Adaptive Fuzzy Observer-Based Active Fault-Tolerant Dynamic Surface Control for a Class of Nonlinear Systems With Actuator Faults

Shen, Qikun; Jiang, Bin; Cocquempot, Vincent

doi:10.1109/tfuzz.2013.2254493

Cited by 200 publications

(96 citation statements)

References 30 publications

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“…Proof First, according to Lemma 2, the uncertain nonaffine nonlinear fault system (4) can be directly expressed as the form of (9), and it follows from (9) that the dynamic system of tracking error (18) holds. Next, we define a Lyapunov function candidate as follows…”

Section: Theorem 1 Consider the Uncertain Nonaffine Nonlinear System mentioning

confidence: 99%

“…Besides, as common tools to deal with nonlinear systems, neural networks (NNs) and fuzzy logic systems (FLSs) have been widely applied to the adaptive control design [10][11][12][13][14][15][16][17] because of their universal approximation properties and adaption abilities. Accordingly, by combing backstepping design method, adaptive fuzzy fault-tolerant control algorithms were developed for nonlinear systems with actuator failures in [18,19]. For SISO and MIMO nonlinear strictfeedback systems with dead-zone nonlinearities, robust adaptive fuzzy backstepping controllers have been proposed in [17] and [20], respectively.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive fault-tolerant control of a class of nonaffine nonlinear systems with mismatched parameter uncertainties and disturbances

Yang

2015

Nonlinear Dyn

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This paper studies the problem of robust adaptive fuzzy fault-tolerant tracking control for a class of nonaffine nonlinear systems with mismatched parameter uncertainties, external disturbances and actuator faults. Without using complicated backstepping technique, a direct adaptive fuzzy tracking control approach is used to achieve the fault-tolerant control objective. First, to overcome the obstacle for the nonaffine nonlinear term with actuator faults, a nonaffine compensation term is constructed to eliminate the effect of the nonaffine fault parameters. Then, based on the implicit function theorem and the mean value theorem, a novel design scheme that converts the nonaffine system into the corresponding affine system is developed, and two effective fuzzy logic systems, which are introduced to approximate the appropriate nonlinear functions, are constructed to compensate the effect on mismatched parameter uncertainty and disturbance, respectively.

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Section: Theorem 1 Consider the Uncertain Nonaffine Nonlinear System mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive fault-tolerant control of a class of nonaffine nonlinear systems with mismatched parameter uncertainties and disturbances

Yang

2015

Nonlinear Dyn

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“…Fault detection uses up to now signal processing techniques [17], for operating phases, which highlights and localizes a dysfunction. Moreover, most of the techniques are based on observers [1,23] or analytical redundancy based on parity space [3].…”

Section: Introductionmentioning

confidence: 99%

Actuator Fault Estimation Based on LPV Unknown Input Observer for Induction Machine

Amrane¹,

Larabi²,

Aïtouche³

2017

STUD INFORM CONTROL

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This machine is not easy to control due to the nonlinearity of its dynamic model. Moreover, its state variables are not all measurable and it is under the influence of parametric variations. Therefore, a good control is necessary to guaranty the stability of its proper functioning. In the literature, most of the works deal with steady state vector control [8-10, 21, 25], Direct Torque Control (DTC) [19], Sliding Mode Control (SMC) [13,27]. In addition, nonlinear control applied to induction machine has been developed such in [14].The advantages of such approaches are their robustness to disturbances and their simplicity of implementation. The major disadvantage of the set-up is that all controls rely on the reliability of the measurement provided by sensors, Abstract: This paper presents an unknown inputs observer (UIO) applied to an induction machine (IM) in order to estimate the actuator faults and the state variables. Knowing that the machine used is highly nonlinear, a model based on LPV (linear parameter varying) system of the machine is used. Indeed, using the induction machine based on LPV model, we develop the structure of an observer where the actuator faults are the unknown inputs. The conditions for convergence are based on the Lyapunov theory that will ensure the stability. Based on the LMI (Linear Matrix Inequalities), the gains of the UIO subject to actuator faults are confirmed and then ensure the efficiency of our approach. The obtained results through simulations demonstrate the effectiveness of the proposed approach.

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“…In this paper a new simplified type-2 fuzzy neural network by using 3-dimentsinal membership functions is presented to overcome the problem of exponential growth of the number of fuzzy rules. Dynamic surface control has been developed in [20,21,22] to cope with this problem in the back-stepping approach 45 A c c e p t e d M a n u s c r i p t procedure.…”

Section: Introductionmentioning

confidence: 99%

A new robust observer-based adaptive type-2 fuzzy control for a class of nonlinear systems

Mohammadzadeh

Hashemzadeh

2015

Applied Soft Computing

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Adaptive Fuzzy Observer-Based Active Fault-Tolerant Dynamic Surface Control for a Class of Nonlinear Systems With Actuator Faults

Cited by 200 publications

References 30 publications

Adaptive fault-tolerant control of a class of nonaffine nonlinear systems with mismatched parameter uncertainties and disturbances

Adaptive fault-tolerant control of a class of nonaffine nonlinear systems with mismatched parameter uncertainties and disturbances

Actuator Fault Estimation Based on LPV Unknown Input Observer for Induction Machine

A new robust observer-based adaptive type-2 fuzzy control for a class of nonlinear systems

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