Dr Jonathan Mayo-Maldonado

My research develops and analyses data-driven control methods for electrified energy systems that must remain reliable despite incomplete models, noisy measurements, changing operating conditions, and unreliable data streams. The work spans control theory, power electronics, electrical machines and drives, and power systems, with particular emphasis on stability, converter control, voltage regulation, monitoring, diagnosis, and resilience. A recurring question in my work is: what can we guarantee from data, and what can we not guarantee?

I am originally from Mexico, where I began my engineering training with BSc and MEng degrees in Electronic and Electrical Engineering at Tecnológico Nacional de México. My early training influenced my approach to engineering problems by drawing attention to the assumptions behind a result, the limits of a model, and the gap between idealised descriptions and observed behaviour. This led me to research problems in which modelling, measurement, and control design have to be considered together.

In 2015, I completed my PhD in Electronic and Electrical Engineering at the University of Southampton. My thesis, *Switched Linear Differential Systems*, received the IET Doctoral Control and Automation Dissertation Prize. The work reinforced the importance of careful reasoning, patient checking, and a clear account of what a result does and does not cover. In control, a claim must be supported by an argument that remains valid when the system is no longer operating in the regime first assumed.

This perspective continues to inform my work in control. Classical control provides a precise language for reasoning about dynamics, feedback, stability, robustness, and performance. My work draws on Lyapunov methods, dissipativity theory, behavioural systems theory, and data-driven control to study how measured trajectories and operating data can support conclusions about stability, dissipativity, or performance. In this sense, data-driven control extends rather than replaces model-based reasoning: it asks how far control design can go when complete models are unavailable, too complex, or unreliable under changing operating conditions.

In power electronics and power networks, these questions appear in model-based and model-free control design for converters, converter networks, and active distribution systems. I study controller design under limited measurements, direct voltage regulation, stability guarantees, and data-driven methods for monitoring and regulation. Renewable generation, storage, flexible demand, and inverter-interfaced resources increasingly make control central to electrified power systems.

Electrical machines and drives present a related challenge in data-driven modelling across changing operating regimes. Speed, temperature, saturation, loading, and duty cycle can affect machine behaviour, so a model useful in one regime may not remain reliable in another. This motivates fault detection, fault-tolerant control, data-driven maximum-voltage-per-ampere and field-weakening operation, and optimal control across varied driving cycles and load changes.

Across these settings, cybersecurity is becoming part of the control problem itself. In digital, networked, measurement-dependent systems, commands and measurements cannot always be treated as trustworthy: compromised, delayed, or manipulated signals can enter the feedback loop and alter physical behaviour. The risk is therefore not only informational; it can become dynamic, with consequences for stability, protection, and continuity of operation. I study cyber-resilient monitoring and control in phasor-measurement-unit-informed power-system operation and in machines and drives, with the aim of detecting signal compromise and triggering corrective actions before the effect propagates through the controlled system.

I welcome enquiries from prospective PhD students who want to work on these questions in the context of electrified energy systems. Potential projects may develop data-driven guarantees for stability, dissipativity, or voltage regulation; design converter and drive controllers under limited or compromised sensing; or advance cyber-resilient monitoring and diagnosis in active networks and converter-rich systems. In each case, the aim is to make a clear technical contribution: a stability guarantee, a voltage-regulation method, a diagnosis approach, a fault-tolerant or cyber-resilient control strategy, or a controller whose assumptions can be checked against data.

My academic path has taken me from Tecnológico de Monterrey to the University of Sheffield and now to the University of Southampton. I have led and contributed to projects supported by SENER-CONACYT in Mexico and UKRI in the United Kingdom, published over 80 journal articles, and collaborated with academic and industrial partners. I am a Fellow of the Higher Education Academy and a Senior Member of the IEEE.

I value supervision as a central part of academic work. I have supervised and worked with more than ten doctoral researchers, and I regard communication as part of research from the beginning, not as something added at the end. My role as a supervisor is to help students turn broad technical interests into precise research questions, rigorous methods, and substantive research contributions.

I view doctoral training as a process of developing judgement: learning what can be claimed, what must be tested, and what should remain uncertain until the evidence is strong. Supervision works best when students value regular technical discussion, direct feedback, and an environment where ideas are examined carefully and constructively. My aim is for students to complete their PhD with strong publications and the judgement needed to work as independent researchers.

Students who thrive in this environment tend to be curious, disciplined, and technically ambitious. They do not need to arrive with a finished research agenda, but they should be willing to engage in careful reasoning, sustained reading, testing assumptions, responding to critique, and improving an idea until it is well supported. When contacting me about PhD supervision, it is useful to explain your academic background, the problem you want to study, why it matters, and how it connects to my work.

The aim is not to remove uncertainty from engineering systems, but to make it explicit, reason about it carefully, and design control methods whose assumptions can be checked and whose behaviour can be evaluated on real devices and networks.