Cannon Mark

Mark Cannon is a Professor of Engineering Science at the University of Oxford, affiliated with the Department of Engineering Science. His research focuses on control and optimization of systems with constraints and model or measurement uncertainty. He is particularly interested in constrained optimal control problems, which have applications in physical, environmental, and economic systems. His work explores feedback control systems that optimize predicted future behavior while accounting for constraints, aiming to improve performance and applicability. Cannon is a member of the Oxford Control Group and has been involved in various research projects related to robust adaptive control, stochastic model predictive control, and distributed optimization methods.

Mark Cannon is a researcher at the University of Oxford specializing in control theory and automatic control. His work focuses on Model Predictive Control (MPC), Robust Control, and methods to handle stochastic uncertainties in control systems. He has contributed to the development of probabilistic constrained MPC, stochastic tubes, and efficient nonlinear MPC algorithms. His research addresses challenges in system estimation, control design, and optimization for nonlinear and linear systems with multiplicative and additive uncertainties. Cannon's publications emphasize the integration of probabilistic distributions and recursive model updates to enhance control performance and robustness.