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

Association of kidney disease measures with risk of renal function worsening in patients with type 1 diabetes

Background: Albuminuria has been classically considered a marker of kidney damage progression in diabetic patients and it is routinely assessed to monitor kidney function. However, the role of a mild GFR reduction on the development of stage 653 CKD has been less explored in type 1 diabetes mellitus (T1DM) patients. Aim of the present study was to evaluate the prognostic role of kidney disease measures, namely albuminuria and reduced GFR, on the development of stage 653 CKD in a large cohort of patients affected by T1DM. Methods: A total of 4284 patients affected by T1DM followed-up at 76 diabetes centers participating to the Italian Association of Clinical Diabetologists (Associazione Medici Diabetologi, AMD) initiative constitutes the study population. Urinary albumin excretion (ACR) and estimated GFR (eGFR) were retrieved and analyzed. The incidence of stage 653 CKD (eGFR < 60 mL/min/1.73 m2) or eGFR reduction > 30% from baseline was evaluated. Results: The mean estimated GFR was 98 \ub1 17 mL/min/1.73m2 and the proportion of patients with albuminuria was 15.3% (n = 654) at baseline. About 8% (n = 337) of patients developed one of the two renal endpoints during the 4-year follow-up period. Age, albuminuria (micro or macro) and baseline eGFR < 90 ml/min/m2 were independent risk factors for stage 653 CKD and renal function worsening. When compared to patients with eGFR > 90 ml/min/1.73m2 and normoalbuminuria, those with albuminuria at baseline had a 1.69 greater risk of reaching stage 3 CKD, while patients with mild eGFR reduction (i.e. eGFR between 90 and 60 mL/min/1.73 m2) show a 3.81 greater risk that rose to 8.24 for those patients with albuminuria and mild eGFR reduction at baseline. Conclusions: Albuminuria and eGFR reduction represent independent risk factors for incident stage 653 CKD in T1DM patients. The simultaneous occurrence of reduced eGFR and albuminuria have a synergistic effect on renal function worsening

A real time implementation of MPC based motion cueing strategy for driving simulators

none3Driving simulators are widely used in many different applications, such as driver training, vehicle development, and medical studies. To fully exploit the potential of such devices, it is crucial to develop platform motion control strategies that generate realistic driving feelings. 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 paper 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. The algorithm has been evaluated experimentally 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 to driver motion sickness, and to devise simple and intuitive tuning procedures.noneA. Beghi; M. Bruschetta; F. MaranBeghi, Alessandro; Bruschetta, Mattia; Maran, Fabi

A fast implementation of MPC-based motion cueing algorithms for mid-size road vehicle motion simulators

The use of dynamic driving simulators is constantly increasing in the automotive community, with applications ranging from vehicle development to rehab and driver training. The effectiveness of such devices is related to their capabilities of well reproducing the driving sensations, hence it is crucial that the motion control strategies gen- erate both realistic and feasible inputs to the platform. Such strate- gies are called motion cueing algorithms (MCAs). In recent years several MCAs based on model predictive control (MPC) techniques have been proposed. The main drawback associated with the use of MPC is its computational burden, that may limit their application to high performance dynamic simulators. In the paper, a fast, real-time implementation of an MPC-based MCA for 9 DOF, high performance platform is proposed. Effectiveness of the approach in managing the available working area is illustrated by presenting experimen- tal results from an implementation on a real device with a 200 Hz control frequency

A real-time implementation of an MPC-based motion cueing strategy with time-varying prediction

Dynamic driving simulators are seeing an increasing interest in the automotive community, both in the research and industrial fields. Different aspects are involved, from virtual prototyping to rehab: racing applications are of particular interest, where simulators are exploited to improve the driver\u2019s capabilities and test different vehicle set-ups avoiding the costs of testing on real tracks. The ability of the platform in reproducing as faithfully as possible the driving feelings is crucial in such context: this is the task of Motion Cueing Algorithms. Recently, innovative MPC-based approaches have been proposed which improve the performance with respect to the classical, filteringbased procedures. The criticality in such approaches is the prediction phase, both for the derivation of reliable reference signals and the computational burden of the optimal problem. In this paper, a first strategy is proposed to derive an affordable reference exploiting the repetitive pattern typical of the racing context, while a move-blocking approach is integrated to assure real-time capabilities

A non-linear MPC based motion cueing implementation for a 9 DOFs dynamic simulator platform.

