A data-driven fault tolerant model predictive control with fault identification

Abstract

Most of the existing active control methodologies need a post-fault/failure model of the faulty process for online retuning the controller parameters, or reconfiguration. However, post-fault model identification process takes the precious post-fault time which delays the recovery procedure. A new data-driven fault tolerant model predictive control (MPC) is developed which does not need the post-fault model. In fact, the model identification and control (re)calculation are combined together and are performed simultaneously to efficiently use the critical post-fault/failure time. The proposed fault tolerant architecture is capable of the online fault identification and adapting effectively to the post-fault/failure model. Several simulations of hover control of an unmanned quad-rotor helicopter are performed to illustrate the usefulness of the proposed approach.