Publication

Set-valued observer-based active fault-tolerant model predictive control

Journal Article (2017)

Journal

Optimal Control Applications and Methods

Pages

683–708

Volume

38

Number

5

Doc link

http://dx.doi.org/10.1002/oca.2284

File

Download the digital copy of the doc pdf document

Abstract

This paper proposes an integrated actuator and sensor active fault-tolerant model predictive control (FTMPC) scheme. In this scheme, fault detection (FD) is implemented by using a set-valued observer, fault isolation (FI) is done by set manipulations and fault-tolerant control (FTC) is carried out through the design of a robust model predictive control (MPC) law. In this paper, a set-valued observer is used to passively complete the FD task while FI is actively performed by making use of the constraint-handling capability of robust MPC. The set-valued observer is chosen to implement fault detection and isolation (FDI) due to its simple mathematical structure that is not affected by the type of faults such as sensor, actuator and system-structural faults. This means that only one set-valued observer is needed to monitor all considered actuator and sensor statuses (health and fault) and to carry out the FDI task instead of using a bank of observers (each observer matching a health/fault status). Furthermore, in the proposed scheme, the advantage of robust MPC is that it can effectively deal with system constraints, disturbances and noises and allow to implement an active FI strategy, which can improve FI sensitivity when compared with the passive FI methods. Finally, a case study based on the well-known two-tank system is used to illustrate the effectiveness of the proposed FTMPC scheme.

Categories

automation, control theory, observability.

Author keywords

Actuator and Sensor Faults, Fault Detection and Isolation, Fault-tolerant Control, Model Predictive Control, Set-valued Observer

Scientific reference

F. Xu, V. Puig, C. Ocampo-Martínez and X. Wang. Set-valued observer-based active fault-tolerant model predictive control. Optimal Control Applications and Methods, 38(5): 683–708, 2017.