46 research outputs found
labcontrol-data/vehicleCAN: Second release Vehicle CAN (source code updated April 2021)
No full textCode and experimental data for the paper: Stability of asynchronous sampled-data systems with input delay: application to an automotive throttle valve'.
Authors: Alessandro N. Vargas, Frederic Mazenc, Constantin F. Caruntu, and Matthew M. Pee
Corrigendum to “Driveline oscillations damping: A tractable predictive control solution based on a piecewise affine model”
No full textThe authors regret that the above-referenced published journal paper omitted to indicate that an initial version of the results was reported in the conference paper [1], “A Predictive Control Solution for Driveline Oscillations Damping”, by C.F. Caruntu, A.E. Balau, M. Lazar, P.P.J. v.d. Bosch, and S. Di Cairano, which was presented at the 2011 Hybrid Systems: Computation and Control Conference, Chicago, Illinois, USA. The authors are grateful to Dr. A.E. Balau for indicating this omission and the fact that the above–referenced published journal paper, even if providing additional contributions, largely builds upon the results presented in the conference paper [1]. In order to rectify these issues, to account for the contribution of all the authors of the conference paper [1] to the underlying proposed modeling and control framework, and to explain the differences with respect to [1], the following (purely textual) modifications to the List of Authors, Abstract, Introduction, Conclusions, Acknowledgements and References were made: Modifications to List of Authors: The authors list: “Constantin F. Caruntu, Mircea Lazar, Stefano Di Cairano” was replaced by the authors list: “Constantin F. Caruntu, Andreea E. Balau, Mircea Lazar, Paul P.J. v.d. Bosch, Stefano Di Cairano” Modifications to Abstract: The following sentences: “The first contribution of this paper is the derivation of an accurate piecewise affine drivetrain model with three inertias. The second contribution is concerned with the design of a horizon-1 predictive controller based on flexible Lyapunov functions.” were replaced with the following sentence: The contribution of this paper is to demonstrate that horizon-1 model predictive control based on flexible Lyapunov functions and piecewise affine drivetrain models with three inertias provides an effective solution to driveline oscillation damping. Modifications to Introduction: The following sentences: “The first contribution of this paper is the development of a PWA model for an automotive driveline, which brings several improvements with respect to the above mentioned models, by considering the driving load given by the airdrag torque, gravity and rolling resistance, and four modes to describe the clutch dynamics. Taking into account all these factors yields a more accurate model of the driveline dynamics.” were replaced with the following sentences: This paper makes use of the PWA model with three inertias for an automotive driveline introduced in [1], and further developed in [2], which brings several improvements with respect to the above mentioned models, by considering the driving load given by the airdrag torque, gravity and rolling resistance, and four modes to describe the clutch dynamics. The first contribution of this paper is to further validate the accuracy of this PWA model with three inertias, compared to other affine and PWA driveline models of the driveline. The following sentences: “As such, the second contribution of this paper is to make use of a recently introduced design method for horizon-1 MPC, based on flexible control Lyapunov functions [30], with the aim of reducing the driveline oscillations. The algorithm therein has the potential to satisfy the timing requirements, due to the short horizon, while it can still offer a non-conservative solution to stabilization due to the flexibility of the Lyapunov function. Several simulation scenarios defined in collaboration with Ford Research and Advanced Engineering, US validate the proposed approach and indicate that the proposed scheme, besides yielding a feasible algorithm, outperforms controllers obtained using typical approaches, such as PID control.” were replaced with the following sentences: As such, this paper makes use of an alternative design method for horizon-1 MPC [1], based on flexible control Lyapunov functions [30], with the aim of reducing driveline oscillations. Due to the short horizon, the algorithm has the potential to satisfy the timing requirements while it can still offer a non-conservative solution to stabilization due to the flexibility of the Lyapunov function. The second contribution is to provide an extensive comparison of the horizon-1 predictive controller with two PID controllers, for several simulation scenarios defined in collaboration with Ford Research and Advanced Engineering, US. The obtained results validate the proposed approach and indicate that the proposed scheme, besides yielding a feasible algorithm, outperforms standard PID controllers. Before the paragraph: “The remainder of the paper is structured as follows. In Section 2, a PWA model of the drivetrain is presented, which considers both driveshaft and clutch flexibilities. Then, in Section 3, the predictive control strategy based on flexible Lyapunov functions is designed. Section 4 presents extensive simulation results, while conclusions