Vehicle System Dynamics Active Suspension Linear Quadratic Gaussian (LQG) Optimal Control

This research implements a Linear Quadratic Gaussian (LQG) control program for vehicle system dynamics active suspension, simulating and calculating optimal active suspension performance under road surface inputs within the Simulink environment. The program utilizes state-space modeling and optimal control theory to design the controller through Riccati equation solutions for weighting matrix optimization. In this study, we employed a vehicle system dynamics active suspension LQG control program to simulate and compute optimal active suspension performance under various road input conditions. Through Simulink simulations incorporating quarter-car or full-vehicle models, we implemented Kalman filtering for state estimation and linear quadratic regulator (LQR) design for optimal control law derivation, enabling comprehensive analysis of suspension system behavior and response characteristics. Compared to traditional passive suspension systems, active suspension systems demonstrate superior performance and enhanced control capabilities. The LQG control strategy achieves optimal performance by minimizing a quadratic cost function that balances ride comfort (body acceleration), road holding (tire deflection), and suspension travel constraints. Through comparative simulations with conventional passive suspension setups using standardized road profiles (such as ISO random road inputs), the advantages and improvements of active suspension systems become clearly evident. In conclusion, this research demonstrates the superiority of optimal active suspension performance under road surface inputs using vehicle system dynamics LQG control implementation in Simulink, with detailed comparisons against traditional passive suspension systems. The simulation results, including frequency response analysis and time-domain performance metrics, provide significant insights for enhancing vehicle suspension system design and performance optimization. The implementation includes functionality for parameter tuning, performance evaluation metrics calculation, and comparative visualization of system responses.