Feedforward control of an active seat for dynamic driving simulators

The Active Seat system is used to overcome the lacking of low-frequency sustained accelerations on dynamic driving simulators. The system provides artificial pressure cues to trick the driver's sensory system to feel an increased acceleration. Different ways are explored: the reproduction of the pressure stimuli of a real vehicle or the creation of an haptic feedback of the acceleration vector. The control schemes are developed taking into account the pressure induced by the platform movement

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

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

Occupant–vehicle dynamics and the role of the internal model

With the increasing need to reduce time and cost of vehicle development there is increasing advantage in simulating mathematically the dynamic interaction of a vehicle and its occu- pant. The larger design space arising from the introduction of automated vehicles further increases the potential advantage. The aim of the paper is to outline the role of the internal model hypothesis in understanding and modelling occupant-vehicle dynamics, specifically the dynamics associated with direction and speed control of the vehicle. The internal model is the driver’s or passenger’s understanding of the vehicle dynamics and is thought to be employed in the perception, cognition and action processes of the brain. The internal model aids the estimation of the states of the vehicle from noisy sensory measurements. It can also be used to optimise cognitive control action by predicting the consequence of the action; thus model predictive control theory (MPC) provides a foundation for modelling the cognition process. The stretch reflex of the neuromuscular system also makes use of the prediction of the internal model. Extensions to the MPC approach are described which account for: interaction with an automated vehicle; robust control; intermittent control; and cognitive workload. Further work to extend understanding of occupant-vehicle dynamic interaction is outlined. This paper is based on a keynote presentation given by the author to the 13th International Symposium on Advanced Vehicle Control (AVEC) conference held in Munich, September 2016

Entwicklung eines Force Cueing Algorithmus für Sitzpolster Druckaktuatoren zum Einsatz im DLR Robotic Motion Simulator

Im Rahmen der Arbeit wurde ein Ansteuerungsalgorithmus für die Aktuatoren eines G-Seats fur den DLR Robotic Motion Simulator (RMS) entwickelt. Bei den Aktuatoren handelt es sich um bewegliche Klappen unter den Sitzpolstern, die Druck auf den Körper des Insassen ausüben. Dadurch wird ein Bewegungseindruck erzeugt. Zunächst wurden dafür die Motoren eines Hardware Prototyps mithilfe des echtzeitfähigen Bussystems EtherCat in Betrieb genommen. Es konnte gezeigt werden, dass die Motoren bis zu einer Frequenz von 2Hz den kompletten Arbeitsbereich ohne Einschränkung befahren können. Bei höheren Frequenzen fällt die maximal befahrbare Amplitude deutlich ab. Zusätzlich wurde die Datenkommunikation in Echtzeit zwischen dem Force Cueing Algorithmus und dem C++ Programm, über welches die EtherCat-Schnittstelle realisiert wurde, über einen Modelnet-Server implementiert. Der entwickelte Force Cueing Algorithmus arbeitet mit einem klassischen Washout Filter. Außerdem projiziert er den eingehenden Beschleunigungsvektor auf die Kraftrichtungen der einzelnen Aktuatoren. Diese verändern sich aufgrund der Klappendynamik während des Betriebs ständig. Der Algorithmus kann den G-Seat sowohl als alleinigen Aktuator als auch in Kombination mit dem Motion Cueing des RMS ansteuern. In zweiterem Fall bildet der G-Seat nur jene Beschleunigungen ab, welche nicht durch den RMS ausgeführt werden. Beim Testen des Algorithmus mit einfachen Eingangsfunktionen und mit einer Flugtrajektorie konnte gezeigt werden, dass plausible Klappenbewegungen erzeugt werden können. Außerdem wurde festgestellt, dass der G-Seat abhängig von der verwendeten Konfiguration und der Position der Klappen manche Beschleunigungen nicht darstellen kann. Zusammenfassend lässt sich sagen, dass die entwickelte Struktur aus Force Cueing Algorithmus und Ansteuerung des Prototyps eine sehr gute Grundlage für weitere Arbeiten am G-Seat liefert

