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In this paper the concept of Hybrid Parameter-Varying Model Predictive Control (HPV-MPC) is applied for lateral vehicle stabilization. Parameter-varying in the MPC context means that a prediction model with non-constant, parameter-varying system matrices is employed. In the investigated scenario, the displacement of a car on an icy road under a sid...
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Citations
... In [7] the authors showed an enhancement in the tracking performance of the MPC controller by combining steering and braking. To reduce the computational complexity of MPC algorithms, Besselmann and Morari present a hybrid MPC approach in [8]. A MPC scheme for the combined control of lateral and longitudinal dynamics can be found in [9], where the control problem is decomposed in a path following and a velocity tracking problem. ...
In this paper, a model predictive control (MPC) approach for the lateral and longitudinal control of a highly automated electric vehicle with all-wheel drive and dual-axis steering is presented. For the prediction of state trajectories a two-track vehicle model is used. The MPC problem for trajectory tracking is formulated by controlling the front and rear steering angle as well as the individual drive torques with respect to actuator and design constraints. Beside the steering angles, the MPC controller computes the individual drive torques to not only match the reference velocity but also to support the lateral dynamics of the vehicle using torque vectoring. The MPC problem is solved using ACADOS, a software package for efficiently solving optimal control problems. The effectiveness of the proposed MPC scheme is demonstrated via simulation.
... Model predictive control (MPC) has aroused the interest in the automotive industry, its application spanning several fields, such as engine control [5], lateral vehicle dynamics [6], cruise control [7] or autonomous vehicle [8]. MPC is a constrained optimization strategy, which calculates a sequence of future commands that minimizes a cost function over a prediction horizon, but where only the first command is applied in a receding horizon manner. ...
The focus of this work is to exploit the potential of model predictive control for the energy management of hybrid electric vehicles. The global framework covers the power distribution between the internal combustion engine and the electric machine(s) in order to minimize the fuel consumption. Current technologies allow the forecast of speed profile and road grade making predictive control a viable solution. The gears within the prediction model are assumed to be pre-calculated at a higher level and therefore, only the torque split problem needs to be addressed by the energy management mechanism. Engine stop can improve fuel gain which is even more noticeable for hybrid vehicles and hence, its inclusion is naturally considered in the present study. Furthermore, optimization of the speed profile is addressed by introducing the free-wheeling functionality.
... Among these strategies, model predictive control (MPC) emerges as a solution with a high potential. It shows an increased presence in the automotive industry, with applications spanning several fields, such as engine control [20], lateral vehicle dynamics [21], cruise control [22] or autonomous vehicle [23]. MPC is a model-based constrained optimization strategy, which calculates a sequence of commands that minimizes a cost function over a finite horizon, but where only the first command is applied. ...
... Parmi ces stratégies, model predictive control (MPC) émerge comme une solution avec un potentiel considérable. Il est de plus en plus présent dans l'industrie de l'automobile, avec des applications qui couvrent différents domaines, comme le contrôle moteur [20], la dynamique latérale du véhicule [21], cruise control [22] où véhicule autonome [23]. MPC est une stratégie d'optimisation à base de modèle, sous contrainte, qui calcule une séquence de commandes qui minimisent une fonction coût sur un horizon fini, mais où seulement la première commande s'applique. ...
For a hybrid electric vehicle, one of the most challenging aspects from a control perspective is the power split between the engine and the motors, problem referred to as energy management. The question that arises is how to exploit the additional degree of freedom - the electric path - such that the vehicle performance related to consumption is improved. The required robustness for the control strategies encourage the choice of model-based methods and in the present context of telemetry data acquisition, model predictive control emerges as an attractive option, motivated in addition by its ability to handle the constraints. Most of the current energy management strategies are application-oriented and therefore, a generic hybrid powertrain model would provide more flexibility. This thesis is developed on two main axes: the synthesis of a generic model and of a MPC-based control strategy for the energy management, with a self-tuning mechanism. If the torque split is the main control objective, other functionalities can be introduced, such as engine stop/start and coasting. The latter is usually handled in the literature with a map-based strategy, but here an analytical solution was proposed. A validation on a vehicle high-fidelity model for a mild hybrid with a dual-clutch transmission closes the work and shows the potential of the proposed strategy.
... Model predictive control (MPC) has aroused the interest in the automotive industry, its application spanning several fields, such as engine control [5], lateral vehicle dynamics [6], cruise control [7] or autonomous vehicle [8]. MPC is a constrained optimization strategy, which calculates a sequence of future commands that minimizes a cost function over a prediction horizon, but where only the first command is applied in a receding horizon manner. ...
