Robust Path Tracking Control with Lateral Dynamics Optimization: A Focus on Sideslip Reduction and Yaw Rate Stability Using Linear Quadratic Regulator and Genetic Algorithms
Currently, one of the most important challenges facing autonomous vehicles’ development due to varying driving conditions is effective path tracking while considering lateral stability. To address this issue, this study proposes the optimization of the linear quadratic regulator (LQR) control system...
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| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Vehicles |
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| Online Access: | https://www.mdpi.com/2624-8921/7/2/50 |
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| author | Karrar Y. A. Al-bayati Ali Mahmood Róbert Szabolcsi |
| author_facet | Karrar Y. A. Al-bayati Ali Mahmood Róbert Szabolcsi |
| author_sort | Karrar Y. A. Al-bayati |
| collection | DOAJ |
| description | Currently, one of the most important challenges facing autonomous vehicles’ development due to varying driving conditions is effective path tracking while considering lateral stability. To address this issue, this study proposes the optimization of the linear quadratic regulator (LQR) control system by using the genetic algorithm (GA) to support the vehicle in following the predefined path accurately, minimizing the sideslip, and stabilizing the vehicle’s yaw rate. The dynamic system model of the vehicle is represented based on yaw rate angle, lateral speed, and vehicle sideslip angle as the variables of the state space model, with the steering angle as an input parameter. Using the GA to optimize the LQR control by tuning the weighting of the <i>Q</i> and <i>R</i> matrices led to enhancing the system response and minimizing deviation errors via a proposed cost function of GA. The simulation results were obtained using MATLAB/Simulink 2024a, with a representation of a predefined path as a Gaussian path. Under external and internal disturbances, such as road conditions, lateral wind, and actuator delay, the model demonstrates improved tracking performance and reduced sideslip angle and lateral acceleration by adjusting the longitudinal vehicle speed. This work highlights the effectiveness of robust control in addressing path planning, driving stability, and safety in autonomous vehicle systems. |
| format | Article |
| id | doaj-art-8f16111ba9294514968942f47c4dc505 |
| institution | Kabale University |
| issn | 2624-8921 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Vehicles |
| spelling | doaj-art-8f16111ba9294514968942f47c4dc5052025-08-20T03:29:39ZengMDPI AGVehicles2624-89212025-05-01725010.3390/vehicles7020050Robust Path Tracking Control with Lateral Dynamics Optimization: A Focus on Sideslip Reduction and Yaw Rate Stability Using Linear Quadratic Regulator and Genetic AlgorithmsKarrar Y. A. Al-bayati0Ali Mahmood1Róbert Szabolcsi2Doctoral School on Safety and Security Sciences, Óbuda University, 1081 Budapest, HungaryDoctoral School on Safety and Security Sciences, Óbuda University, 1081 Budapest, HungaryKandó Kálmán Faculty of Electrical Engineering, Óbuda University, 1034 Budapest, HungaryCurrently, one of the most important challenges facing autonomous vehicles’ development due to varying driving conditions is effective path tracking while considering lateral stability. To address this issue, this study proposes the optimization of the linear quadratic regulator (LQR) control system by using the genetic algorithm (GA) to support the vehicle in following the predefined path accurately, minimizing the sideslip, and stabilizing the vehicle’s yaw rate. The dynamic system model of the vehicle is represented based on yaw rate angle, lateral speed, and vehicle sideslip angle as the variables of the state space model, with the steering angle as an input parameter. Using the GA to optimize the LQR control by tuning the weighting of the <i>Q</i> and <i>R</i> matrices led to enhancing the system response and minimizing deviation errors via a proposed cost function of GA. The simulation results were obtained using MATLAB/Simulink 2024a, with a representation of a predefined path as a Gaussian path. Under external and internal disturbances, such as road conditions, lateral wind, and actuator delay, the model demonstrates improved tracking performance and reduced sideslip angle and lateral acceleration by adjusting the longitudinal vehicle speed. This work highlights the effectiveness of robust control in addressing path planning, driving stability, and safety in autonomous vehicle systems.https://www.mdpi.com/2624-8921/7/2/50path trackingsideslip minimizationyaw rate controlLQR controllergenetic algorithm optimizationvehicle dynamics |
| spellingShingle | Karrar Y. A. Al-bayati Ali Mahmood Róbert Szabolcsi Robust Path Tracking Control with Lateral Dynamics Optimization: A Focus on Sideslip Reduction and Yaw Rate Stability Using Linear Quadratic Regulator and Genetic Algorithms Vehicles path tracking sideslip minimization yaw rate control LQR controller genetic algorithm optimization vehicle dynamics |
| title | Robust Path Tracking Control with Lateral Dynamics Optimization: A Focus on Sideslip Reduction and Yaw Rate Stability Using Linear Quadratic Regulator and Genetic Algorithms |
| title_full | Robust Path Tracking Control with Lateral Dynamics Optimization: A Focus on Sideslip Reduction and Yaw Rate Stability Using Linear Quadratic Regulator and Genetic Algorithms |
| title_fullStr | Robust Path Tracking Control with Lateral Dynamics Optimization: A Focus on Sideslip Reduction and Yaw Rate Stability Using Linear Quadratic Regulator and Genetic Algorithms |
| title_full_unstemmed | Robust Path Tracking Control with Lateral Dynamics Optimization: A Focus on Sideslip Reduction and Yaw Rate Stability Using Linear Quadratic Regulator and Genetic Algorithms |
| title_short | Robust Path Tracking Control with Lateral Dynamics Optimization: A Focus on Sideslip Reduction and Yaw Rate Stability Using Linear Quadratic Regulator and Genetic Algorithms |
| title_sort | robust path tracking control with lateral dynamics optimization a focus on sideslip reduction and yaw rate stability using linear quadratic regulator and genetic algorithms |
| topic | path tracking sideslip minimization yaw rate control LQR controller genetic algorithm optimization vehicle dynamics |
| url | https://www.mdpi.com/2624-8921/7/2/50 |
| work_keys_str_mv | AT karraryaalbayati robustpathtrackingcontrolwithlateraldynamicsoptimizationafocusonsideslipreductionandyawratestabilityusinglinearquadraticregulatorandgeneticalgorithms AT alimahmood robustpathtrackingcontrolwithlateraldynamicsoptimizationafocusonsideslipreductionandyawratestabilityusinglinearquadraticregulatorandgeneticalgorithms AT robertszabolcsi robustpathtrackingcontrolwithlateraldynamicsoptimizationafocusonsideslipreductionandyawratestabilityusinglinearquadraticregulatorandgeneticalgorithms |