Model Predictive Control for Autonomous Driving Vehicles

Minh, Vu Trieu; Moezzi, Reza; Cýrus, Jindřich; Hlava, Jaroslav

doi:10.3390/electronics10212593

Cited by 30 publications

(10 citation statements)

References 19 publications

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“…In recent literature, different types of Model Predictive Control (MPC) controllers are used for autonomous vehicle robust steering control systems. The design of the MPC controller that utilized lateral and steering angle deviation, along with relative yaw angle to control steering angle for collision avoidance based on the LiDAR data is presented in [1]. MPC controller is used to compensate the side slip for improving the tracking performance for higher speeds [2], and its performance is evaluated based on the actuator's bandwidth [3].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Experimental Evaluation and Analysis of PID and MPC Controllers Using HIL Setup for Robust Steering System of Autonomous Vehicles

Gokul Krishnan,

Suresh Kumar,

Nassar Matara

et al. 2024

IEEE Access

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The development of autonomous vehicles has recently received substantial impetus, fueled by researchers and industry personnel. The need for powerful steering control in autonomous vehicles is critical for assuring the vehicles' safety and reliability. Robust steering control allows for precise and accurate maneuvering, allowing the vehicle to traverse complicated road conditions. Comparative research on the certification of a robust steering system for autonomous vehicles is presented in this paper. Traditional controllers (PD and PID) are compared with a modern Model Predictive Control (MPC) controller that uses a multi-turn potentiometer and incremental encoder for position feedback. The controllers are designed in MATLAB Simulink and deployed for real-time testing on a Speedgoat performance real-time target Hardware-in-the-Loop (HIL) machine. The study focuses on evaluating the steering system's real-time performance in terms of accuracy and robustness. The novelty is this work is carried out in a real experimental modified electric vehicle and presents real-time results obtained using the HIL machine. The research covers a thorough examination of the experimental hardware configuration, system identification, controller design, and data-gathering technologies. A significant contribution of this research is the use of the HIL machine for real-time performance testing of different controllers with different velocities and sample times, specifically in a speed breaker scenario. To analyze each controller's response, real-time response data is logged at a high sampling rate of 0.1 milliseconds. The research contributes to the advancement of driverless vehicles by providing insights into the optimal performance of steering systems. It also emphasizes the importance of real-time testing of different controllers' robust performance in ensuring human safety in driverless cars. INDEX TERMSSteering system, Autonomous vehicle, Model Predictive Control (MPC), Rapid control prototyping (RCP), Hardware-in-the-Loop (HIL), Robust control NOMENCLATURE PARAMETERS K p -Proportional Gain K i -Integral Gain K d -Derivative Gain N -Filter Coefficient u(t) -Output of PID Controller U * t (x(t)) -Output of MPC Controller J -Cost Function Nc -Control Horizon Np -Prediction Horizon Ts -Sample Time

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Section: Introductionmentioning

confidence: 99%

Real-Time Experimental Evaluation and Analysis of PID and MPC Controllers Using HIL Setup for Robust Steering System of Autonomous Vehicles

Gokul Krishnan,

Suresh Kumar,

Nassar Matara

et al. 2024

IEEE Access

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“…[2] presents a path-following control algorithm for a vehicle. Some controller design techniques include adaptive control [3], optimal control schemes [4,5], algorithms based on model predictive control (MPC) [6,7], and controllers using artificial intelligence algorithms as reinforcement learning [8], among others, such as [9][10][11][12]. On the other hand, other works were focused on taking advantage of the robustness of the sliding mode control (SMC) algorithms, as any vehicle will encounter the effect of disturbances due to external and internal factors, such as wind, road irregularities, noise in sensor data, model uncertainties, and parameter variations.…”

Section: Introductionmentioning

confidence: 99%

Path-Following Sliding Mode Controller for an Electric Vehicle Considering Actuator Dynamics

Torres-Romero,

Ruiz-Cruz,

González-Jiménez

2024

Machines

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This study introduced a novel path-following controller tailored to electric vehicles equipped with a steer-by-wire system, i.e., the steering angle of the vehicle was defined by an electrical actuator. The control objective was to force the proper steering angle of the vehicle, which permits following a desired path. The system presupposed that an external algorithm that utilized sensor data provided the lateral movement references while maintaining a steady longitudinal velocity for the vehicle. The proposed control scheme was based on a robust sliding mode steering controller to manage the vehicle’s lateral movement. Furthermore, a brushless DC (BLDC) motor was considered as the steering actuator, which was controlled by a field-oriented controller (FOC), which was based on four internal proportional–integral (PI) control loops for precise steering actuation. To assess the performance of the proposed control scheme, numerical simulations were obtained, which demonstrated its effectiveness in achieving the control objective.

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“…With the advancement of powerful computing devices, optimization-based control techniques have become integral in various autonomous mobile industries [14]. Model Predictive Control (MPC) is one such method widely used for path tracking of mobile robots, as it takes into consideration various constraints for optimal control inputs [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Design and Performance Comparison of FuzzyPID and Non-linear Model Predictive Controller for 4-Wheel Omni-drive Robot

PANTA

2024

Preprint

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Trajectory tracking for an Omni-drive robot presents a challenging task that demands an efficient controller design. To address the limitations of manual tuning, we introduce a self-optimizing controller named fuzzyPID, leveraging the analysis of responses from various dynamic and static systems. The rule-based controller design is implemented using Matlab/Simulink, and trajectory tracking simulations are conducted within the CoppeliaSim environment. Similarly, a non-linear model predictive controller(NMPC) is proposed to compare tracking performance with fuzzyPID. We also assess the impact of tunable parameters of NMPC on its tracking accuracy. Simulation results validate the precision and effectiveness of NMPC over fuzzyPID controller while trading computational complexity.

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Model Predictive Control for Autonomous Driving Vehicles

Cited by 30 publications

References 19 publications

Real-Time Experimental Evaluation and Analysis of PID and MPC Controllers Using HIL Setup for Robust Steering System of Autonomous Vehicles

Real-Time Experimental Evaluation and Analysis of PID and MPC Controllers Using HIL Setup for Robust Steering System of Autonomous Vehicles

Path-Following Sliding Mode Controller for an Electric Vehicle Considering Actuator Dynamics

Design and Performance Comparison of FuzzyPID and Non-linear Model Predictive Controller for 4-Wheel Omni-drive Robot

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