Advanced control systems engineering

Safety Embedded Autonomy: From Barrier States to Intelligent and Differentiable Control

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B9 R2325

This talk presents a safety-embedded approach to autonomy that builds safety directly into the control dynamics through Barrier States, then extends it with adaptive and differentiable control so that autonomous systems can stay safe, adapt to changing dynamics, and improve through physical interaction.

Integrated Learning and Optimization and Computationally Efficient Model Predictive Control for Power Systems Applications

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This dissertation studies novel model predictive control and integrated learning-optimization methodologies that enable computationally efficient, robust, and scalable real-time control and decision-making in modern power systems, improving renewable microgrid operation, electricity market performance, and optimization under uncertainty.

Imran Pervez

Imran Pervez's research focuses on integrating machine learning, optimization, and advanced control methodologies for power systems, with applications in renewable energy integration, stochastic and robust model predictive control, and data-driven decision-making to improve the efficiency, reliability, and economic performance of modern energy systems.

Marine Autonomy: Research and Applications

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B18 Fellowship Hall

Featured Sessions
Abstract Marine autonomy does not have as many constraints as there are on land but there are still challenges that need to be overcome to make the most of current and future opportunities. My talk will concentrate on the research works done in the marine autonomous systems research group at Plymouth where the focus of the research group has been towards application of AI techniques to the navigation, guidance and control of autonomous marine vehicles. The talk will also discuss future challenges in the marine autonomy areas and their likely solutions. Biography Dr Sanjay Sharma, Associate