Nonlinear actuator fault estimation observer: An inverse system approach via a T-S fuzzy model

Xu, Dazhuan; Jiang, Bin; Shi, Peng

doi:10.2478/v10006-012-0014-9

Cited by 57 publications

(33 citation statements)

References 43 publications

(50 reference statements)

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“…However, fuzzy TS models, which are used here as residual generators, could be exploited for fault signal reconstruction, as shown, e.g., by Xu et al (2012), in the same way as for neural networks (Korbicz et al, 2004).…”

Section: Actuatorsmentioning

confidence: 99%

Residual generator fuzzy identification for automotive diesel engine fault diagnosis

Simani¹

2013

International Journal of Applied Mathematics and Computer Science

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Safety in dynamic processes is a concern of rising importance, especially if people would be endangered by serious system failure. Moreover, as the control devices which are now exploited to improve the overall performance of processes include both sophisticated control strategies and complex hardware (input-output sensors, actuators, components and processing units), there is an increased probability of faults. As a direct consequence of this, automatic supervision systems should be taken into account to diagnose malfunctions as early as possible. One of the most promising methods for solving this problem relies on the analytical redundancy approach, in which residual signals are generated. If a fault occurs, these residual signals are used to diagnose the malfunction. This paper is focused on fuzzy identification oriented to the design of a bank of fuzzy estimators for fault detection and isolation. The problem is treated in its different aspects covering the model structure, the parameter identification method, the residual generation technique, and the fault diagnosis strategy. The case study of a real diesel engine is considered in order to demonstrate the effectiveness the proposed methodology.

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Section: Actuatorsmentioning

confidence: 99%

Residual generator fuzzy identification for automotive diesel engine fault diagnosis

Simani¹

2013

International Journal of Applied Mathematics and Computer Science

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“…Although AFTC is the topic of the proposed FTC scheme, this paper focuses more on the FD and FI tasks and assumes that actuator-fault magnitudes are known in advance. However, for FTC based on fault estimation and accommodation, the readers are referred to the works by Jiang and Chowdhury (2005), Jiang et al (2006) or Xu et al (2012). Additionally, due to a relatively low complexity and the ability of dealing with system constraints, MPC is chosen as the control strategy for the proposed scheme (Boskovic and Mehra, 2002;Maciejowski, 1999;Yang and Maciejowski, 2015).…”

Section: Introductionmentioning

confidence: 99%

Robust Mpc for Actuator–Fault Tolerance Using Set–Based Passive Fault Detection and Active Fault Isolation

Puig

Ocampo‐Martínez

et al. 2017

International Journal of Applied Mathematics and Computer Science

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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, tubebased 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.

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“…Usually, this kind of DOBs are designed for linear systems since the inverse models are obtained easily from transfer functions [20]. In nonlinear systems, the inverse system can be obtained under the very limited condition.…”

mentioning

confidence: 99%

“…An inverse system of IPMSM is obtained as a convex combination of the local linear inverse systems [16,20]. The existence condition of an inverse system in the state space is to have a input direct through matrix D, however most systems including IPMSM have no such a matrix.…”

mentioning

confidence: 99%

Robust Control of IPMSM Using T-S Fuzzy Disturbance Observer

Kim

Li²,

Park³

et al. 2015

Journal of the Korea Institute of Information and Communication

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To improve the control performance of the IPMSM, a novel nonlinear disturbance observer is proposed by using the T-S fuzzy model. A T-S fuzzy model is the combination of local linear models considered at each operating point. Usually the inverse model is easy to obtain in linear systems but not in nonlinear systems. To design a nonlinear disturbance observer, a nonlinear inverse model is obtained based on nonlinear inverse model which is the fuzzy combination of the local linear inverse models. The proposed DOB is used with a PDC controller which is one of the T-S fuzzy controller, and its performance improvement is shown from the simulation results.

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Nonlinear actuator fault estimation observer: An inverse system approach via a T-S fuzzy model

Cited by 57 publications

References 43 publications

Residual generator fuzzy identification for automotive diesel engine fault diagnosis

Residual generator fuzzy identification for automotive diesel engine fault diagnosis

Robust Mpc for Actuator–Fault Tolerance Using Set–Based Passive Fault Detection and Active Fault Isolation

Robust Control of IPMSM Using T-S Fuzzy Disturbance Observer

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