Actuator and sensor fault detection and isolation for nonlinear systems subject to uncertainty

Abstract

Summary This work addresses the problem of simultaneous actuator and sensor fault detection and isolation (FDI) for control affine nonlinear uncertain systems in the absence of measurement noise. The FDI is achieved by using a bank of filters, which utilize a subset of the measurements along with prescribed values of the control actuators to estimate states and compute expected process behavior. Residuals are next defined as the difference between the observed and expected behavior. Detectability conditions are developed, which, upon satisfaction, ensure that each residual remains sensitive to a subset of fault scenarios in the presence of uncertainty. To this end, first the ability of observers in providing bounded estimation error for a generalized class of nonlinear uncertain systems is rigorously established. These bounds allow determining thresholds that account for the impact of uncertainty on each residual. Finally, the ability of the proposed framework to achieve FDI by ensuring a unique residual breaching pattern for each fault scenario is established. The efficacy of the FDI framework subject to uncertainty and measurement noise is illustrated using a chemical reactor example.

Distributed fault diagnosis of heating, ventilation, and air conditioning systems

“…The expected trajectories are calculated using the following prediction model where is the state of the prediction model, and T is the prediction horizon: T = 1 if 0 < t k ≤ t k ′ , T = k − k ′ if , and T = T p if , with a positive integer T p being a chosen prediction horizon and t k ′ is the time where for t k ≥ t k ′ the with d being the desired bound on estimation error ( t k ′ thus simply denotes the time at which the observer has converged; see Refs. and for more on this). The prediction model is initialized at the state estimate at time t k ‐ T : .…”

Robust reliable H∞ control design for networked control systems with nonlinear actuator faults and randomly missing measurements

“…Thus, it is of great importance to design the most adequate controller, and it guarantees the performance of the NCSs subject to different kinds of actuator faults. Further, the robust reliable control analysis has been investigated effectively for continuous‐time systems with mixed actuator failures . By using the equivalent‐input disturbance‐based adaptive reliable control method, Sakthivel et al reported the problem of output tracking control for NCSs with actuator faults and its asymptotic stability.…”

“…Further, the robust reliable control analysis has been investigated effectively for continuous-time systems with mixed actuator failures. [15][16][17] By using the equivalent-input disturbance-based adaptive reliable control method, Sakthivel et al 18 reported the problem of output tracking control for NCSs with actuator faults and its asymptotic stability.…”

An online fault-diagnosis of electromagnetic actuator based on variation characteristics of load current

“…In this method, residuals are used to distinguish between system and observer outputs, but these methods are inaccurate and unsuitable for quantifying the magnitude of a fault. By using a bank of filters, with thresholds defined in a way that they explicitly account for the effect of uncertainty, a fault detection and isolation (FDI) approach was proposed to the actuator and sensor fault-diagnosis for nonlinear uncertain systems [34]; another simple and useful approach based on threshold was proposed in [35] by monitoring the error between the load current and current set point, but it is also difficult to find the effective thresholds.…”