Zanchetta Mattia, Tavernini Davide, Sorniotti Aldo, Gruber Patrick, Lenzo B., Ferrara A., De Nijs W., Sannen K., De Smet J. (2018) On the Feedback Control of Hitch Angle through Torque-Vectoring,2018 IEEE 15th International Workshop on Advanced Motion Control (AMC)pp. 535-540
Institute of Electrical and Electronics Engineers (IEEE)
This paper describes a torque-vectoring (TV)
algorithm for the control of the hitch angle of an articulated
vehicle. The hitch angle control function prevents trailer
oscillations and instability during extreme cornering maneuvers.
The proposed control variable is a weighted combination of terms
accounting for the yaw rate, sideslip angle and hitch angle of the
articulated vehicle. The novel control variable formulation results
in a single-input single-output (SISO) feedback controller. In the
specific application a simple proportional integral (PI) controller
with gain scheduling on vehicle velocity is developed. The TV
system is implemented and experimentally tested on a fully electric
vehicle with four on-board drivetrains, towing a single-axle passive
trailer. Sinusoidal steer test results show that the proposed
algorithm significantly improves the behavior of the articulated
vehicle, and justify further research on the topic of hitch angle
control through TV.
Steering control for path tracking in autonomous vehicles is well documented in the literature. Also, continuous direct yaw moment control, i.e., torque-vectoring, applied to human-driven electric vehicles with multiple motors is extensively researched. However, the combination of both controllers is not yet well understood. This paper analyzes the benefits of torque-vectoring in an autonomous electric vehicle, either by integrating the torque-vectoring system in the path tracking controller, or through its separate implementation alongside the steering controller for path tracking. A selection of path tracking controllers is compared in obstacle avoidance tests simulated with an experimentally validated vehicle dynamics model. A genetic optimization is used to select the controller parameters. Simulation results confirm that torque-vectoring is beneficial to autonomous vehicle response. The integrated controllers achieve the best performance if they are tuned for the specific tire-road friction condition. However, they can also cause unstable behavior when they operate in lower friction conditions without any re-tuning. On the other hand, separate torque-vectoring implementations provide consistently stable cornering response for a wide range of friction conditions. Controllers with preview formulations, or based on appropriate reference paths with respect to the middle line of the available lane, are beneficial to the path tracking performance.
In electric vehicles with multiple motors, the individual wheel torque control, i.e., the so-called torque-vectoring, significantly enhances the cornering response and active safety. Torque-vectoring can also increase energy efficiency, through the appropriate design of the reference understeer characteristic and the calculation of the wheel torque distribution providing the desired total wheel torque and direct yaw moment. To meet the industrial requirements for real vehicle implementation, the energy-efficiency benefits of torque-vectoring should be achieved via controllers characterised by predictable behaviour, ease of tuning and low computational requirements. This paper discusses a novel energy-efficient torque-vectoring algorithm for an electric vehicle with in-wheel motors, which is based on a set of rules deriving from the combined consideration of: i) the experimentally measured electric powertrain efficiency maps; ii) a set of optimisation results from a non-linear quasi-static vehicle model, including the computation of tyre slip power losses; and iii) drivability requirements for comfortable and safe cornering response. With respect to the same electric vehicle with even wheel torque distribution, the simulation results, based on an experimentally validated vehicle dynamics simulation model, show: a) up to 4% power consumption reduction during straight line operation at constant speed; b) >5% average input power saving in steady-state cornering at lateral accelerations >3.5/m/s2; and c) effective compensation of the yaw rate and sideslip angle oscillations during extreme transient tests.
