Model-Based Control Techniques for Automotive Applications

Two different topics are covered in the thesis. Model Predictive Control applied to the Motion Cueing Problem In the last years the interest about dynamic driving simulators is increasing and new commercial solutions are arising. Driving simulators play an important role in the development of new vehicles and advanced driver assistance devices: in fact, on the one hand, having a human driver on a driving simulator allows automotive manufacturers to bridge the gap between virtual prototyping and on-road testing during the vehicle development phase; on the other hand, novel driver assistance systems (such as advanced accident avoidance systems) can be safely tested by having the driver operating the vehicle in a virtual, highly realistic environment, while being exposed to hazardous situations. In both applications, it is crucial to faithfully reproduce in the simulator the driver's perception of forces acting on the vehicle and its acceleration. This has to be achieved while keeping the platform within its limited operation space. Such strategies go under the name of Motion Cueing Algorithms. In this work, a particular implementation of a Motion Cueing algorithm is described, that is based on Model Predictive Control technique. A distinctive feature of such approach is that it exploits a detailed model of the human vestibular system, and consequently differs from standard Motion Cueing strategies based on Washout Filters: such feature allows for better implementation of tilt coordination and more efficient handling of the platform limits. The algorithm has been evaluated in practice on a small-size, innovative platform, by performing tests with professional drivers. Results show that the MPC-based motion cueing algorithm allows to effectively handle the platform working area, to limit the presence of those platform movements that are typically associated with driver motion sickness, and to devise simple and intuitive tuning procedures. Moreover, the availability of an effective virtual driver allows the development of effective predictive strategies, and first simulation results are reported in the thesis. Control Techniques for a Hybrid Sport Motorcycle Reduction of the environmental impact of transportation systems is a world wide priority. Hybrid propulsion vehicles have proved to have a strong potential to this regard, and different four-wheels solutions have spread out in the market. Differently from cars, and even if they are considered the ideal solution for urban mobility, motorbikes and mopeds have not seen a wide application of hybrid propulsion yet, mostly due to the more strict constraints on available space and driving feeling. In the thesis, the problem of providing a commercial 125cc motorbike with a hybrid propulsion system is considered, by adding an electric engine to its standard internal combustion engine. The aim for the prototype is to use the electrical machine (directly keyed on the drive shaft) to obtain a torque boost during accelerations, improving and regularizing the supplied power while reducing the emissions. Two different control algorithms are proposed 1) the first is based on a standard heuristic with adaptive features, simpler to implement on the ECU for the prototype; 2) the second is a torque-split optimal-control strategy, managing the different contributions from the two engines. A crucial point is the implementation of a Simulink virtual environment, realized starting from a commercial tool, VI-BikeRealTime, to test the algorithms. The hybrid engine model has been implemented in the tool from scratch, as well as a simple battery model, derived directly from data-sheet characteristics by using polynomial interpolation. The simulation system is completed by a virtual rider and a tool for build test circuits. Results of the simulations on a realistic track are included, to evaluate the different performance of the two strategies in a closed loop environment (thanks to the virtual rider). The results from on-track tests of the real prototype, using the first control strategy, are reported too

Complex perception models applied to non-linear MPC based Motion Cueing Algorithms

Almost everyone had suffered from sickness on occasion when travelling as it passenger in an auto, ship, or aircraft. The objective of the present effort is to analyse some mathematical models for conflict generation in motion sickness and to implement them in an efficient way that can make possible their use in nowadays practical applications, such as driving simulators

Investigation of vibration’s effect on driver in optimal motion cueing algorithm

The increased sensation error between the surroundings and the driver is a major problem in driving simulators, resulting in unrealistic motion cues. Intelligent control schemes have to be developed to provide realistic motion cues to the driver. The driver’s body model incorporates the effects of vibrations on the driver’s health, comfort, perception, and motion sickness, and most of the current research on motion cueing has not considered these factors. This article proposes a novel optimal motion cueing algorithm that utilizes the driver’s body model in conjunction with the driver’s perception model to minimize the sensation error. Moreover, this article employs H1 control in place of the linear quadratic regulator to optimize the quadratic cost function of sensation error. As compared to state of the art, we achieve decreased sensation error in terms of small root-mean-square difference (70%, 61%, and 84% decrease in case of longitudinal acceleration, lateral acceleration, and yaw velocity, respectively) and improved coefficient of cross-correlation (3% and 1% increase in case of longitudinal and lateral acceleration, respectively)

Feasibility Analysis For Constrained Model Predictive Control Based Motion Cueing Algorithm

