MPC-based yaw and lateral stabilisation via active front steering and braking

Falcone, Paolo; Tseng, H. Eric; Borrelli, Francesco; Asgari, Jahan; Hrovat, Davor

doi:10.1080/00423110802018297

Cited by 321 publications

(182 citation statements)

References 6 publications

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“…In this paper, we use a double lane [10] as the desired trajectory and the simulation environment is Simulink. And the simulation model is 7 degrees of freedom (longitudinal, lateral, yaw and the rotational of four wheels) vehicle dynamic model.…”

Section: Simulationmentioning

confidence: 99%

“…The authors use direct Lyapunov method to compute the front steering angle and braking torques, then log the calculated data on vehicle dynamic model to achieve a double lane change maneuver. In [10] the authors use the Nonlinear MPC formulation to track a desired path for obstacle avoidance maneuver, by a combined use of braking and steering. More examples are shown in [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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MPC-Based Trajectory Tracking Via Active Front Steering and External Yaw Moment

Wang¹,

Hu²,

Ni³

2017

dtetr

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In this paper, we propose a trajectory tracking method based Model Predictive Control (MPC) by utilizing steering and yaw moment. The main control objective is to track the desired trajectory with the front steering angle and yaw moment. And an external logic computes the torques at the four wheels such that the yaw moment is achieved, at the same time the logic controls the tire slip ratio around 0.15. Finally, the effectiveness of the proposed approach is demonstrated through the simulations at low and high speed.

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Section: Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MPC-Based Trajectory Tracking Via Active Front Steering and External Yaw Moment

Wang¹,

Hu²,

Ni³

2017

dtetr

View full text Add to dashboard Cite

show abstract

“…An approach based on robust invariant sets is explored in [8]. Other work on MPC in vehicle-dynamics control is [9]. MPC was also used in [10] for predictive prevention of roadway departure, with operation restricted to the linear region of the dynamics.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical predictive control for ground-vehicle maneuvering

Berntorp

Magnusson

2015

2015 American Control Conference (ACC)

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Abstract-This paper presents a hierarchical approach to feedback-based trajectory generation for improved vehicle autonomy. Hierarchical vehicle-control structures have been used before-for example, in electronic stability control systems, where a low-level control loop tracks high-level references. Here, the control structure includes a nonlinear vehicle model already at the high level to generate optimization-based references. A nonlinear model-predictive control (MPC) formulation, combined with a linearized MPC acting as a backup controller, tracks these references by allocating torque and steer commands. With this structure the two control layers have a physical coupling, which makes it easier for the low-level loop to track the references. Simulation results show improved performance over an approach based on linearized MPC, as well as feasibility for online implementations.

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“…In the low-layer, wheel torque is distributed for both improving the vehicle maneuverability and reducing the energy consumption. As shown in the literature, many methods have been used to calculate the desired yaw moment in vehicle yaw motion control including sliding mode control [5] (SMC), fuzzy logic control [6], feed forward and feedback control [7], H  robust control [8], model predictive control [9] and adaptive control [10]. In this paper, SMC is adopted in the high-level controller due to its greater robustness to model inaccuracy and parameter error.…”

Section: Introductionmentioning

confidence: 99%

Wheel Torque Distribution of Four-Wheel-Drive Electric Vehicles Based on Multi-Objective Optimization

Lin

2015

Energies

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Abstract:The wheel driving torque on four-wheel-drive electric vehicles (4WDEVs) can be modulated precisely and continuously, therefore maneuverability and energy-saving control can be carried out at the same time. In this paper, a wheel torque distribution strategy is developed based on multi-objective optimization to improve vehicle maneuverability and reduce energy consumption. In the high-layer of the presented method, sliding mode control is used to calculate the desired yaw moment due to the model inaccuracy and parameter error. In the low-layer, mathematical programming with the penalty function consisting of the yaw moment control offset, the drive system energy loss and the slip ratio constraint is used for wheel torque control allocation. The programming is solved with the combination of off-line and on-line optimization to reduce the calculation cost, and the optimization results are sent to motor controllers as torque commands. Co-simulation based on MATLAB ® and Carsim ® proves that the developed strategy can both improve the vehicle maneuverability and reduce energy consumption.

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MPC-based yaw and lateral stabilisation via active front steering and braking

Cited by 321 publications

References 6 publications

MPC-Based Trajectory Tracking Via Active Front Steering and External Yaw Moment

MPC-Based Trajectory Tracking Via Active Front Steering and External Yaw Moment

Hierarchical predictive control for ground-vehicle maneuvering

Wheel Torque Distribution of Four-Wheel-Drive Electric Vehicles Based on Multi-Objective Optimization

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