1,721,183 research outputs found
Nonlinear Model Predictive Control for Autonomous Vehicles
No full textIn this thesis we consider the problem of designing and implementing Model Predictive Controllers (MPC) for stabilizing the dynamics of an autonomous ground vehicle. For such a class of systems, the non-linear dynamics and the fast sampling time limit the real-time implementation of MPC algorithms to local and linear operating regions. This phenomenon becomes more relevant when using the limited computational resources of a standard rapid prototyping system for automotive applications. In this thesis we first study the design and the implementation of a nonlinear MPC controller for an Active Font Steering (AFS) problem. At each time step a trajectory is assumed to be known over a finite horizon, and the nonlinear MPC controller computes the front steering angle in order to follow the trajectory on slippery roads at the highest possible entry speed. We demonstrate that experimental tests can be performed only at low vehicle speed on a dSPACE rapid prototyping system with a frequency of 20 Hz. Then, we propose a low complexity MPC algorithm which is real-time capable for wider operating range of the state and input space (i.e., high vehicle speed and large slip angles). The MPC control algorithm is based on successive on-line linearizations of the nonlinear vehicle model (LTV MPC). We study performance and stability of the proposed MPC scheme. Performance is improved through an ad hoc stabilizing state and input constraints arising from a careful study of the vehicle nonlinearities. The stability of the LTV MPC is enforced by means of an additional convex constraint to the finite time optimization problem. We used the proposed LTV MPC algorithm in order to design AFS controllers and combined steering and braking controllers. We validated the proposed AFS and combined steering and braking MPC algorithms in real-time, on a passenger vehicle equipped with a dSPACE rapid prototyping system. Experiments have been performed in a testing center equipped with snowy and icy tracks. For both controllers we showed that vehicle stabilization can be achieved at high speed (up to 75 Kph) on icy covered roads.This research activity has been supported by Ford Research Laboratories, in Dearborn, MI, USA
Low-Complexity Explicit MPC Controller for Vehicle Lateral Motion Control
No full textWe consider the problem of controlling the vehicle lateral motion in highway scenarios while guaranteeing safety. We propose a solution consisting of a Low-Complexity Explicit Model Predictive Controller (LC-EMPC), where the lateral deviation from the desired path is hard constrained according to prescribed bounds. The robust satisfaction of such safety constraints can be achieved by imposing the terminal state to enter a Robust Invariant Set (RIS), which is known to result into a potentially high number of additional constraints, thus increasing the computational complexity of the controller. Our controller, instead, relies on recent results on the calculation of low-complexity RIS to significantly reduce the number of constraints in the MPC controller. Simulation results show that the designed controller is able to meet the desired objectives with highly reduced complexity
Full-complexity characterization of control-invariant domains for systems with uncertain parameter dependence
No full textThis letter proposes an algorithm to find a robust control invariant (RCI) set of desired complexity and the associated linear, state-feedback control law. The candidate RCI set is restricted to be symmetric around the origin. The algorithm is applicable to rational parameter dependent systems with bounded additive disturbance. The system constraints are framed as simple affine inequalities whereas the invariance condition as a set of sufficient LMI conditions. The proposed iterative algorithm is guaranteed to be recursively feasible and converge to some stationary point
Joint Synthesis of Dynamic Feed-Forward and Static State Feedback for Platoon Control
No full textJoint synthesis of static state-feedback and dynamic feed-forward is considered for a homogenous platoon operating under the leader and predecessor following scheme. The problem is formulated with two H-infinity type performance objectives. The first objective requires the first vehicle to respond in a desirable way to the maneuvers of the leader and it is to be realized by a joint synthesis of the feed-forward filter together with a state feedback gain. Novel LMI conditions are derived for this objective by fixing the order and the poles of the filter. The second objective aims at preventing the amplification of the errors backward along the platoon, while also maintaining robustness against communication problems with the leader. New sufficient LMI conditions are provided for this objective, based on which leader and predecessor feedback gain matrices can be constructed
Passive Radar based on WiFi transmissions: signal processing schemes and experimental results
Get PDFAim of this work is to study innovative techniques and processing strategies for a new passive sensor for short range surveillance. The principle of work of the sensor will be based on the passive radar principle, and WiFi transmissions - which usually provide Internet access within local areas - will be exploited by the passive
sensor to detect, localize and classify targets.EU ATOM Project (FP7
