Application and Comparison of LQR, LQG, and State Observer Controllers in Vehicle Suspension
DOI:
https://doi.org/10.61582/nsf35m43Keywords:
Observadores de estado, LQR, LQG, modelos, sistemas, controlador, sistema de suspensiónAbstract
The use of controllers represents advanced control techniques applied in vehicle suspension to enhance stability, comfort, and driving safety [1]. These techniques allow for the design of active suspension systems that dynamically respond to road conditions and vehicle behavior. Observer-based control is used to estimate system state variables that are not directly measurable [2]. In the context of vehicle suspension, an observer can estimate body speed and damper forces based on displacement and acceleration measurements. LQR (Linear Quadratic Regulator) is an optimal control technique that minimizes a quadratic cost function, generally including terms related to displacement, speed, and actuator energy [3]. In vehicle suspension, LQR aims to balance passenger comfort (by minimizing body accelerations) and tire grip on the road (by minimizing wheel displacement). This is achieved by adjusting the forces generated by the suspension actuators so the system responds optimally to road disturbances [4]. LQG (Linear Quadratic Gaussian) extends LQR by incorporating a state observer (like the Kalman filter) to handle uncertainties in measurements and system models [5]. This is particularly useful in vehicle suspension, where measurements can be noisy and models may not be entirely accurate.
Effective control in vehicle suspension is crucial as it directly affects vehicle safety, comfort, and stability. Without proper control, the suspension may not respond adequately to changing road conditions, resulting in decreased passenger comfort and increased difficulty in maintaining tire grip on the road. This can lead to dangerous situations, especially under adverse driving conditions. Implementing advanced control techniques like LQR and LQG is essential for ensuring optimal performance of the suspension system, enhancing the driving experience and overall vehicle safety. This paper will analyze and implement these controllers in a vehicle suspension model, assessing their effectiveness against simulated road disturbances using platforms that replicate a real-road scenario. The results will allow for a comparison of the efficacy of each controller and determine which offers the best performance in terms of stability and driving comfort.
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