Robust Principal Component Algorithms for High-Order Fast-Sampled Systems

Principal component active control is an important technique for noise and vibration reduction control problems. Most existing robust analysis regarding active control systems is based on the assumption of static open-loop behaviour, whose results can be very limited in practice. More promising results, exploiting integral quadratic constraints, have been reported recently and are more accurate and reliable for practical applications. However, depending on the nature of the system, such analysis may require one to establish the feasibility of an extremely large dimensional linear matrix inequality, effectively limiting the use of the result. This paper proposes an alternative approach, using model reduction methods, in which a set of IQC multipliers are obtained in a first step and the arising frequency domain inequality being verified in a second step. This two-step partition makes the approach much less computationally demanding for many complex practical systems. A detailed rotorcraft application is provided to illustrate the benefits of the proposed approach.