Model Predictive Control for Vehicle Yaw Stability With Practical Concerns

Choi, Mooryong; Choi, Seibum B.

doi:10.1109/tvt.2014.2306733

Cited by 117 publications

(56 citation statements)

References 19 publications

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“…Instead of (3) and (4), (25) and (26) are substituted into (7) and (8). Due to the additional terms, F yf0 and F r0 in (25) and (26), E add is added to (18) and reexpressed as follows:…”

Section: Bicycle Model With Linearized Tire Forcesmentioning

confidence: 99%

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MPC for vehicle lateral stability via differential braking and active front steering considering practical aspects

Choi

2015

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper presents a control architecture that simultaneously utilizes active front steering (AFS) and differential braking for vehicle lateral stability while minimizing longitudinal perturbations. This control scheme is based on the model predictive control (MPC) using the extended bicycle model that captures the lagged characteristics of tire forces and actuators. The nonlinearities of tire force are also reflected on the extended bicycle model by linearizing the tire forces at the operating points. Instead of casting the MPC problem into a quadratic program with constraints that require numerical solvers, the proposed method is designed to follow the reference states with desired inputs since the solutions of MPC problems with affine models to track desired states can be easily obtained by matrix inversion. Simulation results, obtained by the vehicle dynamics software Carsim, demonstrate that the suggested method is able to control the vehicle to track the desired path while keeping the vehicle lateral stability on various road surfaces.

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“…Instead of (3) and (4), (25) and (26) are substituted into (7) and (8). Due to the additional terms, F yf0 and F r0 in (25) and (26), E add is added to (18) and reexpressed as follows:…”

Section: Bicycle Model With Linearized Tire Forcesmentioning

confidence: 99%

“…25 The values of corrective yaw moments when exerting brake forces on the front, M zf , and on the rear, M zr , can be expressed as follows:…”

Section: Generations Of B R and M Z_r For Oversteering Vehiclementioning

confidence: 99%

MPC for vehicle lateral stability via differential braking and active front steering considering practical aspects

Choi

2015

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…under a set of operation constraints [15]. Therefore, the MPC approaches have been widely studied, and applied in the electronic stability control (ESC) [16], the adaptive cruise control (ACC) [17], the path following [18], and the energy management [19] for vehicle systems. However, the MPC-based approaches may cause some computational burdens in finding optimal solutions, and induce accuracy losses when using simplified models in some critical situations [20].…”

Section: Introductionmentioning

confidence: 99%

Torque Vectoring and Rear-Wheel-Steering Control for Vehicle's Uncertain Slips on Soft and Slope Terrain Using Sliding Mode Algorithm

Liang

Zhao

Dong

et al. 2020

IEEE Trans. Veh. Technol.

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Off-road vehicles always experience serious uncertain longitudinal and lateral slips when running on soft and slope terrains, and some parameters of the vehicles, such as the cornering stiffness and the slip of wheels, are always not constants. In this paper, control strategies for the torque of each wheel and the rear-wheel-steering angle are proposed to maintain a stable velocity and approach an ideal reference model for the off-road vehicle by using second-order sliding mode (SOSM) techniques. An observer is constructed to estimate the actual sideslip angle of the vehicle with the consideration of uncertainties and disturbances of the system. Then, with conditions of bounded uncertainties and disturbances, composite super-twisting (ST) controllers combined with a velocity controller are designed to generate the total torque, the differential torque, and the rear-wheel-steering angle. On this basis, the proposed controllers have been verified to lead good robustness for maintaining the stable velocity and approaching the ideal reference model by using an optimal torque allocation controller at a lower layer. In comparison with conventional yaw moment controllers without the rear-wheel-steering control, the proposed controllers are shown to be more effective.

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“…Therefore, using low-cost on-board sensors to measure vehicle state parameters, and then estimating the state of vehicle dynamics based on the vehicle dynamics equation, and then real-time estimation of pavement information has become a research focus [4,5]. Domestic and foreign scholars have made a large amount of studies on tire-road friction coefficient estimation based on -s curve [6][7][8][9][10][11]. However, a large amount of data is required for data fitting because this method has problems of slow response time, weak timeliness, and heavy dependence of identification correctness on model accuracy.…”

Section: Introductionmentioning

confidence: 99%

Calculation Algorithm of Tire‐Road Friction Coefficient Based on Limited‐Memory Adaptive Extended Kalman Filter

Huang

et al. 2019

Mathematical Problems in Engineering

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In this paper, a limited-memory adaptive extended Kalman Filter (LM-AEKF) to estimate tire-road friction coefficient is proposed. By combining extended Kalman filter (EKF) with the limited-memory filter, this algorithm can reduce the effects of old measurement data on filtering and improve the estimation accuracy. Self-adaptive regulatory factors were introduced to weigh covariance matrix of evaluated error. Meanwhile, measured noise covariance matrix was adjusted dynamically by fuzzy inference to accurately track the breaking status of system. Therefore, problems, including large filter error and divergence caused by incorrect model, can be solved. Joint simulation was conducted for the proposed algorithm with Carsim and Matlab/Simulink. Under the different road conditions, real-vehicle road tests were conducted in various working conditions for contrast with traditional EKF results. Simulation and real-vehicle road tests show that this algorithm can enhance the filter stability, improve the estimation accuracy of algorithm, and increase algorithm robustness.

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Model Predictive Control for Vehicle Yaw Stability With Practical Concerns

Cited by 117 publications

References 19 publications

MPC for vehicle lateral stability via differential braking and active front steering considering practical aspects

MPC for vehicle lateral stability via differential braking and active front steering considering practical aspects

Torque Vectoring and Rear-Wheel-Steering Control for Vehicle's Uncertain Slips on Soft and Slope Terrain Using Sliding Mode Algorithm

Calculation Algorithm of Tire‐Road Friction Coefficient Based on Limited‐Memory Adaptive Extended Kalman Filter

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