Model Predictive Control for integrated lateral stability, traction/braking control, and rollover prevention of electric vehicles

Ataei, Mansour; Khajepour, Amir; Jeon, Soo

doi:10.1080/00423114.2019.1585557

Cited by 125 publications

(73 citation statements)

References 28 publications

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“…The benefit brought by rolling optimization on finite and shifting horizons is that MPC can optimize its control law throughout the process of control and therefore can cope with the dynamically changing characteristics of the controlled system. The linear time-varying (LTV) MPC is more applicable in practice as the basic control strategy due to its much higher computational efficiency as compared to nonlinear MPC (NMPC) [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Two-Layer Controller for Lateral Path Tracking Control of Autonomous Vehicles

Yin

Huang

2020

Sensors

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This paper presents a two-layer controller for accurate and robust lateral path tracking control of highly automated vehicles. The upper-layer controller, which produces the front wheel steering angle, is implemented with a Linear Time-Varying MPC (LTV-MPC) whose prediction and control horizon are both optimized offline with particle swarm optimization (PSO) under varying working conditions. A constraint on the slip angle is imposed to prevent lateral forces from saturation to guarantee vehicle stability. The lower layer is a radial basis function neural network proportion-integral-derivative (RBFNN-PID) controller that generates electric current control signals executable by the steering motor to rapidly track the target steering angle. The nonlinear characteristics of the steering system are modeled and are identified on-line with the RBFNN so that the PID controller’s control parameters can be adjusted adaptively. The results of CarSim-Matlab/Simulink joint simulations show that the proposed hierarchical controller achieves a good level of path tracking accuracy while maintaining vehicle stability throughout the path tracking process, and is robust to dynamic changes in vehicle velocities and road adhesion coefficients.

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Section: Introductionmentioning

confidence: 99%

A Two-Layer Controller for Lateral Path Tracking Control of Autonomous Vehicles

Yin

Huang

2020

Sensors

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“…Integrated control of different chassis dynamics has been performed using methods such as switching, 24,25 gain scheduling, 16,26,27 and constrained optimization control. 23,[28][29][30] Switching has two considerable disadvantages. First, the internal stability of the system is not ensured due to the switching process.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, when the system order and consequently the number of actuators are increased, more tuning parameters are required for integration tasks. The design of a controller with constraints on brake and steering actuators is discussed in References [28][29][30]. In these papers, the permissible range of input or output changes in lateral dynamics is calculated based on the allowable area for the yaw rate and sideslip angle.…”

Section: Introductionmentioning

confidence: 99%

Adaptive yaw stability control by coordination of active steering and braking with an optimized lower‐level controller

Ahmadian

Khosravi

Sarhadi

2020

Adaptive Control & Signal

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Summary In this article, an integrated multiinput multioutput model reference adaptive control algorithm is presented based on active front steering and effective direct yaw moment distribution as an advanced driver assistance system. Vehicle parameter uncertainties in mass and tire‐road friction coefficient are considered through adaptation laws at the upper level in the control structure. The efficient distribution of yaw moment on the rear wheels is performed via a constrained optimization at the lower control level. Control commands are executed by additive steering angle on front wheels and brake torque applied on one of the rear wheels. Simulation results for different lateral maneuvers are employed for the evaluation of the proposed adaptive control method. The performance of the integrated control algorithm to enhance vehicle handling and stability is shown on various road conditions.

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“…Thus, a model predictive control (MPC) is proposed in this paper. This technique is widely used to path tracking control since it uses the physical-mathematical model of a vehicle in order to predict a future situation over a finite horizon (Ataei et al, 2020). Considering offroad vehicles, different works have been used dynamic and kinematic models with slip assumption in order to design a MPC controller as Lenain et al (2005); Lenain et al (2007) that used a kinematic with two slip angles to develop a MPC controller which presented satisfactory results even though the slip estimation were considered a problem because the absence of a more robust method in their modelling.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Model Approximation Methods for Off-road Vehicle Path Tracking with MPC

Sousa

Ayala

2020

Anais Do Congresso Brasileiro De Automática 2020

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This paper presents a comparative study between two different approaches for dynamic systems based on Taylor series and based on a double-integrator, and their influence in path tracking control using model predictive controller (MPC) with the view to solve the path tracking problem of an autonomous off-road vehicle. A physical-mathematical model, including the celebrated tire model known as “Magic Formula”, is modified and linearized to control the steering of the vehicle considering different reference trajectory inputs. Simulation results are considered satisfactory, showing that the model linearized considering Taylor series presented better results compared to the double-integrator which indicates that the prediction of the plant output is driven close to the reference. Moreover, the results show that the implemented control strategies for path tracking are adequate for applications in off-road autonomous vehicles.

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Model Predictive Control for integrated lateral stability, traction/braking control, and rollover prevention of electric vehicles

Cited by 125 publications

References 28 publications

A Two-Layer Controller for Lateral Path Tracking Control of Autonomous Vehicles

A Two-Layer Controller for Lateral Path Tracking Control of Autonomous Vehicles

Adaptive yaw stability control by coordination of active steering and braking with an optimized lower‐level controller

Nonlinear Model Approximation Methods for Off-road Vehicle Path Tracking with MPC

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