| 1. |
- Aad, G., et al.
(author)
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- 2011
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swepub:Mat__t (peer-reviewed)
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| 2. |
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| 3. |
- Falcone, Paolo, 1977, et al.
(author)
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A Hierarchical Model Predictive Control Framework for Autonomous Ground Vehicles
- 2008
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In: American Control Conference. - 0743-1619. - 9781424420797 ; , s. 3719 - 3724
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Conference paper (peer-reviewed)abstract
- A hierarchical framework based on Model Predictive Control (MPC) for autonomous vehicles is presented. We formulate a predictive control problem in order to best follow a given path by controlling the front steering angle while fulfilling various physical and design constraints.We start from the low-level active steering-controller presented in [3], [9] and integrate it with a high level trajectory planner. At both levels MPC design is used. At the high-level, a trajectory is computed on-line, in a receding horizon fashion, based on a simplified point-mass vehicle model. At the low-level a MPC controller computes the vehicle inputs in order to best follow the desired trajectory based on detailed nonlinear vehicle model.This article presents the approach, the method for implementing it, and successful preliminary simulative results on slippery roads at high entry speed.
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| 4. |
- Falcone, Paolo, 1977, et al.
(author)
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A Model Predictive Control Approach for Combined Braking and Steering in Autonomous Vehicles
- 2007
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In: 15th Mediterranean Conference on Control and Automation, Athens, Greece, June 2007.
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Conference paper (peer-reviewed)abstract
- In this paper we present a Model Predictive Control (MPC) approach for combined braking and steering systems in autonomous vehicles. We start from the result presented in [1] and [2], where a Model Predictive Controller (MPC) for autonomous steering systems has been presented. We formulate a predictive control problem in order to best follow a given path by controlling the front steering angle and the brakes at the four wheels independently, while fulfilling various physical and design constraints.
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| 5. |
- Falcone, Paolo, 1977, et al.
(author)
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A Real-Time Model Predictive Control Approach for Autonomous Active Steering
- 2006
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In: First IFAC International workshop on NMPC for Fast Systems, Grenoble, France, October 2006.
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Conference paper (peer-reviewed)abstract
- The problem of controlling the front steering to stabilize a vehicle along a desired path is tackled in this paper. Although a Non-LinearModel Predictive Control (NLMPC) approach can achieve good performance and constraints fulfillment, its computational burden does not allow a real-time implementation. In order to decrease the complexity of the controller, in this paper we propose a suboptimal MPC scheme based on successive on-line linearizations of the non-linear vehicle model. The method stems from an accurate analysis of the vehicle nonlinearities, the constraints and the performance index in the optimal control problem. The simulation results show a significant reduction of the controller complexity, with a small loss of performances respect to a NLMPC controller. The suboptimal MPC control policy is compared to the control policy of a robot driver from Ford Motor Company.We show that better performance can be achieved with a smaller control effort, without violating vehicle physical constraints, by using a systematic control design procedure.
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| 6. |
- Falcone, Paolo, 1977, et al.
(author)
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Effects of Roll Dynamics in Model Predictive Control for Autonomous Vehicles
- 2008
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In: 47th IEEE Conference on Decision and Control, December 9-11, 2008, Fiesta Americana Grand Coral Beach, Cancun, Mexico.
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Conference paper (peer-reviewed)abstract
- A Model Predictive Control (MPC) approach for autonomous vehicles is presented. We formulate a predictive control problem in order to best follow a given path by controlling the front steering angle. We start from the results presented in [4] and [7], where the MPC problem formulationrelies on a simple bicycle model, and reformulate the problem by using a more complex vehicle model including roll dynamics. We present and discuss simulations of a vehicle performing high speed double lane change maneuvers where roll dynamics become relevant. The results demonstrate that the proposed model based design is able to effectively stabilize the vehicle by using a three dimensional vehicle model at the cost of a highercomputational load.
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| 7. |
- Falcone, Paolo, 1977, et al.
(author)
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Experimental Validation of Integrated Steering and Braking Model Predictive Control
- 2009
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In: International Journal of Vehicle Autonomous Systems. - 1471-0226 .- 1741-5306. ; 7:3/4, s. 292-309
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Journal article (peer-reviewed)abstract
- In this paper we present and discuss an in-vehicle experimental validation of an integrated steering and braking Model Predictive Control (MPC) algorithm. The experimental results are obtained in autonomous path following tests, where the vehicle has to autonomously perform a double lane change maneuver by simultaneously coordinating the front steering and the braking torques at the four wheels.The maneuvers are performed at high speed on slippery surfaces in order tooperate close to the vehicle stability limits.The proposed controller is an MPC based on successive on-line linearizations of the nonlinear vehicle model. Experimental tests of a double lane change maneuvers at 70 Kph are shown and complex countersteering maneuvers are presented and discussed.
