On the Feedback Control of Hitch Angle through Torque-Vectoring

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

On the enhancement of vehicle handling and energy efficiency of electric vehicles with multiple motors: the iCOMPOSE project

Electric vehicles with multiple motors allow torque-vectoring, i.e., the individual control of each powertrain torque. Torque-vectoring (TV) can provide: i) enhancement of vehicle safety and handling, via the generation of a direct yaw moment to shape the understeer characteristics and increase yaw and sideslip damping; and ii) energy consumption reductions, via appropriate torque allocation to each motor. The FP7 European project iCOMPOSE thoroughly addressed i) and ii). Theoretical analyses were carried out to design state-of-the art TV controllers, which were validated through: a) vehicle simulations; and b) extensive experimental tests, which were performed at rolling road facilities and proving grounds, using a Range Rover Evoque prototype equipped with four identical on-board electric powertrains. This paper provides an overview of the TV-related contributions of iCOMPOSE

On pre-emptive vehicle stability control

Future vehicle localisation technologies enable major enhancements of vehicle dynamics control. This study proposes a novel vehicle stability control paradigm, based on pre-emptive control that considers the curvature profile of the expected path ahead in the computation of the reference direct yaw moment and braking control action. The additional information allows pre-emptive trail braking control, which slows down the vehicle if the predicted speed profile based on the current torque demand is deemed incompatible with the reference trajectory ahead. Nonlinear model predictive control is used to implement the approach, in which also the steering angle and reference yaw rate provided to the internal model are varied along the prediction horizon, to account for the expected vehicle path. Two pre-emptive stability control configurations with different levels of complexity are proposed and compared with the passive vehicle, and two state-of-the-art nonlinear model predictive stability controllers, one with and one without non-pre-emptive trail braking control. The performance is assessed along obstacle avoidance tests, simulated with a high-fidelity model of an electric vehicle with in-wheel motors. Results show that the pre-emptive controllers achieve higher maximum entry speeds – up to ∼34% and ∼60% in high and low tyre-road friction conditions – than the formulations without preview.This work was supported in part by the Horizon 2020 Framework Programme of the European Commission under grant agreements no. 769944 (STEVE project) and no. 824311 (ACHILES project)

Articulated vehicle stability control using brake-based torque vectoring on trailer using nonlinear model predictive control

Unstable articulated vehicles pose a serious threat to the occupants driving them as well as the occupants of the vehicles around them. Articulated vehicles typically experience three types of instability: snaking, jack-knifing, and rollover. An articulated vehicle subjected to any of these instabilities can result in major accidents. In this study a Nonlinear Model Predictive Control (NMPC) that applies brake-based torque vectoring on the trailer is developed to improve the articulated vehicle stability. The NMPC formulation includes tire saturation and applies constraints to prevent rollover. The controller output is a left and right brake force allowing the longitudinal velocity change to be incorporated into the model. Simulations were conducted to instigate snaking and jack-knifing and show the NMPC controller result compared to a simple proportional controller. The NMPC controller can prevent these instabilities and improves the overall handling and safety of the articulated vehicle.https://saemobilus.sae.org/content/V125-2EJhj2024Mechanical and Aeronautical EngineeringNon

Heavy commercial vehicle yaw control simulation

The aim of this article is to present universal multibody dynamic model of the heavy commercial vehicle equipped with direct yaw moment control system. The presented simulation method is based on interconnection of the multibody software ADAMS and the graphical programming environment MATLAB Simulink. The main task is to demonstrate the potential effects of the direct yaw moment control using an active differential by heavy commercial vehicle with rear wheel drive

Multi-axle Vehicle Modeling and Stability Control: A Reconfigurable Approach

Multi-axle vehicles, such as trucks and buses, have been playing a vital role in trucking industry, public transportation system, and long-distance transport services. However, at the same time, statistics suggest more than one million lives are lost in road accidents each year over the world. The high adoption and utilization of multi-axle vehicles hold a significant portion of road accidents and death. To improve the active safety of vehicles, active systems have been developed and commercialized over the last decades to augment the driver's actions. However, unlike two-axle vehicles (e.g., passenger cars), multi-axle vehicles come in a rich diversity and variety to meet with many different transportation needs. Specifically, vehicle configurations are seen in different numbers of axles, numbers of articulations, powertrain modes, and active actuation systems. In addition, multi-axle vehicles are usually articulated, which makes the dynamics and control more complex and challenging as more instability modes appear, such as, trailer sway and jackknife. This research is hence motivated by an essential question: how can a universal and reconfigurable control system be developed for any multi-axle/articulated vehicle with any configuration? Leveraging the matrix approach and optimization-based techniques, this thesis developed a reconfigurable and universal modeling and control framework to this aim. Specifically, a general dynamics modeling that unifies any multi-axle and articulated vehicles in one formulation is developed in an intuitive manner. It defines the `Boolean Matrices' to determine any configuration of the articulation, the number of axles, and the active actuation systems. In this way, the corresponding dynamics model can be easily and quickly formulated when axles, articulations or actuators are added or removed. The general modeling serves to achieve the universality and reconfigurability in controller design. Therefore, a hierarchical, i.e., two-layer, control system is proposed. In the high layer, the optimization process of a model predictive control (MPC) calculates corrective Center of Gravity (CG) forces/moments, which are universal to any vehicle. The lower-level controller is achieved by a Control Allocation (CA) algorithm. It aims to realize the MPC commands by regulating the steering or torque (driving or braking) at each wheel optimally. In addition, the optimization takes into account real-time constraints, such as actuator limits, tire capacity, wheel slips, and actuators failure. Simulations are conducted on different vehicle configurations to evaluate control performance, reconfigurability, and robustness of the system. Additionally, to evaluate the real-time performance of the developed controller, experimental validation is carried out on an articulated vehicle with multiple configurations of differential braking systems. It is observed that the controller is very effective in dynamics control and has a promising reconfigurability when moving from one configuration to another

