This paper proposes an integrated fault tolerant control scheme for a class of systems, modelled in a linear parameter-varying
(LPV) framework and subject to sensor faults. The gain in the LPV sliding mode observer (SMO) and the gain in the LPV
static feedback controller are synthesized simultaneously to optimize the performance of ...
This paper proposes an integrated fault tolerant control scheme for a class of systems, modelled in a linear parameter-varying
(LPV) framework and subject to sensor faults. The gain in the LPV sliding mode observer (SMO) and the gain in the LPV
static feedback controller are synthesized simultaneously to optimize the performance of the closed-loop system in an L2
sense. In the proposed scheme, the sensor faults are reconstructed by the SMO and these estimates are subsequently used
to compensate the corrupted sensor measurements before they are used by the feedback controller. To address the synthesis
problem, an iterative algorithm is proposed based on a diagonalization of the closed-loop Lyapunov matrix at each iteration.
As a result the NP-hard, non-convex linear parameter-varying bilinear matrix inequality (LPV/BMI) associated with the
Bounded Real Lemma formulation, is simplified into a tractable convex LPV/LMI problem. A benchmark scenario, involving
the loss of the angle of attack sensor in a civil aircraft, is used as a case study to demonstrate the effectiveness of the scheme.