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| 8. |
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| 9. |
- Falcone, Paolo, 1977, et al.
(author)
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Integrated Braking and Steering Model Predictive Control Approach in Autonomous Vehicles
- 2007
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In: 5-th IFAC Symposium on Advances in Automotive Control, Berkeley, CA, USA, August 2007.
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Conference paper (peer-reviewed)abstract
- In this paper we present a Model Predictive Control (MPC) approach for combined braking and steering systems in autonomous vehicles. We start from the result presented in (Borrelli et al. (2005)) and (Falcone et al. (2007a)), wherea Model Predictive Controller (MPC) for autonomous steering systems has been presented. As in (Borrelli et al. (2005)) and (Falcone et al. (2007a)) we formulate an MPC control problem in order to stabilize a vehicle along a desired path. In the present paper, the control objective is to best follow a given path by controlling the front steering angle and the brakes at the four wheels independently, while fulfilling various physical and design constraints.
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| 10. |
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| 11. |
- Falcone, Paolo, 1977, et al.
(author)
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Linear Time Varying Model Predictive Control and its Application to Active Steering Systems: Stability Analysis and Experimental Validation
- 2008
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In: International Journal of Robust and Nonlinear Control. - : Wiley. - 1099-1239 .- 1049-8923. ; 18:8, s. 862-875
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Journal article (peer-reviewed)abstract
- A Model Predictive Control (MPC) approach for controlling an Active Front Steering (AFS) system in an autonomous vehicle is presented. At each time step a trajectory is assumed to be known over a finite horizon, and an MPC controller computes the front steering angle in order to best follow the desired trajectory on slippery roads at the highest possible entry speed. We start from the results presented in [2], [6] and formulate the MPC problem based on successive on-line linearization of the nonlinear vehicle model (LTV MPC). We present a sufficient stability conditions for such LTV MPC scheme. The condition is derived for a general class of nonlinear discrete time systems and results into an additional convex constraint to be included in the LTV MPC design. For the AFS control problem, we compare the proposed LTV MPC scheme against the LTV MPC scheme in [6] where stability has been enforced with an ad-hoc constraint. Simulation and experimental tests up to 21 m/s on icy roads show the effectiveness of the LTV MPC formulation.
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| 12. |
- Falcone, Paolo, 1977, et al.
(author)
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Linear Time Varying Model Predictive Control Approach to the Integrated Vehicle Dynamics Control Problem in Autonomous Systems
- 2007
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In: 46th IEEE Conference on Decision and Control, New Orleans, LA, USA, December 2007.
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Conference paper (peer-reviewed)abstract
- A Model Predictive Control (MPC) approach for controlling active front steering, active braking and active differentials in an autonomous vehicle is presented. We formulate a predictive control problem in order to best follow a given path by controlling the front steering angle, brakes and traction at the four wheels independently, while fulfilling various physical and design constraints. At each time step a trajectory is assumed to be known over a finite horizon, and an MPC controller computes the system inputs in order to best follow the desired trajectory on slippery roads at a given entry speed. We start from the results presented in [1], [2] and formulate the MPC problem based on successive on-line linearization of the nonlinear vehicle model (LTV MPC). Simulative results are presented, interpreted and compared against LTV MPC schemes which make use only of steering and/or braking.
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| 13. |
- Falcone, Paolo, 1977, et al.
(author)
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Low Complexity MPC Schemes for Integrated Vehicle Dynamics Control Problems
- 2008
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In: 9th International Symposium on Advanced Vehicle Control (AVEC ’08).
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Conference paper (peer-reviewed)abstract
- A low complexity Model Predictive Control (MPC) approach to the problem of autonomous path following via combined steering and independent braking is presented in this paper. We start from the simpler approach in [5] and significantly improve the performance by better modeling the longitudinal dynamics and slightly increasing the number of optimization variables, i.e., the computational complexity. In order to assess the performance improvement, simulations are presented and compared against the results of the simpler approach in [5]. Moreover, experimental results are shown and discussed.
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| 14. |
- Falcone, Paolo, 1977, et al.