Optimal slip control for tractors with feedback of drive torque

Traction efficiency of tractors barely reaches 50 % in field operations. On the other hand, modern trends in agriculture show growth of the global tractor markets and at the same time increased demands for greenhouse gas emission reduction as well as energy efficiency due to increasing fuel costs. Engine power of farm tractors is growing at 1.8 kW per year reaching today about 500 kW for the highest traction class machines. The problem of effective use of energy has become crucial. Existing slip control approaches for tractors do not fulfil this requirement due to fixed reference set-point. The present work suggests an optimal control scheme based on set-point optimization and on assessment of soil conditions, namely, wheel-ground parameter identification using fuzzy-logic-assisted adaptive unscented Kalman filter.:List of figures VIII List of tables IX Keywords XI List of abbreviations XII List of mathematical symbols XIII Indices XV 1 Introduction 1 1.1 Problem description and challenges 1 1.1.1 Development of agricultural industry 1 1.1.2 Power flows and energy efficiency of a farm tractor 2 1.2 Motivation 9 1.3 Purpose and approach 12 1.3.1 Purpose and goals 12 1.3.2 Brief description of methodology 14 1.3.2.1 Drive torque feedback 14 1.3.2.2 Measurement signals 15 1.3.2.3 Identification of traction parameters 15 1.3.2.4 Definition of optimal slip 15 1.4 Outline 16 2 State of the art in traction management and parameter estimation 17 2.1 Slip control for farm tractors 17 2.2 Acquisition of drive torque feedback 23 2.3 Tire-ground parameter estimation 25 2.3.1 Kalman filter 25 2.3.2 Extended Kalman filter 27 2.3.3 Unscented Kalman filter 27 2.3.4 Adaptation algorithms for Kalman filter 29 3 Modelling vehicle dynamics for traction control 31 3.1 Tire-soil interaction 31 3.1.1 Forces in wheel-ground contact 32 3.1.1.1 Vertical force 32 3.1.1.2 Tire-ground surface geometry 34 3.1.2 Longitudinal force 36 3.1.3 Zero-slip condition 37 3.1.3.1 Soil shear stress 38 3.1.3.2 Rolling resistance 39 3.2 Vehicle body and wheels 40 3.2.1 Short description of Multi-Body-Simulation 40 3.2.2 Vehicle body and wheel models 42 3.2.3 Wheel structure 43 3.3 Stochastic input signals 45 3.3.1 Influence of trend and low-frequency components 47 3.3.2 Modelling stochastic signals 49 3.4 Further components and general view of tractor model 53 3.4.1 Generator, intermediate circuit, electrical motors and braking resistor 53 3.4.2 Diesel engine 55 4 Identification of traction parameters 56 4.1 Description of identification approaches 56 4.2 Vehicle model 58 4.2.1 Vehicle longitudinal dynamics 58 4.2.2 Wheel rotational dynamics 59 4.2.3 Tire dynamic rolling radius and inner rolling resistance coefficient 60 4.2.4 Whole model 61 4.3 Static methods of parameter identification 63 4.4 Adaptation mechanism of the unscented Kalman filter 63 4.5 Fuzzy supervisor for the adaptive unscented Kalman filter 66 4.5.1 Structure of the fuzzy supervisor 67 4.5.2 Stability analysis of the adaptive unscented Kalman filter with the fuzzy supervisor 69 5 Optimal slip control 73 5.1 Approaches for slip control by means of traction control system 73 5.1.1 Feedback compensation law 73 5.1.2 Sliding mode control 74 5.1.3 Funnel control 77 5.1.4 Lyapunov-Candidate-Function-based control, other approaches and choice of algorithm 78 5.2 General description of optimal slip control algorithm 79 5.3 Estimation of traction force characteristic curves 82 5.4 Optimal slip set-point computation 85 6 Verification of identification and optimal slip control systems 91 6.1 Simulation results 91 6.1.1 Identification of traction parameters 91 6.1.1.1 Comparison of extended Kalman filter and unscented Kalman filter 92 6.1.1.2 Comparison of ordinary and adaptive unscented Kalman filters 96 6.1.1.3 Comparison of the adaptive unscented Kalman filter with the fuzzy supervisor and static methods 99 6.1.1.4 Description of soil conditions 100 6.1.1.5 Identification of traction parameters under changing soil conditions 101 6.1.2 Approximation of characteristic curves 102 6.1.3 Slip control with reference of 10% 103 6.1.4 Comparison of operating with fixed and optimal slip reference 104 6.2 Experimental verification 108 6.2.1 Setup and description of the experiments 108 6.2.2 Virtual slip control without load machine 109 6.2.3 Virtual slip control with load machine 113 7 Summary, conclusions and future challenges 122 7.1 Summary of results and discussion 122 7.2 Contributions of the dissertation 123 7.3 Future challenges 123 Bibliography 125 A Measurement systems 137 A.1 Measurement of vehicle velocity 137 A.2 Measurement of wheel speed 138 A.3 Measurement or estimation of wheel vertical load 139 A.4 Measurement of draft force 140 A.5 Further possible measurement systems 141 B Basic probability theoretical notions 142 B.1 Brief description of the theory of stochastic processes 142 B.2 Properties of stochastic signals 144 B.3 Bayesian filtering 145 C Modelling stochastic draft force and field microprofile 147 D Approximation of kappa-curves 152 E Simulation parameters 15

