Adjust method of nonlinear gain in four-wheel steering control using Sliding Mode Control

Kasahara, Misawa; Kanai, Yuki; Mori, Yuichi

doi:10.1109/iciea.2009.5138273

Cited by 2 publications

(5 citation statements)

References 10 publications

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“…In particular, small steering angle and constant velocity assumptions were frequently made to linearize a trigonometric function for steering inputs and to ensure the denominator divided by the velocity is constant [19,23,24]. In order to avoid the performance deterioration caused by the model linearization, the nonlinear control method has been introduced in designing the path and velocity tracking controllers [6][7][8][39][40][41][42][43][44][45]. Among the approaches using the non-linear control, a nonlinear MPC (NMPC) has been used to directly utilize the non-linear vehicle model.…”

Section: Discretization and Linearization Of The Vehicle Modelmentioning

confidence: 99%

“…These algorithms were designed for 4WS with conventional 4WD vehicle systems. In other words, 4WS of the base algorithms control the left and right wheels with identical steering angles as done in the previous studies [4][5][6]8,36,[38][39][40][41][42][43][44][45][46]. Base #1 is a simplified version of the proposed algorithm, which determines the FWS and RWS angles and a single driving torque.…”

Section: Base Algorithmsmentioning

confidence: 99%

“…Similarly, a parallel steering maneuver, which actuates the front and rear wheels at the same angle, was introduced to simplify the SMC algorithm [43]. Furthermore, several types of SMC algorithms were also used to allocate the control effort to the differential steering system by using integral SMC [8], adaptive multivariable super-twisting control [44], and non-linear gain adjustment [45]. The integrated SMC controller for steering and driving/braking was designed to compensate the external yaw moment needed to improve the path tracking performance [4].…”

Section: Introductionmentioning

confidence: 99%

“…This approach required the assumptions on the reference path in order to derive the derivatives of the lateral and heading error, which restricts the road conditions where performance can be achieved. In addition, the 4WIS was frequently regarded as a 4WS system, so control inputs were the front and rear steering angles, which were equally applied to the left and right-side wheels [4][5][6]8,36,[38][39][40][41][42][43][44][45]. Furthermore, the effect of the four-wheel independently driving (4WID) on the yaw motion of the vehicle was not considered when designing the path tracking controller.…”

Section: Introductionmentioning

confidence: 99%

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Model Predictive Control-Based Integrated Path Tracking and Velocity Control for Autonomous Vehicle with Four-Wheel Independent Steering and Driving

Jeong

Yim

2021

Electronics

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This paper presents an MPC-based integrated control algorithm for an autonomous vehicle equipped with four-wheel independent steering and driving systems. The objective of this research is to improve the performance of the path and velocity tracking controllers by distributing the control effort to the multiple actuators. The proposed algorithm has two modules: reference state decision and MPC-based vehicle motion controller. Reference state decision module determines reference state profiles consisting of yaw rate and velocity in order to overcome the limitation of the error dynamics-based path tracking controller, which requires several assumptions on the reference path. The MPC-based vehicle motion controller is designed with a linear time-varying vehicle model in order to optimally allocate the control effort to each actuator. A linear time-varying MPC is adopted to reduce computational burden caused by using a non-linear one. The effectiveness of the proposed algorithm is validated via simulation on MATLAB/Simulink and CarSim. The simulation results show that the proposed algorithm improves the reference tracking performance by effectively distributing the control effort to the steering angle and driving force of each actuator.

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Section: Discretization and Linearization Of The Vehicle Modelmentioning

confidence: 99%

Section: Base Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Model Predictive Control-Based Integrated Path Tracking and Velocity Control for Autonomous Vehicle with Four-Wheel Independent Steering and Driving

Jeong

Yim

2021

Electronics

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“…Therefore, considering this, the study on lateral motion control algorithm should focus on solutions to these problems. In Li et al 1 and Kasahara et al, 2 the related contents are studied directly at complicated nonlinearity and uncertainty mentioned above to design a lateral motion controller with robustness.…”

Section: Introductionmentioning

confidence: 99%

Optimal lane change motion of intelligent vehicles based on extended adaptive pseudo-spectral method under uncertain vehicle mass

Fang

Duan

2017

Advances in Mechanical Engineering

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Lane change operation is widely used in numerous traffic scenarios; to minimize lane change time and avoid collisions with other vehicles required by intelligent vehicles, an optimal lane change motion is proposed for the shortest time free lane change and emergency obstacle avoidance lane change. First, two optimal lane change motion models are constructed based on a 3-degree-of-freedom dynamical vehicle model. Because of uncertain parameters in the vehicle model, the optimal lane change motion problem is an uncertain optimal control problem. Subsequently, targeted at nonlinearity and uncertain parameters, an extended adaptive pseudo-spectral method is also presented on the basis of approximate and numerical integration of parameter space. Finally, solving the optimal lane change motion problem, optimization results, control results based on the nonlinear model predictive control algorithm, and experimental results are shown under circumstances of certain vehicle mass and uncertain vehicle mass. As for uncertain parameters, optimization results are featured with robustness. Thus, uncertain parameters need not be measured, and optimization calculation need not be carried out in real time. The algorithm put forward here could be adopted to solve an uncertain optimal control problem. The optimal lane change motion can be extended to apply in more complex operations like merging, double-lane change, entering/exiting highways, or overtaking another vehicle.

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Adjust method of nonlinear gain in four-wheel steering control using Sliding Mode Control

Cited by 2 publications

References 10 publications

Model Predictive Control-Based Integrated Path Tracking and Velocity Control for Autonomous Vehicle with Four-Wheel Independent Steering and Driving

Model Predictive Control-Based Integrated Path Tracking and Velocity Control for Autonomous Vehicle with Four-Wheel Independent Steering and Driving

Optimal lane change motion of intelligent vehicles based on extended adaptive pseudo-spectral method under uncertain vehicle mass

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