(author)
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MPC-Based Yaw and Lateral Stabilization Via Active Front Steering and Braking
- 2008
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In: Vehicle System Dynamics. - : Informa UK Limited. - 1744-5159 .- 0042-3114. ; 46, Supplement:SUPPL.1, s. 611-628
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Journal article (peer-reviewed)abstract
- In this paper we propose a path following Model Predictive Control-based (MPC) scheme utilizing steering and braking. The control objective is to track a desired path for obstacle avoidance maneuver, by a combined use of braking and steering. The proposed control scheme relies on the Nonlinear MPC (NMPC) formulation we used in [1] and [2]. In this work, the NMPC formulation will be used in order to derive two different approaches. The first relies on a full tenth order vehicle model and has high computational burden. The second approach is based on a simplified bicycle model and has a lower computational complexity compared to the first. The effectiveness of the proposed approaches is demonstrated through simulations and experiments.
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| 15. |
- Falcone, Paolo, 1977, et al.
(author)
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On Low Complexity Predictive Approaches to Control of Autonomous Vehicles
- 2010
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In: Lecture Notes in Control and Information Sciences. - London : Springer London. - 0170-8643. - 9781849960700 ; 402, s. 195-210
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Book chapter (other academic/artistic)abstract
- In this chapter we present low complexity predictive approaches to the control of autonomous vehicles. A general hierarchical architecture for fully autonomous vehicle guidance systems is presented together with a review of two control design paradigms. Our review emphasizes the trade off between performance and computational complexity at different control levels of the architecture. In particular, experimental results are presented, showing that if the controller at the lower level is properly designed, then it can handle system nonlinearities and model uncertainties even if those are not taken into account at the higher level.
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| 16. |
- Falcone, Paolo, 1977, et al.
(author)
-
On Low Complexity Predictive Approaches to Control of Autonomous Vehicles
- 2009
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In: Automotive Model Predictive Control: Models, Methods and Applications, Linz, Austria, 2009, Springer-Verlag..
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Book chapter (other academic/artistic)abstract
- In this paper we present low complexity predictive approaches to thecontrol of autonomous vehicles. A general hierarchical architecture for fully autonomousvehicle guidance systems is presented together with a review of two controldesign paradigms. Our review emphasizes the trade off between performanceand computational complexity at different control levels of the architecture. In particular,experimental results are presented, showing that if the controller at the lowerlevel is properly designed, then it can handle system nonlinearities and model uncertaintieseven if those are not taken into account at the higher level.
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| 17. |
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| 18. |
- Falcone, Paolo, 1977, et al.
(author)
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Predictive Active Steering Control for Autonomous Vehicle Systems
- 2007
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In: IEEE Transactions on Control Systems Technology. ; 15:3, s. 566-580
-
Journal article (peer-reviewed)abstract
- In this paper a Model Predictive Control (MPC) approach for controlling an Active Front Steering system in an autonomous vehicle is presented. At each time step a trajectory in assumed to be known over a finite horizon, and an MPC controller computes the front steering angle in order to follow the trajectory on slippery roads at the highest possible entry speed. We present two approaches with different computational complexities. In the first approach we formulate the MPC problem by using a non-linear vehicle model. The second approach is based on successive on-line linearization of the vehicle model. Discussions on computational complexity and performance of the two schemes are presented. The effectiveness of the proposed MPC formulation is demonstrated by simulation and experimental tests up to 21 m/s on icy roads.
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| 19. |
- Falcone, Paolo, 1977, et al.
(author)
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Regenerative Braking and Yaw Dynamics Optimal Control in Hybrid Vehicles
- 2009
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In: 21st International Symposium on Dynamics of Vehicles on Roads and Tracks, 17-21 August 2009, Stockholm, Sweden.
