Design of a robust vehicle dynamics controller based on optimal fuzzy theory for passenger car

Chu, Liang; Zhang, Yongsheng; Xu, Mingfa; Shi, Yanru; Ou, Yang

doi:10.1109/icindma.2010.5538081

Cited by 1 publication

(2 citation statements)

References 6 publications

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“…The model control is optimized based on the model predictive control theory as explained in [8] through adjusting the tire parameters on-line, the vehicle stability control performance is well, but the robustness was poor. In addition to that, the vehicle stability controller is designed based on the Linear Quadratic Regulator (LQR) and fuzzy control theory as proposed in [1] where the yaw moment is produced by differential braking and the target of control vehicle stability is achieved. And in [9] it is used neural controller based genetic algorithm to control the vehicle to track a desired lateral velocity from front and rear steering angles.…”

Section: ‫عصبي‬ ‫مسيطر‬ ‫تصميم‬ (Pid) ‫ل‬ ‫ألخطي‬ ‫أساسه‬ ‫لمركبة‬ ‫أ...mentioning

confidence: 99%

“…Figure (1) shows the two DOF vehicle model and it is widely used for lateral control design and has been shown to provide accurate response characteristics compared to more complex models for conditions up to 0.3g lateral acceleration [6, 7, 11, 12 and 13]. The linear dynamics model of vehicle lateral motion [13 and 14] with interaction in multi-input multi-output system are expressed as the state space equations as follows:…”

Section: Vehicle Dynamics Model 2-dofmentioning

confidence: 99%

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Designing a Nonlinear PID Neural Controller of Differential Braking System for Vehicle Model Based on Particle Swarm Optimization

Al-Araji¹

2014

ETJ

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This paper presents a nonlinear PID neural controller for the 2-DOF vehicle model in order to improve stability and performances of vehicle lateral dynamics by achieving required yaw rate and reducing lateral velocity in a short period of time to prevent vehicle from sliding out the curvature. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the nonlinear PID neural controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The differential braking system and front wheel steering angle are the outputs of the nonlinear PID neural controller that has automatically controlled the vehicle lateral motion when the vehicle rotates the curvatures. Simulation results show the effectiveness of the proposed control algorithm in terms of the best transient state outputs of the system and minimum tracking errors as well as smoothness control signals obtained with bounded external disturbances.

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Section: ‫عصبي‬ ‫مسيطر‬ ‫تصميم‬ (Pid) ‫ل‬ ‫ألخطي‬ ‫أساسه‬ ‫لمركبة‬ ‫أ...mentioning

confidence: 99%

Section: Vehicle Dynamics Model 2-dofmentioning

confidence: 99%

Designing a Nonlinear PID Neural Controller of Differential Braking System for Vehicle Model Based on Particle Swarm Optimization

Al-Araji¹

2014

ETJ

View full text Add to dashboard Cite

show abstract

Design of a robust vehicle dynamics controller based on optimal fuzzy theory for passenger car

Cited by 1 publication

References 6 publications

Designing a Nonlinear PID Neural Controller of Differential Braking System for Vehicle Model Based on Particle Swarm Optimization

Designing a Nonlinear PID Neural Controller of Differential Braking System for Vehicle Model Based on Particle Swarm Optimization

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