Adaptive Minimum-Entropy Hybrid Compensation for Compound Faults of Non-Gaussian Stochastic Systems

Kaiyu Hu,Fuyang Chen,Changyun Wen,Zian Cheng

doi:10.1109/access.2019.2936100

Kaiyu Hu, Fuyang Chen + Show 2 more

Open Access

https://doi.org/10.1109/access.2019.2936100

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Abstract

This study investigated minimum-entropy hybrid fault-tolerant control (FTC) theory for non-Gaussian stochastic systems with compound faults. After fuzzy linearization for the singular systems, the output probability density function (PDF) is generated by rational square root B-splines. To deal with the compound faults consisting of single sensor fault and intermittent multiple actuator faults, an active-passive hybrid adaptive FTC scheme is proposed: A passive compensation function can directly reconstruct the algorithm to mask the sensor fault; then, actuator fault estimation accurately tracks the multiple actuator faults. Hence, the hybrid FTC combines estimated information and passive compensation simultaneously implements active actuator fault repair and passive sensor fault shielding. A novel variable parameter algorithm that mimics animal predation behavior is designed and incorporated into learning rates, making the controller more sensitive to the incipient deviations in actuator faults. Finally, with the optimal indicators containing entropy and mean of non-Gaussian PDF, the minimum-entropy FTC is achieved. Lyapunov and indicator functions prove the stability, simulation verifies the effectiveness of the methods.

Highlights

Non-Gaussian stochastic distribution control (SDC) has a wide range of application scenarios; it can describe industrial processes such as parallel vibration tables, aerospace engine tail flames, and pulper fiber distribution control [1]–[4]. Studying such systems’ fault diagnosis (FD) and fault-tolerant control (FTC) can compensate for the faults that cannot be displayed by traditional system outputs, provide new ideas for anti-laser hypersonic vehicles and industrial process safety design
There are some under consequential issues in this research area, including FTC of compound faults, estimation of incipient or intermittent faults, and optimal control when the expected output is unknown
The hybrid FTC scheme developed in this study effectively implements automatic repair of compound faults of non-Gaussian stochastic systems

Summary

INTRODUCTION

Non-Gaussian stochastic distribution control (SDC) has a wide range of application scenarios; it can describe industrial processes such as parallel vibration tables, aerospace engine tail flames, and pulper fiber distribution control [1]–[4]. K. Hu et al.: Adaptive Minimum-Entropy Hybrid Compensation for Compound Faults of Non-Gaussian Stochastic Systems and component faults. In [20], an interval sliding mode observer and an incipient sensor fault detection method were proposed for a class of nonlinear control systems with observer unmatched uncertainties. We design a novel hybrid FTC scheme, where non-Gaussian stochastic systems can achieve the optimal FTC of entropy index under the compound faults and unknown expected PDF conditions. The main contributions of this study include: 1) Construction of fuzzy singular non-Gaussian systems and a performance index with mean and entropy, designing fusion adaptive control combining fuzzy premise variables and fault information to achieve the minimum-entropy optimal control. 3) Design of a novel variable parameter algorithm imitating animal predation behavior, making the systems more sensitive to time-varying step faults with incipient deviation, improving the estimation and FTC performance.

MODEL SYSTEMS AND FAULTS

FAULT ESTIMATION

HYBRID COMPENSATION

PREY ALGORITHM

SIMULATION

CONCLUSION