This paper deals with motion control for an 8-degree-of-freedom (DOF) high performance driving simulator. We formulate a constrained optimal control that defines the dynamical behavior of the system. Furthermore, the paper brings together various methodologies for addressing feasibility issues arising in implicit model predictive control-based motion cueing algorithms. The implementation of different techniques is described and discussed subsequently. Several simulations are carried out in the simulator platform. It is observed that the only technique that can provide ensured closed-loop stability by assuring feasibility over all prediction horizons is a braking law that basically saturates the control inputs in the constrained form

Eine Studie über State-of-the-Art Motion-Cueing-Algorithmen angewendet auf Planar Motion mit Pure Lateral Acceleration - Vergleich, Auto-Tuning und subjektive Bewertung auf einem KUKA Robocoaster Serial Ride Simulator

Driving simulators are widely used during the training of drivers/pilots, and for entertainment, as well as in the research of human behavior. Motion cueing algorithms (MCAs) are aimed at mapping the motion of moving vehicles into the limited workspace of driving simulators, while preserving the perceptual realism of the simulation. Several MCAs have been developed in the literature to improve the realism of the simulation. However, each MCA presents strengths and weaknesses when compared to the others. Most importantly, the tuning of the MCA parameters is an open issue because these parameters are not intuitive for normal simulator users. The current dissertation considers a systematic comparison of existing MCAs based on a simple maneuver with only lateral (left/right) accelerations, i.e. a trajectory along a planar S-shaped track with constant velocity. All the MCAs were implemented and compared numerically using a novel measure – the well-tuned index. In a later stage, the trajectories of the MCAs were implemented in the KUKA Robocoaster and assessed by a group of 17 test-subjects. The results of the subjective assessment were compared with the numerical metrics. Furthermore, in the thesis an auto-tuning process based on the mean-variance mapping optimization (MVMO) and numerical perception scores was developed by which one is able to automatically tune the parameters to obtain a high well-tuned index, reducing drastically the current high manual tuning times. It was observed that, for a serial robot, the circular motion of the cabin can be well-compensated by the pitch angle. Among the MCAs, the optimal tracking algorithm (ZyRo) and the model predictive control (MPC*) can simulate large amplitude input signals while keeping a high value of the well-tuned index. Furthermore, these algorithms exploit the simulator’s workspace better than the other MCAs and are easily tuned. The ZyRo algorithm produces similar results as the MPC* algorithm, but requires less computational time. The responses of all the MCAs included in this study are similar when using a same low scaling factor (k = 0.4). However, if a larger scale factor is used, the responses of the MCAs change significantly. Finally, it was concluded that a good correlation between the average subjective scores and the objective measures can be achieved. However, due to the large variability of the individual scores, further research is needed to better understand the subjective ratings of simulation rides.Fahrsimulatoren werden für die Schulung von Fahrern/Piloten, in Fahrgeschäften, sowie in der Forschung des menschlichen Verhaltens verwendet. Motion-Cueing-Algorithmen (MCAs) zielen darauf ab, die Bewegung von fahrenden Fahrzeugen in dem begrenzten Arbeitsbereich der Fahrsimulatoren abzubilden und gleichzeitig den Wahrnehmungsrealismus der Simulation zu bewahren. In der Literatur wurden mehrere MCAs entwickelt, um den Realismus der Simulation zu verbessern. Jedoch weist jeder MCA Stärken und Schwächen im Vergleich zu den anderen. Dabei ist die Abstimmung der MCA-Parameter kritisch, da die Parameter für normale Simulator-Benutzer nicht intuitiv sind. In dieser Dissertation wird ein systematischer Vergleich zwischen bestehenden MCAs anhand eines einfachen Manövers mit nur seitlichen (links / rechts) Beschleunigungen, d.h. entlang einer ebenen S-förmigen Spur mit konstanter Geschwindigkeit durchgeführt. Alle MCAs wurden implementiert und numerisch mit einem neuartigen Maß - dem „gut abgestimmten Index“ („well-tuned index“) – miteinander verglichen. In einem späteren Stadium wurden die Trajektorien der MCAs im KUKA Robocoaster implementiert und von einer Gruppe von 17 Versuchspersonen bewertet. Die Ergebnisse der subjektiven Bewertung wurden dargestellt und mit den numerischen Metriken verglichen. In der Dissertation wird auch eine neue Auto-Tuning Methode entwickelt, die die Mittel-Varianz-Mapping-Optimierung (Mean Variance Mapping Optimization = MVMO) sowie numerische Empfindungscores verwendet, mit der die Parameter automatisch abgestimmt und damit hohe Werte des Bewegungsempfindungsindex erzielt werden können. Dadurch entfallen die sehr zeitaufwändigen manuellen Abstimmungsarbeiten bei gleicher oder besserer Bewegungsqualität. Es wurde beobachtet, dass für einen seriellen Roboter die Kreisbewegung der Kabine durch den Nickwinkel gut kompensiert werden kann. Unter den MCAs konnten der optimale Tracking-Algorithmus (ZyRo) und die modellbasierte prädiktive Regelung (MPC*) große Amplituden des Eingangssignals unter Erzielung eines hohen Wert des „gut abgestimmten Index“ simulieren. Darüber hinaus nutzten diese Algorithmen den Arbeitsbereich des Simulators besser als die anderen MCAs und waren leichter abzustimmen. Der ZyRo-Algorithmus erzeugt ähnliche Ergebnisse wie der MPC*-Algorithmus, erfordert aber weniger Rechenzeit. Die Bewegungseigenschaften aller in dieser Studie enthaltenen MCAs sind bei der Verwendung des gleichen niedrigen Skalierungsfaktors (k = 0,4) ähnlich. Wenn jedoch ein größerer Skalierungsfaktor verwendet wird, ändern sich die Antworten der MCAs signifikant. Zum Schluss wurde festgestellt, dass eine gute Korrelation zwischen den durchschnittlichen subjektiven Bewertungen und den objektiven Maßnahmen erreicht werden kann. Allerdings sind aufgrund der großen Variabilität der einzelnen Bewertungen weitere Untersuchungen erforderlich, um die subjektiven Bewertungen der Fahrsimulationen besser zu verstehen

