A data‐driven sensor fault‐tolerant control scheme based on subspace identification

We study the sensor fault estimation and accommodation problems in a data‐driven setting, leading to a data‐driven sensor fault‐tolerant control scheme. First, we formulate the fault estimation problem as a finite‐horizon minimax ‐optimization problem in a data‐driven setup, whose solution yields the fault estimate. The estimated fault is then used for output compensation. This compensated output and the experimental input are used to achieve certain control objectives in a data‐driven setting. Next, the data‐driven fault estimation and control problems are solved using a subspace predictor‐based approach. Finally, the proposed algorithm is applied to the steering subsystem of the remotely operated underwater vehicle.