major braking approaches are considered to examine in this study in order to create an efficient regenerative braking system (RBS). This review article intends to provide an overview of major subsystems in the RBS such as motor control system and hydraulic braking system and how it affects the braking performance is also well discussed. Additionally, it complies with some of the recent research applications and systematically reviews the several braking control strategies implied. The prominent ones are fuzzy logic control, neural network, MPC, sliding mode, and adaptive control modelling approaches. These control strategies are used to enhance energy regeneration without affecting vehicle performance. Further, this article discusses the RBS design process and its calibration variables, such as speed of the vehicle and brake force estimation, which can be used to improve braking performance. Moreover, challenges on RBS improvements are effectively addressed, coupled with brief suggestions and discussions for the growth of future RBS development. Finally, this article will hopefully help the reader to critically analyse the working of RBS and encourage to design of an efficient RBS for electro‐mobility application. Highlights A holistic overview of the RBS control system architecture and its various control systems is reviewed. Various brake energy control strategies like Fuzzy, MPC, NN, SMC, Adaptive and learning‐based controls are critically evaluated. The discussion of necessary calibration process and its associated parameters are elucidated. Representation of real‐time design and development process of an efficient RBS in EV. Some of the prominent challenges faced during design and development of RBS and scope of future improvement is suggested.

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Wheel slip control with torque blending using linear and nonlinear model predictive control

Cited by 69 publications

References 27 publications

Explicit Nonlinear Model Predictive Control for Electric Vehicle Traction Control

Explicit Nonlinear Model Predictive Control for Electric Vehicle Traction Control

An Explicit Nonlinear Model Predictive ABS Controller for Electro-Hydraulic Braking Systems

Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

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