Areas of expertise

  • Aeronautical Systems
  • Autonomous Systems
  • Instrumentation, Sensors and Measurement Science
  • Mechatronics & Advanced Controls
  • Systems Engineering


Adolfo Perrusquía is an expert in reinforcement learning, especially for the control of dynamical systems (e.g., robotics, autonomous vehicles). In particular, he is expertise in combining classical nonlinear control theory with recent data-driven learning methods.

He has a M.Sc. and PhD. degrees in automatic control from the CINVESTAV-IPN (rank 2 research in Latin America) and the B.Eng. degree in Mechatronic Engineering from the IPN (rank 4 university in Mexico). He has published extensively in employing artificial intelligence techniques applied to dynamical systems. He was awarded by the Mexican Society of Artificial Intelligence with the third place for the best PhD thesis in Artificial Intelligence 2021.

He has been appointed Chair of the Task Force on Reinforcement Learning for Robots in the IEEE Computational Intelligence Society. Adolfo Perrusquía joined Cranfield in 2021 as a Research Fellow. He has been awarded by the Royal Academy of Engineering with a UK-IC postdoctoral fellowship in 2021. He is within the Human Machine Intelligence Research Group led by Prof. Weisi Guo.

Current activities

Adolfo Perrusquia is a Research Fellow in Reinforcement Learning for Engineering in the School of Aerospace, Transport and Manufacturing (SATM) and an UK-IC Postdoctoral Research Fellow.

His expertise is on theory and applications of both control and artificial intelligence. In particular, he is extremely interested in system identification, nonlinear control (which includes adaptive and robust control), robotics, deep learning and especially in reinforcement learning applications. Since January 2021, He is teaching some modules of the M.Sc. in Applied Artificial Intelligence.



Department for Transport




Articles In Journals

Conference Papers