In this paper, the energy management of hybrid electric vehicles is considered and two problems are addressed: torque split and engine stop-start policies. Model predictive control potential is proven especially for the decision to stop the engine at the proper moment and also to anticipate engine restart. A piecewise linear polynomial with respect to torque is used to approximate the fuel consumption and it will be shown that its partition can be exploited to implicitly consider engine stop decision. The strategy is applied to a dual-clutch transmission hybrid architecture which was simulated on a high-fidelity vehicle model.
... LPV control designs for lateral control are presented in [157]–[159]. LPV models are used in [160], [161] together with predictive control approaches for path and trajectory stabilization. At a lower level of automation, LPV control techniques have been proposed for integrated system control. ...
Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency, and convenience of automotive transportation. Safety-critical tasks that must be executed by a self-driving vehicle include planning of motions through a dynamic environment shared with other vehicles and pedestrians, and their robust executions via feedback control. The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting. A selection of proposed techniques is reviewed along with a discussion of their effectiveness. The surveyed approaches differ in the vehicle mobility model used, in assumptions on the structure of the environment, and in computational requirements. The side-by-side comparison presented in this survey helps to gain insight into the strengths and limitations of the reviewed approaches and assists with system level design choices.
... We consider a scenario where the yaw motion of a vehicle has to be stabilized in a constant radius curve on a slippery surface where instability might occur due to the lateral tire force saturation at the rear axle. We start from a bicycle model [28] where the tire forces are computed through nonlinear static characteristics and piecewise linearize them as in [10]. We present simulation results showing that a vehicle operating in an unstable region of the state and input space can be stabilized through the proposed approach. ...
We present a methodology for designing reference tracking controllers for constrained, discrete-time piecewise affine systems. The approach follows the idea of reference governor techniques where the desired set-point is filtered by a system called the “reference governor”. Based on the system current state, set-point, and prescribed constraints, the reference governor computes a new set-point for a low-level controller so that the state and input constraints are satisfied and convergence to the original set-point is guaranteed.In this note we show how to design a reference governor for constrained piecewise affine systems by using polyhedral invariant sets, reachable sets, multiparametric programming and dynamic programming techniques.
... Although remarkable control performance could be achieved, the high computational burden is a drawback for practical implementations. In [5] it was shown for the side-wind rejection scenario how to lower this burden by using simplified prediction models that approximate the nonlinear model of the vehicle dynamics. In the following the results of [5] are extended by considering also the double lane-change scenario and by adding conceptual and computational details. ...
... In [5] it was shown for the side-wind rejection scenario how to lower this burden by using simplified prediction models that approximate the nonlinear model of the vehicle dynamics. In the following the results of [5] are extended by considering also the double lane-change scenario and by adding conceptual and computational details. Three simplified models are derived, all of them allow the use of quadratic programming (QP) solvers instead of nonlinear programming (NP) solvers, and thus a reduction in computational complexity. ...
In this paper the concept of Hybrid Parameter-Varying Model Predictive Control (HPV-MPC) is applied for autonomous vehicle steering. Parameter-varying in the MPC context means that a prediction model with non-constant, parameter-varying system matrices is employed. In the investigated scenarios, the displacement of a car on an icy road due to a side wind gust shall be mitigated, and a double lane-change maneuver shall be performed autonomously. In order to explore a possible reduction of online computations and the inherent degradation of control performance, the nonlinear model of the lateral dynamics is approximated in various ways. A comparison between controllers using prediction models varying from the full nonlinear model, as an indication for the maximal achievable performance, to a linear model was performed. Particular emphasis was put on the hybrid parameter-varying prediction model, to investigate their potential in terms of computational effort and control performance.
... Optimal control was used to evaluate the effectiveness of several vehicle actuator configurations in emergency lane change maneuvers, including steer-by-wire, differential braking, and active suspension [9]. Model predictive control (MPC), a general control framework for control of systems with hard constraints has also been applied to control of mobile robots operating without wheel slip [5] and with wheel slip10111213. In [10], a linear MPC controller was designed using a model of tire compliance and suspension roll compliance to concurrently improve rollover stability and directional response, though path tracking was not considered explicitly. ...
... Due to the computational intensity of MPC, the controller is evaluated on icy road conditions, where the limit of friction can be reached at low speeds. In [13], computational requirements are studied for a similar icy road scenario with several MPC controllers of varying complexity. While the effects of tire compliance and terrain slope on path tracking performance were acknowledged in [8], to the author's knowledge, no other path tracking controllers have been proposed that account for tire compliance effects and terrain slope. ...
Path tracking control on non-flat terrain is an important capability of mobile robots operating in outdoor environments. A path tracking controller based on the model predictive control (MPC) framework is presented that explicitly considers terrain geometry and actuator limitations. The controller performance is studied with three vehicle dynamic models in a high-fidelity ADAMS simulation. The effect of model order on path tracking performance on flat terrain and sloped terrain is evaluated. It is shown that improved performance can be obtained by explicitly considering terrain effects.