Electric vehicles with independently controlled
drivetrains allow torque-vectoring, which enhances active safety
and handling qualities. This paper proposes an approach for the
concurrent control of yaw rate and sideslip angle based on a single
input single output (SISO) yaw rate controller. With the SISO
formulation, the reference yaw rate is firstly defined according to
the vehicle handling requirements, and is then corrected based on
the actual sideslip angle. The sideslip angle contribution
guarantees a prompt corrective action in critical situations such as
incipient vehicle oversteer during limit cornering in low tire-road
friction conditions. A design methodology in the frequency domain
is discussed, including stability analysis based on the theory of
switched linear systems. The performance of the control structure
is assessed via: i) phase-plane plots obtained with a non-linear
vehicle model; ii) simulations with an experimentally validated
model, including multiple feedback control structures; and iii)
experimental tests on an electric vehicle demonstrator along step
steer maneuvers with purposely induced and controlled vehicle
drift. Results show that the SISO controller allows constraining
the sideslip angle within the predetermined thresholds and yields
tire-road friction adaptation with all the considered feedback
This paper investigates a torque-vectoring formulation for the combined control of the yaw rate and hitch angle of an articulated vehicle through a direct yaw moment generated on the towing car. The formulation is based on a single-input single-output feedback control structure, in which the reference yaw rate for the car is modified when the incipient instability of the trailer is detected with a hitch angle sensor. The design of the hitch angle controller is described, including the gain scheduling as a function of vehicle speed. The controller performance is assessed by means of frequency domain and phase plane analyses, and compared with that of an industrial trailer sway mitigation algorithm. In addition, the novel control strategy is implemented in a high-fidelity articulated vehicle model for robustness assessment, and experimentally tested on an electric vehicle demonstrator with four on-board drivetrains, towing two different conventional single-axle trailers. The results show that: (i) the torque-vectoring controller based only on the yaw rate of the car is not sufficient to mitigate trailer instability in extreme conditions; and (ii) the proposed controller provides safe trailer behaviour during the comprehensive set of manoeuvres, thus justifying the additional hardware complexity associated with the hitch angle measurement.
In vehicle dynamics, yaw rate control is used to improve the cornering response in steady-state and transient conditions. This can be achieved through an appropriate anti-roll moment distribution between the front and rear axles of a vehicle with controllable suspension actuators. Such control action alters the load transfer distribution, which in turn provokes a lateral tire force variation. With respect to the extensive set of papers from the literature discussing yaw rate tracking through active suspension control, this study presents: i) A detailed analysis of the effect of the load transfer on the lateral axle force and cornering stiffness; ii) A novel linearized single-track vehicle model formulation for control system design, based on the results in i); and iii) An optimization-based routine for the design of the non-linear feedforward contribution of the control action. The resulting feedforward-feedback controller is assessed through: a) Simulations with an experimentally validated model of a vehicle with active anti-roll bars (case study 1); and b) Experimental tests on a vehicle prototype with an active suspension system (case study 2).
In the last decades autonomous vehicles have been at the centre of the research in both the academic and the industrial fields, but not without difficulties. In particular, the problem of path planning and tracking at the limit of the handling capabilities of a vehicle poses many challenges from a control perspective, and it is yet to be understood whether the integration with stability controllers can improve the cornering performance of autonomous vehicles as much as it does for human drivers. This thesis aims to provide insights on these topics. The first part of the work is dedicated to the planning and tracking layers of an autonomous vehicle driving on racetracks. The analysis covers the offline optimisation of the trajectory and the description of a re-planning algorithm for the avoidance of obstacles. A comparison among several path tracking controllers is then provided, to understand whether the gain in performance obtained from advanced controllers justifies the design complexity. In the second part, the thesis highlights the benefits of yaw rate control on the behaviour of over-actuated vehicles. An algorithm for yaw rate control is introduced and implemented in a torque vectoring controller, and the proof of asymptotic stability of the system is provided. Several application examples are presented, with simulation and experimental results that demonstrate the potential and versatility of yaw rate control. Finally, the integration of torque vectoring and path tracking control in an autonomous racing vehicle is presented and assessed with a simulation study along obstacle avoidance tests. The results of the thesis show that: i) including road preview information in path tracking controllers improves the control action, resulting in better vehicle behaviour, and ii) torque vectoring control always improves the vehicle performance, and it also enhances the system robustness to variations in the tyre-road friction coefficient.