Autonomous robots are increasingly improving at performing navigation tasks, however they will likely fail at some point or not perform as intended due to uncertainties or unforeseen situations in the real world. In such scenarios, explaining the robot's behaviour to humans is crucial to build trust and resolve potential issues. Recently, large language models (LLMs) have shown great potential in analysing robot log data, e.g., obtained in Robot Operating System (ROS), and providing users with useful explanations. Yet, these models often fail to consistently generate high quality answers. This study develops an approach using parameter-efficient fine-tuning (PEFT) to improve explanations generated by LLMs and tailoring them towards a target audience (expert, non-expert) and preferred lengths (short, medium, long). We collected ROS log data from the TIAGo robot in simulation, combined them with user questions, and corresponding answers generated using GPT-4o to create a dataset for fine-tuning Mistral 7B with PEFT. Furthermore, we use a panel of LLMs (GPT-4o, Mistral-Large, Llama3-8B) as judges to evaluate these explanations based on quality criteria and user study (N=17) to validate these results on a group of roboticists. Our findings show that personalisation significantly improves both the suitability of explanations, with personalised answers consistently outperforming non-personalised ones. Furthermore, tailored explanations achieved higher clarity and user understanding. Additionally, a single feedback loop iteration using textual feedback from LLMs further enhanced explanation relevance and contextual quality, demonstrating the value of iterative improvement in explainability systems, despite minor trade-offs in other criteria.