Abstract

The operation of heavy-duty vehicles at small inter-vehicular distances, known as platoons, lowers the aerodynamic drag and, therefore, reduces fuel consumption and greenhouse gas emissions. Tests conducted on flat roads have shown the potential of platooning to reduce the fuel consumption of about 10%. However, platoons are expected to operate on public highways with varying topography alongside other vehicles. Due to the large mass and limited engine power of heavy-duty vehicles, road slopes have a significant impact on feasible and optimal speed profiles. For single vehicles, experiments have shown that optimizing the speed according to the road profile resulted in fuel saving of up to 3.5%. The use of such a look-ahead control framework is expected to lead to large benefits also for platooning. This thesis presents the design of safe and fuel-efficient control of heavy-duty vehicle platoons driving on realistic road profiles. The scenario where the platooning vehicles cooperate to optimize their overall fuel-efficiency is studied together with the scenario where the vehicles do not explicitly cooperate. First, we propose a control architecture that splits the cooperative platooning control problem into two layers. The top layer computes a reference speed profile that ensures fuel-efficient operation of the entire platoon based on dynamic programming. The bottom layer relies on model predictive control to safely track the reference speed. Simulations show the ability of the proposed controller to save up to 12% of fuel for following vehicles compared to existing platoon controllers and to safely react to emergency braking of the leading vehicle. Second, we propose a gear management layer that fits in the cooperative platooning control architecture and explicitly takes the gear selection into account. The underlying optimal control problem aims at minimizing the vehicle fuel consumption and the reference tracking deviations. Simulations indicate how this formulation outperforms existing alternatives, both in terms of fuel-efficiency and tracking error. Third, we address non-cooperative platooning by proposing a vehicle-following controller suitable for fuel-efficient control of heavy-duty vehicles. The proposed controller explores both the benefits given by the short inter-vehicular distance and those given by pulse-and-glide, i.e., alternating traction and coasting phases. A simulation study suggests fuel saving of up to 18% compared to the single vehicle case, and up to 7% compared to when a constant-distance vehicle-following controller is used. Last, we propose a vehicle-following controller aimed at exploiting long preview of the preceding vehicle trajectory by directly manipulating the inputs of low-level vehicle controllers. This is achieved through a model predictive controller that uses a short prediction horizon and includes a terminal state set that incorporates preview information about the preceding vehicle. Experiments indicate the ability of the controller to avoid unnecessary braking, while simulations show behavior similar to the optimal control behavior. "p"QC 20180416

Abstract

Safety in the operations of UAVs (Unmanned Aerial Vehicles) depends on the current and future reduction of technical barriers and on the improvements related to their autonomous capabilities. Since the early stages, aviation has been based on pilots and Air Traffic Controllers that take decisions to make aircraft follow their routes while avoiding collisions. RPA (Remotely Piloted Aircraft) can still involve pilots as they are UAVs controlled from ground, but need the definition of common rules, of a dedicated Traffic Controller and exit strategies in the case of lack of communication between the Ground Control Station and the aircraft. On the other hand, completely autonomous aircraft are currently banned from civil airspace, but researchers and engineers are spending great effort in developing methodologies and technologies to increase the reliability of fully autonomous flight in view of a safe and efficient integration of UAVs in the civil airspace. This paper deals with the design of a collision avoidance system based on a Distributed Model Predictive Controller (DMPC) for trajectory tracking, where anticollision constraints are defined in accordance with the Right of Way rules, as prescribed by the International Civil Aviation Organization (ICAO) for human piloted flights. To reduce the computational burden, the DMPC is formulated as a Mixed Integer Quadratic Programming optimization problem. Simulation results are shown to prove the effectiveness of the approach, also in the presence of a densely populated airspace.

Abstract

In this paper, a real-time distributed economic model predictive control approach for complete vehicle energy management (CVEM) is presented using a receding control horizon in combination with a dual decomposition. The dual decomposition allows the CVEM optimization problem to be solved by solving several smaller optimization problems. The receding horizon control problem is formulated with variable sample intervals, allowing for large prediction horizons with only a limited number of decision variables and constraints in the optimization problem. Furthermore, a novel on/off control concept for the control of the refrigerated semi-trailer, the air supply system and the climate control system is introduced. Simulation results on a low-fidelity vehicle model show that close to optimal fuel reduction performance can be achieved. The fuel reduction for the on/off controlled subsystems strongly depends on the number of switches allowed. By allowing up to 15-times more switches, a fuel reduction of 1.3% can be achieved. The approach is also validated on a high-fidelity vehicle model, for which the road slope is predicted by an e-horizon sensor, leading to a prediction of the propulsion power and engine speed. The prediction algorithm is demonstrated with measured ADASIS information on a public road around Eindhoven, which shows that accurate prediction of the propulsion power and engine speed is feasible when the vehicle follows the most probable path. A fuel reduction of up to 0.63% is achieved for the high-fidelity vehicle model. Document type: Article