Model Predictive Control based Dynamic Path Tracking of a Four-Wheel Steering Mobile Robot

Fnadi, Mohamed; Plumet, Frédéric; Benamar, Faiz

doi:10.1109/iros40897.2019.8967627

Cited by 22 publications

(13 citation statements)

References 18 publications

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“…Note that this simplification can actually result in large errors in the presence of non-negligible external disturbances, which may cause the designed controller based on Equation (15) fail to achieve desired performance on the original system. Therefore, we need to design an algorithm that is robust enough to overcome the errors caused by the simplification.…”

Section: Proof Of Flatnessmentioning

confidence: 99%

“…To further demonstrate that the proposed schemes have strong robustness and that all the simplifications made to derive the flat system are justifiable, we follow the same simulation procedure and apply the control inputs obtained from the simplified system (15) to a more realistic model (A1)-(A5) without re-tuning the controllers' parameters. Simulation results are presented hereafter.…”

Section: Data Availability Statementmentioning

confidence: 99%

“…Currently, the most commonly used tracking algorithms are proportional-integral-derivative (PID) control, sliding mode control and model predictive control. [13][14][15][16] These methods either rely heavily on the parameters or on an accurate model, and thereby are not robust enough to obtain good tracking results under varying model parameters and disturbances. In addition, their control performances may further deteriorate with sensor noise.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Trajectory planning and tracking for four‐wheel steering vehicle based on differential flatness and active disturbance rejection controller

Xia

Lin

Zhang

et al. 2021

Adaptive Control & Signal

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To address the difficulties of under-actuation, nonlinearity, strong coupling, disturbances and measurement noise in four-wheel steering (4WS) vehicle tracking control, this paper presents an innovative control strategy based on differential flatness and linear active disturbance rejection controller (LADRC). The flatness property of 4WS vehicle is derived from a simplified dynamic model that ignores some complicated dynamics and external disturbances. The impact of these neglected components on the system is treated as part of the total-disturbance, which is estimated by the observer and compensated by LADRC. Three flatness-based control schemes, including feedback linearization controller with PID corrective term, traditional LADRC and LADRC with robust generalized proportional integral observer (RGPIO), are designed and investigated in this paper. Moreover, an overtaking scenario is selected as an application example for the tracking controllers, and the corresponding trajectory planner is designed.The tracking performances of these schemes for the overtaking trajectory are simulated on a three-degree-of-freedom dynamic model and a more complete dynamic model of the 4WS vehicle, respectively. Results show the effectiveness of these schemes, especially the excellent tracking performance of LADRC with RGPIO in the presence of strong external disturbances and measurement noise.

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Section: Proof Of Flatnessmentioning

confidence: 99%

Section: Data Availability Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Trajectory planning and tracking for four‐wheel steering vehicle based on differential flatness and active disturbance rejection controller

Xia

Lin

Zhang

et al. 2021

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…However, it is difficult for this simple model to maintain performance when the curvature or acceleration increases. To prevent the deviation from the linear section of tire force, an MPC with slip angle constraints has been proposed [48]. In addition, a multi-model based MPC was proposed to improve the performance in various driving conditions [49].…”

Section: Introductionmentioning

confidence: 99%

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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“…In [19] a nonlinear MPC was used to track fixed wing UAV in extreme manoeuvres (fast turn, sharp turn, flip), in case of actuators failures and for obstacle avoidance. The MPC was also used for off-road unmanned ground vehicle path tracking with taking into account sliding and steering constrains [20].…”

Section: Introductionmentioning

confidence: 99%

System Identification and LQR Controller Design with Incomplete State Observation for Aircraft Trajectory Tracking

2020

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This paper presents a controller design process for an aircraft tracking problem when not all states are available. In the study, a nonlinear-transport aircraft simulation model was used and identified through Maximum Likelihood Principle and Extended Kalman Filter. The obtained mathematical model was used to design a Linear–Quadratic Regulator (LQR) with optimal weighting matrices when not all states are measured. The nonlinear aircraft simulation model with LQR controller tracking abilities were analyzed for multiple experiments with various noise levels. It was shown that the designed controller is robust and allows for accurate trajectory tracking. It was found that, in ideal atmospheric conditions, the tracking errors are small, even for unmeasured variables. In wind presence, the tracking errors were proportional to the wind velocity and acceptable for small and moderate disturbances. When turbulence was present in the experiment, state variable oscillations occurred that were proportional to the turbulence intensity and acceptable for small and moderate disturbances.

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Model Predictive Control based Dynamic Path Tracking of a Four-Wheel Steering Mobile Robot

Cited by 22 publications

References 18 publications

Trajectory planning and tracking for four‐wheel steering vehicle based on differential flatness and active disturbance rejection controller

Trajectory planning and tracking for four‐wheel steering vehicle based on differential flatness and active disturbance rejection controller

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

System Identification and LQR Controller Design with Incomplete State Observation for Aircraft Trajectory Tracking

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