Scalable Slip Control With Torque Vectoring Including Input-to-State Stability Analysis

Degel, Wolfgang; Lupberger, Stefan; Odenthal, Dirk; Bajçinca, Naim

doi:10.1109/tcst.2022.3224839

Cited by 1 publication

(1 citation statement)

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“…Despite its performance being evaluated in a high-fidelity simulation environment, the MPCC controller has never been extended or compared with a controller that utilises torque vectoring capabilities. However, new control techniques utilising torque vectoring have become particularly attractive with the commercialisation of new electric powertrain configurations, especially those based on multiple in-wheel electric motors [10], [11].…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Bayesian optimisation for Automatic Tuning of an Unscented Kalman Filter for Vehicle Sideslip Angle Estimation

A.¹,

Shyrokau²,

Alirezaei³

et al. 2022

2022 IEEE Intelligent Vehicles Symposium (IV)

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This paper presents a novel methodology to autotune an Unscented Kalman Filter (UKF). It involves using a Two-Stage Bayesian Optimisation (TSBO), based on a t-Student Process to optimise the process noise parameters of a UKF for vehicle sideslip angle estimation. Our method minimises performance metrics, given by the average sum of the states' and measurement' estimation error for various vehicle manoeuvres covering a wide range of vehicle behaviour. The predefined cost function is minimised through a TSBO which aims to find a location in the feasible region that maximises the probability of improving the current best solution. Results on an experimental dataset show the capability to tune the UKF in 79.9 % less time than using a genetic algorithm (GA) and the overall capacity to improve the estimation performance in an experimental test dataset of 9.9 % to the current state-of-the-art GA.

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Section: Introductionmentioning

confidence: 99%