Driving Simulator Motion Cueing Assessment: A Platform Design Perspective

The overall aim of this thesis was to study the effects of a simulator’s motion system on vestibular motion cueing fidelity in different contexts, evaluated in terms of drivers’ perception and behaviour, in low and high road friction conditions. The effects of manipulating the motion cueing algorithm (MCA), was found to be a function of the vehicle motion in a manoeuvre, and significant effects were observed. The applicability of simulators for the assessment of vehicle driven attribute qualities such as ride, steering and handling were studied by manipulating vehicle ride height (RH). The differences between the RHs were subjectively distinguishable by the drivers in the simulator. Incongruities between the subjective preferences and objective performances were observed in both of the independent comparisons of the MCAs and RHs. The effects of motion platform (MP) workspace size were found to be dependent on the manoeuvres and road friction level. In the low-friction condition, with the increase of MP size, two opposite effects were observed on drivers’ preferences and their performances, depending on the manoeuvre. In high-friction, in most of the handling and steering qualities, a direct relation was found between the MP size and appropriate vehicle RH. Furthermore, the optimal tuning of the MCAs and optimisation of the MP workspace size was introduced. A conservative motion cueing fidelity criteria was defined. A multi-layered optimisation method was developed that uses the optimal setting of the MCA, to address the MP translational workspace size, and to meet the fidelity criteria; applicable for different manoeuvres. This method was tested on the drivers’ performance data collected from the experiments in the simulator

Algorithms and Applications for Nonlinear Model Predictive Control with Long Prediction Horizon

Fast implementations of NMPC are important when addressing real-time control of systems exhibiting features like fast dynamics, large dimension, and long prediction horizon, as in such situations the computational burden of the NMPC may limit the achievable control bandwidth. For that purpose, this thesis addresses both algorithms and applications. First, fast NMPC algorithms for controlling continuous-time dynamic systems using a long prediction horizon have been developed. A bridge between linear and nonlinear MPC is built using partial linearizations or sensitivity update. In order to update the sensitivities only when necessary, a Curvature-like measure of nonlinearity (CMoN) for dynamic systems has been introduced and applied to existing NMPC algorithms. Based on CMoN, intuitive and advanced updating logic have been developed for different numerical and control performance. Thus, the CMoN, together with the updating logic, formulates a partial sensitivity updating scheme for fast NMPC, named CMoN-RTI. Simulation examples are used to demonstrate the effectiveness and efficiency of CMoN-RTI. In addition, a rigorous analysis on the optimality and local convergence of CMoN-RTI is given and illustrated using numerical examples. Partial condensing algorithms have been developed when using the proposed partial sensitivity update scheme. The computational complexity has been reduced since part of the condensing information are exploited from previous sampling instants. A sensitivity updating logic together with partial condensing is proposed with a complexity linear in prediction length, leading to a speed up by a factor of ten. Partial matrix factorization algorithms are also proposed to exploit partial sensitivity update. By applying splitting methods to multi-stage problems, only part of the resulting KKT system need to be updated, which is computationally dominant in on-line optimization. Significant improvement has been proved by giving floating point operations (flops). Second, efficient implementations of NMPC have been achieved by developing a Matlab based package named MATMPC. MATMPC has two working modes: the one completely relies on Matlab and the other employs the MATLAB C language API. The advantages of MATMPC are that algorithms are easy to develop and debug thanks to Matlab, and libraries and toolboxes from Matlab can be directly used. When working in the second mode, the computational efficiency of MATMPC is comparable with those software using optimized code generation. Real-time implementations are achieved for a nine degree of freedom dynamic driving simulator and for multi-sensory motion cueing with active seat