The use of dynamical driving simulators is nowadays common practice in many different application fields, such as driver training, vehicle development, and medical studies. Platforms with different mechanical structure are designed, depending on the particular application and the corresponding targeted market. The effectiveness of such devices is related to their capabilities of well reproducing the driving feelings, hence it is crucial that the motion control strategies generate both realistic and feasible signals to the platform, to assure that it is kept within its limited operation space. Such strategies are called Motion Cueing Algorithms (MCAs), and they are clearly tailored to the particular mechanical structure of the device. In this paper we describe an MCA based on non linear Model Predictive Control (NMPC) techniques for a simulator of new conception, that consists of an hexapod over a flat base moved by a tripod, thus exhibiting highly non linear behaviour. The procedure is based on previous works where a linear, MPC-based MCA has been applied to a simpler device. The algorithm has been evaluated on a simulation environment, and a first implementation on the real device is in progress. Preliminary results show that a full exploitation of the working area is achieved, while managing at best all the limitations given by the particular structure and preserving the ease of tune and intuitiveness of the linear approach

A nonlinear, MPC-based motion cueing algorithm for a high-performance, nine-DOF dynamic simulator platform

The use of dynamical driving simulators is nowadays common in many different application fields, such as driver training, vehicle development, and medical studies. Platforms with different mechanical structures have been designed, depending on the particular application and the cor- responding targeted market. The effectiveness of such devices is related to their capabilities of well reproducing the driving sen- sations, and hence, it is crucial that the motion control strategies generate both realistic and feasible inputs to the platform, to ensure that it is kept within its limited operation space. Such strategies are called motion cueing algorithms (MCAs). In this brief, we describe an MCA based on nonlinear model predictive control (MPC) techniques, for a nine-degree of freedom simulator based on a hexapod mounted on a flat base moved by a tripod, exhibiting highly nonlinear behavior. The algorithm has been evaluated in a simulation environment. Simulation results show that the full exploitation of the working area is achieved, while managing at best all the limitations given by the particular structure and preserving the easiness and intuitiveness of tuning, which is typical of linear MPC-based approaches

A virtual environment for the design of power management strategies for hybrid motorcycles

In this paper, we consider the problem of providing a commercial 125cc motorbike with a hybrid propulsion system, 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, while reducing the emissions. Two different control algorithms are proposed, based on a standard heuristic and on torque-split optimal-control strategies, respectively. A SIMULINK virtual environment has been realized starting from a commercial tool, VI-BikeRealTime, to test the algorithms. Strategies performance is evaluated by means of simulations on a realistic track

A simulation environment for assessing power management strategies in hybrid motorcycles

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. 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 this paper, we consider the problem of providing a commercial 125cc motorbike with a hybrid propulsion system, 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, while reducing the emissions. Two different control algorithms are proposed, based on a standard heuristic and on torque-split optimal-control strategies, respectively. A Simulink virtual environment has been realized starting from a commercial tool, VI-BikeRealTime, to test the algorithms. The hybrid engine 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

A motion cueing algorithm with look-Ahead and driver characterization: Application to vertical car dynamics

Driving simulators are nowadays a widely used tool in the automotive industry. In particular, the need for safe and repeatable conditions in automated driving testing is now defining a new challenge: to extend the use of the tool to nonprofessional drivers. Quality of the motion control strategies in generating both realistic and feasible inputs to the driver is therefore, more than ever, a crucial aspect. The motion strategies are implemented in the so-called motion cueing algorithms (MCAs). A recently proposed effective approach to MCA is based on model predictive control (MPC), as it is well suited to solve constrained optimal control problems and to take advantage of models of the human sensing system. However, the predictive aspect of the algorithm has not been exploited yet, due to the hard real-time requirement when using long prediction windows. In this paper, a real-time implementation of an MPC-based MCA with predictive feature is presented, endowed with an on-line switching policy to a nonpredictive algorithm when the expected driver behavior is considered unreliable. The motion action based on the actual driver behavior and the expected one are considered in the same procedure, thus fully exploiting the availability of a perceptive model. An optimal tuning procedure is also proposed, based on a multiobjective optimization, where both performance improvement due to the prediction exploitation, and robustness to varying driver behaviour are considered. Finally, a characterization of the driver skill level is proposed and validated in an experimental environment for the specific case of the vertical DOF