State Feedback Synthesis for Homogenous Platoons under the Leader and Predecessor Following Scheme
No full textSynthesis of state-feedback controllers is considered for a homogenous platoon operating under the leader and predecessor following scheme. The problem is formulated as a multi-objective H-infinity type synthesis. The performance specifications are shaped by the string stability objective and prevention of extensive control effort, as well as by the concerns about the communication problems with the leader. Sufficient LMI conditions are then provided for the synthesis of common statefeedback gains. Some aspects of the developed approach are illustrated in the longitudinal control of a vehicle platoon
Controller Synthesis for a Homogenous Platoon under Leader and Predecessor Following Scheme
No full textSynthesis of dynamic output-feedback controllers is considered in a homogenous platoon operating under the leader and predecessor following scheme. The problem is formulated as a multi-objective H∞-type synthesis in which the goal is to keep the worst-case spacing error variations induced by the maneuvers of the leader as small as possible, while also avoiding excessively large control inputs. Concerns about possible communication problems are also reflected in the problem formulation. Sufficient solvability conditions are then derived in the form of linear matrix inequalities, which also depend on a scalar parameter over which a line search has to be performed. It is also described how the transient behavior of the platoon can be shaped by a simple additional condition. The procedure is illustrated by some sample designs and simulations in the longitudinal control of vehicle platoons
Robust static output feedback synthesis for platoons under leader and predecessor feedback
No full textA multi-objective static output feedback synthesis problem is considered for the control of vehicle platoons
under leader and predecessor feedback. Sufficient linear matrix inequality conditions are derived for the
solvability of the problem in a way to facilitate static feed-forward as well. A novel velocity-dependent spacing
policy is integrated into the control scheme together with a platoon model in which the emphasis on the
predecessor information can be adjusted by a normalized scalar weight. It is shown that the string stability of
the spacing errors and the acceleration signals can always be guaranteed by choosing this weight sufficiently
small. Moreover, provided that the time headway is chosen sufficiently large, the synthesis can be performed
in a way to avoid the amplification of acceleration energies if compared with the leader. As a particularly
convenient feature, the target spacing between the vehicles becomes smaller when moving backward along
the platoon. The total increase in the platoon length caused by the introduction of the velocity-dependent
scheme is shown to be bounded and decreasing with decreasing predecessor weight. It is also established
that the predecessor weight can be adjusted smoothly over time without endangering the formation stability.
In addition to the optimization of the parameters of common fixed-structure controllers for general vehicle models, the proposed synthesis procedure provides various tools for improving robustness against measurement noise, communication delay, and model uncertainty
New LMI Conditions for Static Output Feedback Synthesis with Multiple Performance Objectives
No full textThe static output feedback synthesis problem is considered with H-infinity and generalized H-two performance objectives. New sufficient LMI conditions are derived for guaranteeing the required performance objectives. These conditions also depend on scalar parameters that need to be fixed beforehand. The output feedback gain matrix is computed from the involved matrix variables without the use of system matrices. Hence the conditions can directly be used to solve the multi-objective synthesis problem also for parameter-dependent systems with constant or parameter-dependent gain matrices. The method is illustrated in the adaptive cruise control problem
Safe Transitions From Automated to Manual Driving Using Driver Controllability Estimation
No full textIn this paper, we consider the problem of assessing when the control of a vehicle can be safely transferred from an automated driving system to the driver. We propose a method based on a description of the driver\u27s capabilities to maneuver the vehicle, which is defined as a subset of the vehicle\u27s state space and called the driver controllability set (DCS). Since drivers\u27 capabilities vary among individuals, the DCS is updated online during manual driving. By identifying the limits of the individual driver\u27s normal driving envelope, we find the estimated bounds of the DCS. Using a vehicle model and reachability analysis, we assess whether the states of the vehicle start and remain within the DCS during the transition to manual driving. Only if the states are within the DCS is the transition to manual driving classified as safe. We demonstrate the estimation of the DCS for four drivers based on the data collected with real vehicles in highway and city driving. Experiments on transitions to manual driving are also conducted with real vehicles. Results show that the proposed method can be implemented with a real system to classify transitions from automated to manual driving
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