Robust MPC for actuator-fault tolerance using set-based passive fault detection and active fault isolation
Journal Article (2017)
International Journal of Applied Mathematics and Computer Science
In this paper, a fault-tolerant control (FTC) scheme is proposed for actuator faults, which is built upon tube-based model predictive control (MPC) as well as set-based fault detection and isolation (FDI). In the class of MPC techniques, tube-based MPC can effectively deal with system constraints and uncertainties with relatively low computational complexity compared with other robust MPC techniques such as min-max MPC. Set-based FDI, generally considering the worst case of uncertainties, can robustly detect and isolate actuator faults. In the proposed FTC scheme, fault detection (FD) is passive by using invariant sets, while fault isolation (FI) is active by means of MPC and tubes. The active FI method proposed in this paper is implemented by making use of the constraint-handling ability of MPC to manipulate the bounds of inputs. After the system has been detected to become faulty, the input-constraint set of the MPC controller is adjusted to actively excite the system for achieving FI guarantees on-line, where an active FI-oriented input set is designed off-line. In this way, the system can be excited in order to obtain more extra system-operating information for FI than passive FI approaches. Overall, the objective of this paper is to propose an actuator MPC scheme with as little as possible of FI conservatism and computational complexity by combining tube-based MPC and set theory within the framework of MPC, respectively. Finally, a case study is used to show the effectiveness of the proposed FTC scheme.
automation, control theory, optimisation.
fault detection, fault isolation, set-theoretic method, fault-tolerant control, model predictive control
F. Xu, V. Puig, C. Ocampo-Martínez, S. Olaru and S. Niculescu. Robust MPC for actuator-fault tolerance using set-based passive fault detection and active fault isolation. International Journal of Applied Mathematics and Computer Science, 27(1): 43-61, 2017.