Robust fault tolerant model predictive control (RFTMPC) for high order systems

Lead Research Organisation: Imperial College London
Department Name: Electrical and Electronic Engineering


EPSRC area: Control Engineering
Industrial partner: Schlumberger Cambridge Research Limited
Case voucher: 17100038

Process control and optimization are becoming more important as automation and robotics are incorporated into complex safety-critical processes; for example, the construction of oil and gas wells.

Model predictive control (MPC) is an online control strategy for problems with hard constrains. It is most suitable for lower order known models, since the resulting optimization is a quadratic program.

Complex models need to be replaced by approximations. Approximation, parametric uncertainties of the original model and disturbances, requires Robust MPC. In safety critical applications, a fault tolerance measure is necessary.

The project aims to integrate model identification and approximation, incorporate uncertainties and fault scenarios into a Robust Fault Tolerant MPC problem. A further aim is to develop feasible relaxation based solution algorithms to the resulting non convex optimization problems, and apply in industrial problems of interest to Schlumberger (e.g.: curvature control).


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/R512540/1 01/10/2017 30/09/2021
2103076 Studentship EP/R512540/1 15/01/2018 30/09/2021 Anastasis Georgiou Georgiou