are summarized in Section 5.” the following paragraph was added: A preliminary version of this work appeared in the conference paper [1]. Additionally to the results presented in [1], this paper provides an extensive validation of the PWA model (Section 2.2.1.), a realistic comparison with two PID controllers (Section 4.1), it includes several observations useful for the practical relevance of the model (reported in Remark 2.1, Remark 2.2, Remark 4.3), it provides updated simulations (Section 4) and additional relevant references. Modifications to Conclusions: The following sentence: “As such, firstly, a novel, more accurate state-space piecewise affine drivetrain model with three inertias was derived, in which both driveshafts and clutch flexibilities were considered.” was replaced by the following sentence: As such, firstly, a more accurate state-space piecewise affine drivetrain model with three inertias is used, in which both driveshafts and clutch flexibilities were considered. Modifications to Acknowledgements: The following sentence: “The authors are grateful to Prof. C. Lazar for his contribution in the drivetrain modeling phase and the reset condition development and to Dr. A. Balau and Prof. P.P.J. van den Bosch for the useful comments and suggestions.” was replaced by: The authors are grateful to Prof. C. Lazar for his contribution in the drivetrain modeling phase and the reset condition development. Modifications to References: The references [1] and [2] were added to the reference list
A Less Conservative Condition for Flexible Control Lyapunov Functions used in Networked Predictive Control Systems
No full textDistributed predictive control for wind farms efficiency maximization: challenges and opportunities
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Wireless vehicle-to-infrastructure data gathering for robot platooning
No full textAbstract: This paper presents firstly the development of the infrastructure to emulate and verify the behavior of robots in a platooning scenario based on mOway mobile robots. Radio Frequency communications and how they are handled in both ends are the keys in this distributed wireless vehicle-to-infrastructure (V2I) data gathering implementation. Secondly, the paper investigates a state-space model predictive control strategy for platoon guidance using only longitudinal changes for the automatically controlled robots. The control strategy was implemented and tested in simulation and in real-time, while the data gathered through the distributed wireless V2I system was used to monitor the mobile robots on-line and afterwards to analyze the behavior of the mobile robot platoon. Studying robot platooning and the relationship with the communication issues allows one to understand the dynamics of the platoon and, therefore, to develop a suitable traffic control strategy
Cruise and headway control for vehicle platooning using a distributed model predictive control algorithm
No full textMultiple-lane vehicle platooning based on a multi-agent distributed model predictive control strategy
No full textVehicle platooning became an interesting topic in the last years, many researchers and practitioners from the academia and industry trying to develop new theories and design appropriate control methods and communication methodologies in order to bring this concept as fast as possible on the roads. Since vehicles drive on multi-lane roads and highways, the subsequent paradigm was to treat vehicles as swarms, i. e., groups of vehicles that travel closely together on different lanes and are electronically connected. A step forward towards this new concept would be the design of multiple-lane platoons. As such, this paper proposes a multi-agent distributed model predictive control strategy for the longitudinal coordination of the vehicles in individual platoons and a classical PI control algorithm for the lateral control of each vehicle in the platoon w. r. t. its neighbors. The simulation results obtained in Matlab/Simulink and the performance analysis prove that the concept is viable
Coalitional Distributed Model Predictive Control Strategy with Switching Topologies for Multi-Agent Systems
No full textControlling multi-agent systems (MASs) has attracted increased interest within the control community. Since the control challenge consists of the fact that each agent has limited local capabilities, our adopted solution is tailored so that a group of such entities works together and shares resources and information to fulfill a given task. In this work, we propose a coalitional control solution using the distributed model predictive control (DMPC) framework, suitable for a multi-agent system. The methodology has a switching mechanism that selects the best communication topology for the overall system. The proposed control algorithm was validated in simulation using a homogeneous vehicle platooning application with longitudinal dynamics. The available communication topologies were specifically tailored taking into account the information flow between adjacent vehicles. The obtained results show that when the platoon’s string stability is risked, the algorithm switches between different communication topologies. The resulting coalitions between vehicles ensure an increase in the overall stability of the entire system and prove the efficacy of our proposed methodology
