Vehicle path tracking by integrated chassis control

Salehpour, Saman; Pourasad, Yaghoub; Taheri, Seyyed Hadi

doi:10.1007/s11771-015-2655-y

Cited by 22 publications

(16 citation statements)

References 19 publications

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“…The model parameter uncertainty as well as system interferences are considered, so a robust tracking controller is used to calculate the additional steering angle to track the pre-planned CA trajectory. Salehpour presented a vehicle path tracking by integrated chassis control [10]. In order to follow the desired path, linear quadratic regulator controller was developed to regulate direct yaw moment and corrective steering angle on wheels.…”

Section: Introductionmentioning

confidence: 99%

Path Tracking Control for Active Rear Steering Vehicles Considering Driver Steering Characteristics

2020

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In this paper, a path tracking control for active rear steering vehicles considering driver steering characteristics is proposed to help drivers track the desired vehicle trajectory in a tailored way. Firstly, the driver model is established, and based on the driver steering data, the driver parameters are identified by the least square method. Based on driver model parameter identification, driver and vehicle models are formed for the MPC controller design. In MPC controller design, global objective function, considering the task performance, vehicle stability, driver's physical workloads, mental workloads and actuator performance, is designed to optimize the overall performance of the driver-vehicle system. On the constraint, the driver operating limit, actuator physical limit and vehicle stability performance limit are also considered to make the controller feasible to in the real practice. Simulation results under different conditions show that the controller can significantly improve the system performance and reduce the driver's workloads. INDEX TERMS Path tracking, vehicle dynamics, active steering, model predictive control.

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Section: Introductionmentioning

confidence: 99%

Path Tracking Control for Active Rear Steering Vehicles Considering Driver Steering Characteristics

2020

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“…Meta-heuristic optimization algorithms are used enormously to design controllers. 20,21 Genetic algorithm has been used to optimize the FNN controller for tracking the vehicle's manoeuvre in lane changing. 20 Proportional integral derivative (PID) and linear quadratic regulator (LQR) controllers are optimized using particle swarm optimization (PSO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Lateral control of an autonomous vehicle using integrated backstepping and sliding mode controller

Norouzi

Masoumi

Barari

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part K:

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In this paper, a novel Lyapunov-based robust controller by using meta-heuristic optimization algorithm has been proposed for lateral control of an autonomous vehicle. In the first step, double lane change path has been designed using a fifth-degree polynomial (quantic) function and dynamic constraints. A lane changing path planning method has been used to design the double lane change manoeuvre. In the next step, position and orientation errors have been extracted based on the two-degree-of-freedom vehicle bicycle model. A combination of sliding mode and backstepping controllers has been used to control the steering in this paper. Overall stability of the combined controller has been analytically proved by defining a Lyapunov function and based on Lyapunov stability theorem. The proposed controller includes some constant parameters which have effects on controller performance; therefore, particle swarm optimization algorithm has been used for finding optimum values of these parameters. The comparing result of the proposed controller with backstepping controller illustrated the better performance of the proposed controller, especially in the low road frictions. Simulation of designed controllers has been conducted by linking CarSim software with Matlab/Simulink which provides a nonlinear full vehicle model. The simulation was performed for manoeuvres with different durations and road frictions. The proposed controller has outperformed the backstepping controller, especially in low frictions.

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“…In the past 10 years, the researchers have employed different control rules including sliding mode control (SMC), [3][4][5][6][7][8][9][10][11][12] HN robust control, 13,14 model predictive control (MPC), [15][16][17][18][19][20] fuzzy control, [21][22][23][24][25][26] backstepping, 27,28 adaptive control, [29][30][31] proportional-integralderivative (PID) controllers, [32][33][34][35] linear-quadratic regulator (LQR), 22,36,37 optimization algorithms 38,39 and solution of linear matrix inequalities (LMI) 40 to design controllers. Sliding mode control as a non-linear control plays an important role against different friction changes of road and different velocities in the presence of parameter uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Vehicle lateral control in the presence of uncertainty for lane change maneuver using adaptive sliding mode control with fuzzy boundary layer

Norouzi

Kazemi

Azadi

2017

Proceedings of the Institution of Mechanical Engineers, Part I:

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Recent researches on advanced driver-assistance systems indicate great advances in terms of safety and comfort in automated driving. Advanced driver-assistance systems use control systems to perform most of the maneuvers as performed by the driver in the past. One of the useful advanced driver-assistance systems is automatic lane change system in order to avoid accidents. This study designs the controller of an automatic lane change system for an autonomous vehicle. The control law in this study is adaptive sliding mode control. To avoid chattering in adaptive sliding mode control, fuzzy boundary layer is used. Also, adaptive law is used for sliding-based switching gain. This adaptive controlling law is used to avoid the calculation of upper bound of system uncertainties. In this study, based on the boundary conditions, the vehicle lane change path planning and different maneuver periods are evaluated. To simulate the designed controller, CarSim-Simulink joint simulation model is used. This linkage leads to a full non-linear vehicle model. The results of simulation show excellent tracking for dry road conditions and acceptable tracking in icy and wet roads in some maneuvers of above 4-s long.

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Vehicle path tracking by integrated chassis control

Cited by 22 publications

References 19 publications

Path Tracking Control for Active Rear Steering Vehicles Considering Driver Steering Characteristics

Path Tracking Control for Active Rear Steering Vehicles Considering Driver Steering Characteristics

Lateral control of an autonomous vehicle using integrated backstepping and sliding mode controller

Vehicle lateral control in the presence of uncertainty for lane change maneuver using adaptive sliding mode control with fuzzy boundary layer

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