Sliding-mode controller for four-wheel-steering vehicle: Trajectory-tracking problem

Șolea, Răzvan; Filipescu, Adrian; Filipescu, Silviu; Dumitrascu, Bogdan

doi:10.1109/wcica.2010.5554625

Cited by 10 publications

(5 citation statements)

References 11 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%

“…By assigning two control points at the front and rear wheels of a vehicle, SMC can be applied to the front-and rear-wheel steering, respectively [7]. 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].…”

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%

See 3 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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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

View full text Add to dashboard Cite

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“…The PID control method is a typical representative of classical control algorithms, PID controllers were proposed in [ 28 , 29 , 30 ] and used these for the experimental evolution of control. Sliding mode controllers [ 31 , 32 ] are used for trajectory tracking. The fuzzy logic control [ 33 ] is used for a skid steering vehicle.…”

Section: Introductionmentioning

confidence: 99%

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

Gai-ning

Wen

et al. 2017

Sensors

116

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The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control.

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Discrete-Time Sliding-Mode Control of a Mobile Platform with Four Driving/Steering Wheels

Mînzu

Filipescu

Minca

2011

Lecture Notes in Electrical Engineering

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Sliding-mode controller for four-wheel-steering vehicle: Trajectory-tracking problem

Cited by 10 publications

References 11 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

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

Discrete-Time Sliding-Mode Control of a Mobile Platform with Four Driving/Steering Wheels

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