Neural-Network-Based Set-Membership Fault Estimation for 2-D Systems Under Encoding-Decoding Mechanism

The problem of quick and accurate fault estimation in nonlinear systems is addressed in this article by combining the technique of radial basis function neural network (RBFNN) and global fast terminal sliding mode control (GFTSMC) concept. A new strategy to update the neural network weights, by using the global fast terminal sliding surface instead of conventional error back propagation method, is introduced to achieve real time, quick and accurate fault estimation which is critical for fault tolerant control system design. The combination of online learning ability of RBFNN, to approximate any nonlinear function, and finite time convergence property of GFTSMC ensures quick detection and accurate estimation of faults in real time. The effectiveness of the proposed strategy is demonstrated through simulations using a nonlinear model of a commercial aircraft and considering a wide range of sensors and actuators faults. The simulation results show that the proposed method is capable of quick and accurate online fault estimation in nonlinear systems and shows improved performance as compared to conventional RBFNN and other techniques existing in literature.

Download Full-text

Adaptive neural network–based fault estimation for nonlinear systems with actuator faults in simultaneous multiplicative and additive forms

Transactions of the Institute of Measurement and Control ◽

10.1177/01423312211058549 ◽

2021 ◽

pp. 014233122110585

Author(s):

Yan Zhao ◽

Minhang Song ◽

Xiangguo Huang ◽

Ming Chen

Keyword(s):

Neural Network ◽

Fault Tolerant ◽

Fault Estimation ◽

Actuator Faults ◽

Closed Loop System ◽

Adaptive Neural Network ◽

Neural Controller ◽

Adaptive Parameter ◽

Practical Engineering ◽

Adaptive Neural Controller

Non-linearities and actuator faults often exist in practical systems which may degrade system performance or even lead to catastrophic accidents. In this article, a fault-tolerant compensation control strategy is proposed for a class of non-linear systems with actuator faults in simultaneous multiplicative and additive forms. First, radial basis function neural network is employed to approximate the system non-linearity. The approximation is achieved by only one adaptive parameter, which simplifies the computation burden. Then, by means of the backstepping technique, an adaptive neural controller is developed to cope with the adverse effects brought by the system non-linearity and actuator faults in multiplicative and additive forms. Meanwhile, the proposed control design scheme can guarantee that the considered closed-loop system is stable. The novelty of the article lies in that the system non-linearity, the additive actuator faults, and the multiplicative actuator faults that often exist in practical engineering are catered for simultaneously. Furthermore, compared with some existing works, the approximation of the system non-linearity is achieved by only one adaptive parameter for the purpose of reducing the computation burden. Therefore, its applicability is more general. Finally, a numerical simulation and a comparative simulation are carried out to show the effectiveness of the developed controller.

Download Full-text

Novel neural observer based fault estimation, reconstruction and fault-tolerant control scheme for nonlinear systems

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201830 ◽

2021 ◽

Vol 41 (1) ◽

pp. 355-386

Author(s):

Muhammad Taimoor ◽

Xiao Lu ◽

Wasif Shabbir ◽

Chunyang Sheng ◽

Muhammad Samiuddin

Keyword(s):

Neural Network ◽

Nonlinear Systems ◽

Fault Detection ◽

Fault Tolerant ◽

Fault Tolerant Control ◽

High Gain ◽

Fault Isolation ◽

Fault Estimation ◽

High Gain Observer ◽

Highly Nonlinear

This research is concerned with the adaptive neural network observer based fault approximation and fault-tolerant control of time-varying nonlinear systems. A new strategy for adaptively updating the weights of neural network parameters is proposed to enhance fault detection accuracy. Lyapunov function theory (LFT) is applied for adaptively updating the learning parameters weights of multi-layer neural network (MLNN). The purpose of using adaptive learning rates to update the weight parameters of MLNN is to obtain the global minima for highly nonlinear functions without increasing the computational complexities and costs and increase the efficacy of fault detection. Results of the proposed adaptive MLNN observer are compared with conventional MLNN observer and high gain observer. The effects of various faults or failures are studied in detail. The proposed strategy shows more robustness to disturbances, uncertainties, and unmodelled system dynamics compared to the conventional neural network, high gain observer and other existing techniques in literature. Fault tolerant control (FTC) schemes are also proposed to account for the presence of various faults and failures. Separate sliding mode control (SMC) based FTC schemes are designed for each observer to ensure stability of the faulty system. The suggested strategy is validated on Boeing 747 100/200 aircraft. Results demonstrate the effectiveness of both the proposed adaptive MLNN observer and the FTC based on the proposed adaptive MLNN compared to the conventional MLNN, high gain observer and other existing schemes in literature. Comparison of the performance of all the strategies validates the superiority of the proposed strategy and shows that the FTC based on proposed adaptive MLNN strategy provides better robustness to various situations such as disturbances and uncertainties. It is concluded that the proposed strategy can be integrated into the aircraft for the purpose of fault diagnosis, fault isolation and FTC scheme for increasing the performance of the system.

Download Full-text

A GRAY-BOX NEURAL NETWORK-BASED MODEL IDENTIFICATION AND FAULT ESTIMATION SCHEME FOR NONLINEAR DYNAMIC SYSTEMS

International Journal of Neural Systems ◽

10.1142/s0129065713500251 ◽

2013 ◽

Vol 23 (06) ◽

pp. 1350025 ◽

Cited By ~ 19

Author(s):

ZHAOHUI CEN ◽

JIAOLONG WEI ◽

RUI JIANG

Keyword(s):

Neural Network ◽

Dynamic Systems ◽

Attitude Control ◽

Nonlinear Dynamic ◽

Model Identification ◽

Nonlinear Dynamic Systems ◽

Fault Estimation ◽

Attitude Control System ◽

Object System ◽

Fault Parameters

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

Download Full-text