Cooperative look-ahead control for fuel-efficient and safe heavy-duty vehicle platooning

The operation of groups of heavy-duty vehicles (HDVs) at a small inter-vehicular distance (known as platoon) allows to lower the overall aerodynamic drag and, therefore, to reduce fuel consumption and greenhouse gas emissions. However, due to the large mass and limited engine power of HDVs, slopes have a significant impact on the feasible and optimal speed profiles that each vehicle can and should follow. Therefore maintaining a short inter-vehicular distance as required by platooning without coordination between vehicles can often result in inefficient or even unfeasible trajectories. In this paper we propose a two-layer control architecture for HDV platooning aimed to safely and fuel-efficiently coordinate the vehicles in the platoon. Here, the layers are responsible for the inclusion of preview information on road topography and the real-time control of the vehicles, respectively. Within this architecture, dynamic programming is used to compute the fuel-optimal speed profile for the entire platoon and a distributed model predictive control framework is developed for the real-time control of the vehicles. The effectiveness of the proposed controller is analyzed by means of simulations of several realistic scenarios that suggest a possible fuel saving of up to 12% for the follower vehicles compared to the use of standard platoon controllers.Comment: 16 pages, 16 figures, submitted to journa

Improving yaw dynamics by feedforward rear wheel steering

Active rear wheel steering can be applied to improve vehicle yaw dynamics. In this paper two possible control algorithms are discussed. The first method is a yaw rate feedback controller with a reference model, which has been reported in a similar form previously in literature. The second controller is a feedforward controller, which only requires the front wheel steering angle and vehicle forward velocity. It has a similar performance as the feedback controller. Both controllers are evaluated using an enhanced bicycle model, which includes tyre relaxation behaviour and suspension steering compliance

An error bound for model reduction of Lur'e-type systems

In general, existing model reduction techniques for stable nonlinear systems lack a guarantee on stability of the reduced-order model, as well as an error bound. In this paper, a model reduction procedure for absolutely stable Lur’e-type systems is presented, where conditions to ensure absolute stability of the reduced-order model as well as an error bound are given. The proposed model reduction procedure exploits linear model reduction techniques for the reduction of the linear part of the Lur’e-type system. Hence, the proposed model reduction strategy is computationally attractive. Moreover, both stability and the error bound for the obtained reduced-order model hold for an entire class of nonlinearities. The results are illustrated by application to a nonlinear mechanical system

Error estimation in reduced basis method for systems with time-varying and nonlinear boundary conditions

Many physical phenomena, such as mass transport and heat transfer, are modeled by systems of partial differential equations with time-varying and nonlinear boundary conditions. Control inputs and disturbances typically affect the system dynamics at the boundaries and a correct numerical implementation of boundary conditions is therefore crucial. However, numerical simulations of high-order discretized partial differential equations are often too computationally expensive for real-time and many-query analysis. For this reason, model complexity reduction is essential. In this paper, it is shown that the classical reduced basis method is unable to incorporate time-varying and nonlinear boundary conditions. To address this issue, it is shown that, by using a modified surrogate formulation of the reduced basis ansatz combined with a feedback interconnection and a input-related term, the effects of the boundary conditions are accurately described in the reduced-order model. The results are compared with the classical reduced basis method. Unlike the classical method, the modified ansatz incorporates boundary conditions without generating unphysical results at the boundaries. Moreover, a new approximation of the bound and a new estimate for the error induced by model reduction are introduced. The effectiveness of the error measures is studied through simulation case studies and a comparison with existing error bounds and estimates is provided. The proposed approximate error bound gives a finite bound of the actual error, unlike existing error bounds that grow exponentially over time. Finally, the proposed error estimate is more accurate than existing error estimates

Vehicle State Estimator based regenerative braking implementation on an electric vehicle to improve lateral vehicle stability

The driving range of electric vehicles can be extended using regenerative braking. Regenerative braking uses the electric drive system, and therefore only the driven wheels, for decelerating the vehicle. Braking on one axle affects the stability of the vehicle, especially for road conditions with reduced friction. This paper discusses three control strategies for preventing loss of stability while applying regenerative braking, two of which are using a state estimation algorithm developed by TNO. Experiments have been conducted with a front wheel driven vehicle on a low friction test track. The conclusions concerning the control concepts are however based on simulation results, due to unexpected system behaviour of the test vehicle. The results also indicate that the effectiveness of regenerative braking can be improved in cornering situations by using the vehicle yaw rate as a control signal. Due to hardware limitations, it has not been possible to rank the performance of the individual regenerative braking controllers in practise. It is recommended to further study the control concepts using an improved hardware setup

Modeling, analysis and control of a variable geometry actuator

A new design of variable geometry force actuator is presented in this paper. Based upon this design, a model is derived which is used for steady-state analysis, as well as controller design in the presence of friction. The controlled actuator model is finally used to evaluate the power consumption under worst case conditions. © 2008 IEEE

Passivity-Preserving, Balancing-Based Model Reduction for Interconnected Systems

This paper proposes a balancing-based model reduction approach for an interconnection of passive dynamic subsystems. This approach preserves the passivity and stability of both the subsystems and the interconnected system. Hereto, one Linear Matrix Inequality (LMI) per subsystem and a single Lyapunov equation for the entire interconnected system needs to be solved, the latter of which warrants the relevance of the reduction of the subsystems for the accurate reduction of the interconnected system, while preserving the modularity of the reduction approach. In a numerical example from structural dynamics, the presented approach displays superior accuracy with respect to an approach in which the individual subsystems are reduced independently.Comment: 6 pages, 4 figures, to appear in Proceedings of IFAC World Congress 202

Error estimates for model order reduction of Burgers' equation

Burgers' equation is a nonlinear scalar partial differential equation, commonly used as a testbed for model order reduction techniques and error estimates. Model order reduction of the parameterized Burgers' equation is commonly done by using the reduced basis method. In this method, an error estimate plays a crucial rule in both accelerating the offline phase and quantifying the error induced after reduction in the online phase. In this study, we introduce two new estimates for this reduction error. The first error estimate is based on a Lur'e-type model formulation of the system obtained after the full-discretization of Burgers' equation. The second error estimate is built upon snapshots generated in the offline phase of the reduced basis method. The second error estimate is applicable to a wider range of systems compared to the first error estimate. Results reveal that when conditions for the error estimates are satisfied, the error estimates are accurate and work efficiently in terms of computational effort