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A faulty sensor may lead to degraded system performance, system instability or even a fatal accident. On the other hand, the increasing need for safety and reliability has motivated the development of fault-tolerant control techniques. In this work, the sensor performance degradation due to its aging is modelled by the increment of sensor measurement noise covariance. The main contribution of this paper is the characterisation of the control synthesis conditions using parametrised linear matrix inequalities (PLMIs) for a multi-objective gain-scheduled noisy output-feedback controller that minimises the output cost on H 2 performance with satisfactory system stability, H ∞ performance and control input covariance constraints ( H 2 constraints on the control inputs) in the presence of sensor aging. The closed-loop system stability and performance, in terms of mixed H 2 / H ∞ performances, relative improvement, numerical complexity, computation time, and initial conditions response, are studied, and a numerical example is used to illustrate the effectiveness of the proposed control scheme. The synthesised controller guarantees not only the stability but also the closed-loop mixed H 2 / H ∞ performances, and it is feasible for real-time applications. [ABSTRACT FROM AUTHOR]

The vast majority of the existing work in gain-scheduling (GS) control literature assumes perfect knowledge of scheduling parameters. Generally, this assumption is not realistic since for practical control applications measurement noises are unavoidable. In this paper, novel synthesis conditions are derived to synthesise robust GS controllers with mixedperformance subject to uncertain scheduling parameters. The conditions are formulated in terms of parameterised bilinear matrix inequalities (PBMIs) that depend on varying parameters inside multi-simplex domain. The conditions providepracticalGS controllers independent of the derivatives of scheduling parameters. That is, the designed controllers are feasible for implementation. Since bilinear matrix inequality problems are intractable, an iterative PBMI algorithm is developed to solve the developed synthesis conditions. By the virtue of this algorithm, conservativeness reduction is achieved with few iterations. Examples are presented to illustrate the effectiveness of the developed conditions. Compared to other design methods from literature, the developed conditions achieve better performance. [ABSTRACT FROM AUTHOR]