Particle Swarm Optimization Of Direct Yaw Control Using Linear Quadratic Integral For Vehicle Stability

• Main Author: Omar, Mohd Firdaus
• Format: Thesis
• Language: English; English
• Published: 2020
• Subjects: T Technology (General); TL Motor vehicles. Aeronautics. Astronautics
• Online Access: http://eprints.utem.edu.my/id/eprint/25397/1/Particle%20Swarm%20Optimization%20Of%20Direct%20Yaw%20Control%20Using%20Linear%20Quadratic%20Integral%20For%20Vehicle%20Stability.pdf; http://eprints.utem.edu.my/id/eprint/25397/2/Particle%20Swarm%20Optimization%20Of%20Direct%20Yaw%20Control%20Using%20Linear%20Quadratic%20Integral%20For%20Vehicle%20Stability.pdf; http://eprints.utem.edu.my/id/eprint/25397/; https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=119685

Direct Yaw Control (DYC) for Vehicle Stability Control (VSC) system is recognized as an effective method to enhance the vehicle lateral stability during severe manoeuvre and critical situation. However, when driving in a high-speed environment, the effectiveness of the DYC might be degraded especially during wet road and sidewind disturbance. Thus, an appropriate controller strategy is essential to assist the DYC method to overcome these problems. Besides that, an optimization method in the controller gains playing vital role in the overall performance. In this study, the mathematical model for single-track car based on Sport Utility Vehicle (SUV) was developed. The bicycle model is used in this research to investigate and analyse the performance of DYC which taking into account the effect of uncertainties and disturbance occurred in the system. Then, a Linear Quadratic Integral (LQI) controller is proposed to assist the DYC for the vehicle’s yaw rate and sideslip angle regulation, specifically for the VSC. The LQI control strategy is formulated based on minimization of the linear quadratic cost function, where the system input and the states of the function are the measured performances. However, there are inadequate study involves for the LQI controller in the DYC system or vehicle handling and until to date, there is still no proper method for selecting the parameter gain for this control strategy. Therefore, in order to achieve an optimum performance of the DYC, the LQI controller gains have been properly selected by using the Particle Swarm Optimization (PSO) algorithm. Apart from optimizing the controller parameters, the LQI with optimization using PSO algorithm capable to maintain the stability of the vehicle in several manoeuvre circumstances. In the results of the Step Steering and the Slalom manoeuvre in dry road condition, all the controllers performed well even struck by sidewind. However, in the wet road test, there are significant changes in the performance of all controllers. The result shows the optimized LQI controller produced significant improvement in yaw rate performance for the Step Steering manoeuvre in wet road condition. Compared with the optimized Proportional-IntegralDerivative (PID) and the Linear Quadratic Regulator (LQR) controllers, the LQI controller outperform with 0.84% overshoot, 0.49s settling time, 0.3996s rise time and 0.5648x10-5rad/s steady state error. In the sideslip angle performance, the optimized LQI controller produced 0.84% overshoot and 1.33s settling time compared with optimized PID and LQR controllers. For the Slalom manoeuvre in the wet road condition, the optimized LQI controller has lower Root Mean Square Error (RMSE) which is 0.4220x10-2rad/s compared with optimized PID and LQR controllers in yaw rate performance. In the sideslip angle performance, the optimized LQI controllers produced 9.5408x10-6rad lower RMSE compared with optimized PID and LQR controllers. As a conclusion, the optimized LQI controller by using PSO has successfully performed as an effective control method to overcome the vehicle lateral stability issues.