Bio

Fred Livingston joined the ISE Department in 2023 as an adjunct professor. When not in the office or co-working space, you can find him competing in challenges such as Badwater Ultra, IRONMAN, and Spartan.

Research Interests

Livingston’s research involves the advancement of cyber-physical systems for autonomy and advanced manufacturing. These systems utilized multi-agent robotics technologies, decentralized control, sensor fusion, optimal motion planning, edge-computing, reinforcement learning, blockchain, smart contracts and security. He firmly believes the key to success is physically and mentally pushing boundaries.

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Publications

• A Review of Advances in Smart Wheelchair Technology from 2016 Onwards , Proceedings of the 19th International Conference on Intelligent Autonomous Systems (IAS-19) (2025)
• A Transformer Architecture Including Reinforcement Learning for Smart Wheelchair Navigation , Proceedings of the 19th International Conference on Intelligent Autonomous Systems (IAS-19) (2025)
• Adaptive Dynamic Programming for Voltage Regulation in Standalone Photovoltaic Systems with Unknown Loads and Uncertain Dynamics , Texas Power and Energy Conference (TPEC 2025) (2025)
• Anticipating Household Rescue Demand in Hurricanes Using Socio-Demographic Data and Machine Learning , SSRN Electronic Journal (2025)
• Automated Post-Processing with Cyber-Physical Systems , APICAM 2025 -- International Conference on Additive Manufacturing (2025)
• Model-Free Optimal Control of PV-DC Microgrid Integration Using Value Iteration Algorithm , 2025 7TH GLOBAL POWER, ENERGY AND COMMUNICATION CONFERENCE, GPECOM (2025)
• PV Array Integration with Single-Phase AC Grid Utilizing Adaptive Dynamic Programming , IEEE Kansas Power and Energy Conference (KPEC 2025) (2025)
• PV Array Interface with DC Grid Implementation Using Value Iteration Algorithm for Adaptive Optimal Control in MPPT Reference Voltage Tracking , IEEE Kansas Power and Energy Conference (KPEC 2025) (2025)
• The Role NC State Plays in Modern Manufacturing , (2025)
• Using Machine Learning for Tool Life Optimization in Manufacturing , Manufacturing Science and Engineering Conference (2024)

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