Eco-Driving Strategy Implementation for Ultra-Efficient Lightweight Electric Vehicles in Realistic Driving Scenarios

This paper aims to provide a quantitative assessment of the effect of driver action and road traffic conditions in the real implementation of eco-driving strategies. The study specifically refers to an ultra-efficient battery-powered electric vehicle designed for energy-efficiency competitions. The method is based on the definition of digital twins of vehicle and driving scenario. The models are used in a driving simulator to accurately evaluate the power demand. The vehicle digital twin is built in a co-simulation environment between VI-CarRealTime and Simulink. A digital twin of the Brooklands Circuit (UK) is created leveraging the software RoadRunner. After validation with actual telemetry acquisitions, the model is employed offline to find the optimal driving strategy, namely, the optimal input throttle profile, which minimizes the energy consumption over an entire lap. The obtained reference driving strategy is used during real-time driving sessions at the dynamic driving simulator installed at Politecnico di Milano (DriSMi) to include the effects of human driver and road traffic conditions. Results assess that, in a realistic driving scenario, the energy demand could increase more than 20% with respect to the theoretical value. Such a reduction in performance can be mitigated by adopting eco-driving assistance systems

Mechatronics integration for a vehicle simulator.

Master of Science in Mechanical Engineering. University of KwaZulu-Natal, Pietermaritzburg 2016.This dissertation presents the research and integration of a mechatronics system to be used in a vehicle simulator. The vehicle simulator is comprised of a 3-DOF platform which is used to provide motion cues to the driver. Kinematic analysis is performed on the 3-DOF system and this analysis assists in implementing platform motion control. To recreate the motion sensations experienced in an actual vehicle while respecting the platform workspace limits the classical washout algorithm is implemented in the vehicle simulator. A novel simulation system was contributed in Matlab/Simulink to aid in vehicle simulator design. This simulation setup incorporates all the motion cueing aspects; these aspects include input vehicle data scaling, the classical washout algorithm and inverse kinematic analysis. The developed simulation system was used to adjust the motion cueing parameters to ensure motion that respects the actuator motion constraints. These constraints ensure the vehicle simulator is operated safely. A second contribution used the developed simulation system in Matlab/Simulink and the human vestibular system models. A performance evaluation was performed on the 3-DOF system against the traditional 6-DOF system. The results highlight the benefits of the 3-DOF system in replication of certain motion cues. Software was developed to receive input game data and output actuator stroke lengths to the motion control system. Limitations in the motion control system were found when testing was done on the vehicle simulator. These limitations led to a modified partial 2-DOF vehicle simulator. The motion control hardware is able to replicate actuator motion well. The final vehicle simulator system is a partial 2-DOF system that provides visual and motion cues that create a realistic driving experience. The developed system is suitable for applications with cost constraints and reasonable performance requirements

Contributions to shared control and coordination of single and multiple robots

L’ensemble des travaux présentés dans cette habilitation traite de l'interface entre un d'un opérateur humain avec un ou plusieurs robots semi-autonomes aussi connu comme le problème du « contrôle partagé ».Le premier chapitre traite de la possibilité de fournir des repères visuels / vestibulaires à un opérateur humain pour la commande à distance de robots mobiles.Le second chapitre aborde le problème, plus classique, de la mise à disposition à l’opérateur d’indices visuels ou de retour haptique pour la commande d’un ou plusieurs robots mobiles (en particulier pour les drones quadri-rotors).Le troisième chapitre se concentre sur certains des défis algorithmiques rencontrés lors de l'élaboration de techniques de coordination multi-robots.Le quatrième chapitre introduit une nouvelle conception mécanique pour un drone quadrirotor sur-actionné avec pour objectif de pouvoir, à terme, avoir 6 degrés de liberté sur une plateforme quadrirotor classique (mais sous-actionné).Enfin, le cinquième chapitre présente une cadre général pour la vision active permettant, en optimisant les mouvements de la caméra, l’optimisation en ligne des performances (en terme de vitesse de convergence et de précision finale) de processus d’estimation « basés vision »