Simultaneous disturbance attenuation and fault detection using proportional integral observers

Abstract

This paper considers the design of a proportional integral observer (PIO) for simultaneous disturbance attenuation and fault detection. Unlike a proportional observer, an integral observer alone suffices to achieve good convergence and filtering properties when sensor noise is present in the system. On the other hand, a proportional integral observer makes it possible to decouple the modeling uncertainties while estimating states and faults with satisfactory convergence properties. We show that a generalized P I 0 structure, a proportional integral fading observer (PIFO), facilitates the design procedure for achieving the above goal.

Robust proportional-integral Kalman filter design using a convex optimization method

“…(8) (9) where P is a symmetric positive definite solution, which leads to the error dynamics in Eq. (4) is asymptotically stable.…”

“…Several design methods for these PI observers have been introduced: pole-placement methods [6,7], eigenstructure assignments [12], H-infinity norm minimization problems [9,11], or minimum estimation error variance approaches [13,14]. For instance, Duan et al [12] introduced a parametric eigenstructure assignment method that provides complete degrees of freedom in designing PI observers, which leads to lower eigenvalue sensitivities.…”

“…This type of the observer scheme was first introduced by Wojciechowski [3] for single-input single-output systems and then extended to multivariable systems by Kaczorek [4] and Shafai and Carroll [5], addressing a more flexible way for performing design objectives. For example, this loop can be used to identify unknown inputs or nonlinearities [6], to increase the stability margin in the LTR design [7,8], to attenuate or decouple disturbances [9,10], or to detect sensor or actuator faults [9,11].…”

Proportional two Integral (P2I) observer synthesis for single track vehicle

“…In this context, including all methodologies (Luenberger observer, Takagi-Seguno based observer, Extended Kalman filter), very few studies exist [10], [20]. The present paper proposes a robust proportional two integral approach, [12], [11], [13], [17], helping in states observation of linear motorcycle model and the reconstruction of rider's action (couple or steering angle) under disturbances and system's uncertainties. Disturbances and the rider action are assumed to be almost affine.…”

Adaptive Fault Diagnosis of Hydraulic Actuation System

“…However, it is difficult to decoupling and determine the failure location actually if some random factors exist, for example the modeling error and disturbance. So it is necessary to study a robust way to separate the failure characteristics from estimated information effectively [3]. Directing to the disturbance existed in hydraulic actuation system, this paper presents the diagnostic observer based on PIO, which imports the integral item of residual error in observer to be suitable to the influence of random input [4].…”