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

A Case Study on Vestibular Sensations in Driving Simulators

Motion platforms have been used in simulators of all types for several decades. Since it is impossible to reproduce the accelerations of a vehicle without limitations through a physically limited system (platform), it is common to use washout filters and motion cueing algorithms (MCA) to select which accelerations are reproduced and which are not. Despite the time that has passed since their development, most of these algorithms still use the classical washout algorithm. In the use of these MCAs, there is always information that is lost and, if that information is important for the purpose of the simulator (the training simulators), the result obtained by the users of that simulator will not be satisfactory. This paper shows a case study where a BMW 325Xi AUT fitted with a sensor, recorded the accelerations produced in all degrees of freedom (DOF) during several runs, and data have been introduced in mathematical simulation software (washout + kinematics + actuator simulation) of a 6DOF motion platform. The input to the system has been qualitatively compared with the output, observing that most of the simulation adequately reflects the input to the system. Still, there are three events where the accelerations are lost. These events are considered by experts to be of vital importance for the outcome of a learning process in the simulator to be adequat

Motion cueing in driving simulators for research applications

This research investigated the perception of self-motion in driving simulation, focussing on the dynamic cues produced by a motion platform. The study was undertaken in three stages, evaluating various motion cueing techniques based on both subjective ratings of realism and objective measures of driver performance. Using a Just Noticeable Difference methodology, Stage 1 determined the maximum perceptible motion scaling for platform movement in both translation and tilt. Motion cues scaled by 90% or more could not be perceptibly differentiated from unscaled motion. This result was used in Stage 2‟s examination of the most appropriate point in space at which the platform translations and rotations should be centred (Motion Reference Point, MRP). Participants undertook two tracking tasks requiring both longitudinal (braking) and lateral (steering) vehicle control. Whilst drivers appeared unable to perceive a change in MRP from head level to a point 1.1m lower, the higher position (closer to the vestibular organs) did result in marginally smoother braking, corresponding to the given requirements of the longitudinal driving task. Stage 3 explored the perceptual trade-off between the specific force error and tilt rate error generated by the platform. Three independent experimental factors were manipulated: motion scale-factor, platform tilt rate and additional platform displacement afforded by a XY-table. For the longitudinal task, slow tilt that remained sub-threshold was perceived as the most realistic, especially when supplemented by the extra surge of the XY-table. However, braking task performance was superior when a more rapid tilt was experienced. For the lateral task, perceived realism was enhanced when motion cues were scaled by 50%, particularly with added XY-sway. This preference was also supported by improvements in task accuracy. Participants ratings were unmoved by changing tilt rate, although rapid tilt did result in more precise lane control. Several interactions were also observed, most notably between platform tilt rate and XY-table availability. When the XY-table was operational, driving task performance varied little between sub-threshold and more rapid tilt. However, while the XY-table was inactive, both driving tasks were better achieved in conditions of high tilt rate. An interpretation of these results suggests that without the benefit of significant extra translational capability, priority should be given to the minimisation of specific force error through motion cues presented at a perceptibly high tilt rate. However, XY-table availability affords the simulator engineer the luxury of attaining a slower tilt that provides both accurate driving task performance and accomplishes maximum perceived realism

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 »

Measurement and Modelling of Human Sensory Feedback in Car Driving

Data supporting this thesis is available at https://doi.org/10.17863/CAM.9741With the growing complexity of vehicle control systems it is becoming increasingly important to understand the interaction between drivers and vehicles. Existing driver models do not adequately characterise limitations resulting from drivers’ physical systems. In particular, sensory dynamics limit the ability of drivers to perceive the states of real or simulated vehicles. Therefore, the aim of this thesis is to understand the impact of sensory dynamics on the control performance of a human driver in real and virtual environments. A new model of driver steering control is developed based on optimal control and state estimation theory, incorporating models of sensory dynamics, delays and noise. Some results are taken from published literature, however recent studies have shown that sensory delays and noise amplitudes may increase during an active control task such as driving. Therefore, a parameter identification procedure is used to fit the model predictions to measured steering responses of real drivers in a simulator. The model is found to fit measured results well under a variety of conditions. An initial experiment is designed with the physical motion of the simulator matching the motion of the virtual vehicle at full scale. However, during more realistic manoeuvres the physical motion must be scaled or filtered, introducing conflicts between measurements from different sensory systems. Drivers are found to adapt to simple conflicts such as scaled motion, but they have difficulty adapting to more complicated motion filters. The driver model is initially derived for linear vehicles with stochastic target and disturbance signals. In later chapters it is extended to account for transient targets and disturbances and vehicles with nonlinear tyres, and validated once again with experimental results. A series of simulations is used to demonstrate novel insights into how drivers use sensory information, and the resulting impact on control performance. The new model is also shown to predict difficulties real drivers have controlling unstable vehicles more reliably than existing driver models.This work was funded by the UK Engineering and Physical Sciences Research Council (EPSRC), under studentship EP/P505445/1