Stability Control of Triple Trailer Vehicles

While vehicle stability control is a well-established technology in the passenger car realm, it is still an area of active research for commercial vehicles as indicated by the recent notice of proposed rulemaking on commercial vehicle stability by the National Highway Traffic Safety Administration (NHTSA, 2012). The reasons that commercial vehicle electronic stability control (ESC) development has lagged passenger vehicle ESC include the fact that the industry is generally slow to adopt new technologies and that commercial vehicles are far more complex requiring adaptation of existing technology. From the controller theory perspective, current commercial vehicle stability systems are generally passenger car based ESC systems that have been modified to manage additional brakes (axles). They do not monitor the entire vehicle nor do they manage the entire vehicle as a system

Trailer Sway Control Using an Active Hitch

The handling and yaw stability characteristics of passenger vehicles are drastically changed when towing a trailer, which can lead to unsafe oscillations in the trailer yaw, known as trailer sway. This thesis examines the feasibility of using lateral articulation of the hitch ball to reduce sway behavior in passenger-sized tractor-trailer configurations. An articulating hitch ball design has the advantage of not being dependent on the trailer being towed, providing stability improvements to the wide variety of trailers that a passenger vehicle may tow over its life cycle. Changes in the lateral position of the hitch relative to the tractor create dynamic changes to the heading angle of the trailer relative to the tractor, which act as compensating steering inputs into the system. To examine the effectiveness of this method, a linear handling model was developed to predict the system response with different trailer configurations and feedback methods. This model was simulated with various feedback controllers, and the modeling was validated using a model constructed in MapleSim, a high-fidelity multibody simulation tool. After establishing the required performance characteristics of the active hitch, a prototype was designed, manufactured, and tested in a full scale tractor-trailer combination. The modeling techniques showed good agreement with the physical testing, where the control design of proportional feedback on the trailer articulation angle provided improved yaw stability across many trailer configurations. The simple controller design is adaptable to driving conditions and requires minimal measurements of vehicle states. The performance of the active hitch prototype is best shown in a response to a steering impulse at 65km/h, where a highly unstable trailer causes steady state oscillation without control, and settles in under 4 seconds with control active

Trailer Sway Control Using an Active Hitch

The handling and yaw stability characteristics of passenger vehicles are drastically changed when towing a trailer, which can lead to unsafe oscillations in the trailer yaw, known as trailer sway. This thesis examines the feasibility of using lateral articulation of the hitch ball to reduce sway behavior in passenger-sized tractor-trailer configurations. An articulating hitch ball design has the advantage of not being dependent on the trailer being towed, providing stability improvements to the wide variety of trailers that a passenger vehicle may tow over its life cycle. Changes in the lateral position of the hitch relative to the tractor create dynamic changes to the heading angle of the trailer relative to the tractor, which act as compensating steering inputs into the system. To examine the effectiveness of this method, a linear handling model was developed to predict the system response with different trailer configurations and feedback methods. This model was simulated with various feedback controllers, and the modeling was validated using a model constructed in MapleSim, a high-fidelity multibody simulation tool. After establishing the required performance characteristics of the active hitch, a prototype was designed, manufactured, and tested in a full scale tractor-trailer combination. The modeling techniques showed good agreement with the physical testing, where the control design of proportional feedback on the trailer articulation angle provided improved yaw stability across many trailer configurations. The simple controller design is adaptable to driving conditions and requires minimal measurements of vehicle states. The performance of the active hitch prototype is best shown in a response to a steering impulse at 65km/h, where a highly unstable trailer causes steady state oscillation without control, and settles in under 4 seconds with control active