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Optimization of Autonomous Vehicle System using Advanced Optimal Control Algorithms

F.O. Philip- Kpae, L. E. Ogbondamati, J. M. Owolabi

Abstract


This study explores the application of an optimal control approach, specifically a Linear Quadratic Regulator (LQR), for enhancing speed accuracy in Advanced Driver Assistance Systems (ADAS). The purpose is to determine if LQR, compared to traditional Proportional Integral Derivative (PID) control, can achieve more precise speed tracking with minimal energy consumption and adaptability to disturbances. ADAS systems are essential for improving vehicle safety and efficiency, yet conventional controllers often face challenges in balancing accuracy and control effort under dynamic conditions, highlighting a need for more robust solutions. Utilizing an LQR framework with state cost and control effort weights of (Q = 1) and (R = 0.01) respectively, we conducted a speed-tracking simulation targeting a speed of 25 m/s. Results indicate that the LQR controller achieved an accuracy of up to 98%, significantly reducing speed tracking error while maintaining smooth control efforts over a 60-second simulation period. This balance of accuracy and control effort underscores the method’s efficiency. These findings suggest that policies promoting optimal control techniques in ADAS could enhance vehicle responsiveness to real-time conditions. The study advances knowledge on control precision and efficiency, supporting the integration of LQR for improved ADAS performance, safety, and reliability.


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