Parameter Estimation of PID Controlled Cruise Control System
Term
4. term
Education
Publication year
2023
Submitted on
2023-10-27
Pages
79
Abstract
In this thesis, our objective is to investigate methodologies for predicting the parameters of a PID-controlled cruise control system. Specifically, we delve into an examination and comparison of three distinct approaches; one involving numerical solutions, the second utilizing deep neural networks (DNN), and finally a combination of the two. We present several possible approaches for modeling the system as an ordinary differential equation (ODE), a prerequisite for utilizing numerical solutions. We outline the challenges of this process, as well as address issues unique to the explored system. We also address the critical concern of selecting an appropriate ODE solver, in addition to exploring optimization methods thereof. Additionally, we investigate hyper-parameter optimization of neural networks using Weight and Biases and delve into approaches for identifying and mitigating issues related to model saturation and overfitting. The ultimate goal is to compare these different methods. This comparison encompasses an assessment of prediction accuracy, computation speed, and an analysis of the respective strengths and weaknesses of the different approaches, with the intent of finding the one best suited for our task.
Keywords
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