Authors - Byron Albuja-Sanchez, Miguel Angel Lema Carrera, Luis Antonio Ortiz Parra Abstract - This study focuses on evaluating the capabilities of different large language models chatbots in the task of designing a PID controller for a third-order transfer function of a real-world vehicle’s cruise control system. Chatbots received a detailed prompt containing the system’s transfer function and the design’s goals in the form of overshoot and settling time constraints. Chatbots only received simulation-response information as feedback during the tuning process to test their predisposition to fix the errors without being specifically asked to do so. Results showed that chatbots have a good level of knowledge regarding basic control theory and basic tuning methods for PID controllers. Preferred tuning methods involved pole placement with dominant second order dynamics, Ziegler-Nichols and heuristic methodologies. Simulation results compared the controllers designed by chatbots with a PID tuned with ant lion optimizer algorithm, none of the evaluated chatbots outperformed the optimization-based benchmark controller. However, Gemini 3 Flash designed a controller which performance was close to the ant lion optimizer results. Chatbots’ underperformance was attributed to the following facts: no expert feedback was given to them to fix the observed flaws in the proposed designs, no specific methodologies were asked to be used in order to improve the results, and no specific instructions to redesign the controllers were given to chatbots in order to test their disposition to fix their errors. Results suggest that LLMs can assist in preliminary controller design tasks, although their effectiveness remains limited without expert-guided iteration and explicit optimization-oriented prompting.
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