Contact Dr Adolfo Perrusquia Guzman
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 (14 peer-reviewed journal papers-PhD completion in 2020) in employing artificial intelligence techniques applied to dynamical systems.
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 is within the Human Machine Intelligence Research Group led by Prof. Weisi Guo.
I am always keen to hear from interested candidates in advanced control and intelligent applications for autonomous and cyber-physical systems. Candidates with strong background in control and intelligent techniques are extremely encouraged to contact me and have a discussion.
Adolfo Perrusquia is a Research Fellow in Reinforcement Learning for Engineering in the School of Aerospace, Transport and Manufacturing (SATM).
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 reiforcement learning applications. Since January 2021, He is teaching the Reinforcement Learning Laboratory of the M.Sc. in Applied Aritficial Intelligence.
Articles In Journals
- Flores-Campos JA, Perrusquía A, Gómez LH, González N & Armenta-Molina A (2021) Constant speed control of slider-crank mechanisms: a joint-task space hybrid control approach, IEEE Access, 9, 65676-65687.
- Perrusquía A, Flores-Campos JA & Yu W (2021) Optimal sliding mode control for cutting tasks of quick-return mechanisms, ISA Transactions, Available online 28 April 2021.