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Conference paper (peer-reviewed)abstract
- In hybrid vehicles, regenerative braking is used in order to recover energy when vehiclebrakes. Energy is recovered by converting the vehicle kinetic energy into electric energy tobe stored in electricity buffers, i.e., batteries or capacitors. The recovered energy can then beused for powering the vehicle and thus reduce the fuel consumption. In particular, in order togenerate a braking force, the wheels can be connected to the electric motor, thus providingmotion energy to the generator and charging the electric buffer. When regenerative brakingis applied, the connection of the wheels to the generator results in a load torque (i.e., a braketorque), slowing the vehicle down, and at the same time enables energy recovery.In this paper, we consider hybrid drivelines where the electric motor is connected to therear axle, i.e., the regenerative braking takes place by braking the rear wheels, and focus onthe implications of the regenerative braking on the vehicle dynamics.The scenario considered in this paper (i.e., regenerative braking at the rear axle) is challengingfrom both the brake force delivery and distribution and the vehicle stabilizationperspectives [1]. In fact, we first observe that the maximum force the regenerative brakingcan deliver is limited and, in general, less than the friction braking. In particular, a brakingforce request from the driver might not be delivered entirely through regenerative brakingand a combination of friction and regenerative braking might be necessary. Secondly, werecall that an “optimum” brake proportioning between front and rear axles exists, such thatthe braking performance is maximized and the vehicle stability is preserved (see [2] for adetailed explanation). Clearly, maximizing the braking at one axle might conflict with a brakeforce distribution determined according to some “optimum” brake proportioning. Moreover,preserving the vehicle stability and comfort on slippery surfaces while maximizing the energyrecovering is a significant challenge as well. In particular, on low friction surfaces, the braketorque from regenerative braking might be large enough to lock-up the rear wheel. This wouldinduce an oversteering behavior and might even lead to instability, i.e., vehicle spinning [1].Even though instability does not occur, the driver might perceive a reduction of comfort asconsequence of braking at the rear wheels. In particular, on low friction surfaces, where thevehicle can easily operate at the limit of tire force capabilities, a sudden reduction of lateralforce might be experienced as consequence of braking.In this paper, we consider testing scenarios where the driver demands a braking force whilethe vehicle is performing a cornering manoeuvres on slippery surfaces, i.e., snow or ice. Thecontrol objective is to maximize the energy recovery (i.e., the regenerative braking), while (i)delivering the requested braking force by introducing front and rear friction braking as well,if necessary, (ii) preserving the vehicle stability and (iii) limit the lateral force reduction. Weshow how this problem can be effectively formulated as a Model Predictive Control (MPC)problem. In particular, we design a cost function in order to achieve our control objectives.Every time step, based on measurements of the demanded brake force, the vehicle yaw turningrate and longitudinal and lateral velocities, we repeatedly solve an optimization problem inorder to find the braking policy minimizing the cost function while fulfilling design and systemconstraints. As shown in [3], such control approach can be high computational demandingand even prevent real-time implementation. In order to implement our MPC algorithms inreal-time, we resort to the low complexity MPC formulation used in [4], [5], [6] to solveautonomous path following problems.REFERENCES[1] M. Hancock and F. Assadian. Impact of regenerative braking on vehicle stability. IET The Institution of Engineeringand Technology, Hybrid Vehicle Conference, 2006.[2] T. Gillespie. Fundamentals of Vehicle Dynamics, chapter 3, pages 60–67. Society of Automotive Engineers (SAE),1992.[3] F. Borrelli, P. Falcone, T. Keviczky, J. Asgari, and D. Hrovat. MPC-based approach to active steering for autonomousvehicle systems. Int. J. Vehicle Autonomous Systems, 3(2/3/4):265–291, 2005.[4] P. Falcone, F. Borrelli, J. Asgari, H. E. Tseng, and D. Hrovat. Predictive active steering control for autonomous vehiclesystems. IEEE Trans. on Control System Technology, 15(3), 2007.[5] P. Falcone, F. Borrelli, J. Asgari, H. E. Tseng, and D. Hrovat. Linear time varying model predictive control and itsapplication to active steering systems: Stability analisys and experimental validation. International Journal of Robustand Nonlinear Control., 18:862–875, 2008.[6] P. Falcone. Nonlinear Model Predictive Control for Autonomous Vehicles. PhD thesis, Universit`a del Sannio,Dipartimento di Ingegneria, Piazza Roma 21, 82100, Benevento, Italy, June 2007.
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| 20. |
- Falcone, Paolo, 1977, et al.
(author)
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Towards Real-Time Model Predictive Control Approach for Autonomous Active Steering
- 2006
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In: 8th International Symposium on Advanced Vehicle Control, Taipei, Taiwan, August 2006.
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Conference paper (peer-reviewed)abstract
- In this paper we follow the novel approach presented in [1] to autonomous active steering control design. A nonlinear Model Predictive Control (MPC) scheme is designed to control front wheel steering in order to stabilize a vehicle along a desired path while fulfilling its physical constraints.The proposed nonlinear MPC controller has been implemented in in real time by using advanced sensors, actuators and non-linear optimization solvers. This papers presents the experimental setup and the experimental results obtained at low vehicle speeds on icy roads with a passenger car.
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