Yingchun Zhang

Fault Reconstruction and Fault-Tolerant Control via Learning Observers in Takagi–Sugeno Fuzzy Descriptor Systems With Time Delays

This paper addresses the problems of observer-based fault reconstruction and fault-tolerant control for Takagi-Sugeno fuzzy descriptor systems subject to time delays and external disturbances. A novel fuzzy descriptor learning observer is constructed to achieve simultaneous reconstruction of system states and actuator faults. Sufficient conditions for the existence of the proposed observer are explicitly provided. Utilizing the reconstructed fault information, a reconfigurable fuzzy fault-tolerant controller based on the separation property is designed to compensate for the impact of actuator faults on system performance by stabilizing the closed-loop system. In addition, the design of the fault reconstruction observer and the fault-tolerant controller is formulated in terms of linear matrix inequalities that can be conveniently solved using convex optimization techniques. Finally, simulation results on a truck-trailer system are presented to verify the effectiveness of the proposed approaches.

Integrated design of fault reconstruction and fault-tolerant control against actuator faults using learning observers

ABSTRACT This paper addresses the problem of integrated fault reconstruction and fault-tolerant control in linear systems subject to actuator faults via learning observers (LOs). A reconfigurable fault-tolerant controller is designed based on the constructed LO to compensate for the influence of actuator faults by stabilising the closed-loop system. An integrated design of the proposed LO and the fault-tolerant controller is explored such that their performance can be simultaneously considered and their coupling problem can be effectively solved. In addition, such an integrated design is formulated in terms of linear matrix inequalities (LMIs) that can be conveniently solved in a unified framework using LMI optimisation technique. At last, simulation studies on a micro-satellite attitude control system are provided to verify the effectiveness of the proposed approach.

A star tracking algorithm suitable for star sensor

The sufficient attitude knowledge including location of recognized stars in star image can be obtained with Lost-in-Space case. In order to make use of the sufficient attitude knowledge, the star sensor may work in star tracking case. To achieve this, a star tracking algorithm is proposed in this paper. The previous location of recognized stars in star image may be used to obtain location of observed star in proper region of current star image with the star tracking algorithm. The simulations and real sky experiment results show that the star tracking algorithm proposed in this paper not only improves update rate of star sensor, but also avoids fault star pattern recognition. Finally, a star sensor featuring star tracking algorithm proposed in this paper was for on-orbit demonstration.

Fault reconstruction and accommodation in linear parameter-varying systems via learning unknown-input observers

This paper addresses the problem of observer-based fault reconstruction and accommodation for polytopic linear parameter-varying (LPV) systems. A polytopic representation of an LPV system subject to actuator faults and external disturbances is first established; then, a novel polytopic learning unknown-input observer (LUIO) is constructed for simultaneous state estimation and robust fault reconstruction. The stability of the presented LUIO is proved using Lyapunov stability theory together with H∞ techniques. Further, using reconstructed fault information, a reconfigurable fault-tolerant controller is designed to compensate for the influence of actuator faults by stabilizing the closed-loop system. At last, an aircraft example is employed to illustrate the effectiveness and practicability of the proposed techniques.

An autonomous star pattern recognition algorithm using bit match

A novel star pattern recognition algorithm using bit match for star sensor is presented in the paper. At first the entire celestial sphere is divided into n un-overlapped square sub-areas. The number of navigation stars in the sub-area data is saved in the first part of the sub-area data. Every bit 1 refers to a navigation star while every bit 0 not. An 18-bit binary code and an extra 0 are used to the number of successive bit 0s, and 11 are used to a bit 1. These resulted bit numbers are stored in corresponding position in the sub-area data. Let all sub-area data rearrange with the number of navigation stars in ascending order and be stored as the navigation star database. The logical AND operation is used for star pattern recognition. The simulations show that the less time for star pattern recognition and uses less memory for storing navigation star database are used with the algorithm in this paper than those with KMP (Knuth-Morris-Pratt) algorithm.

Robust Fault Reconstruction in Discrete-Time Lipschitz Nonlinear Systems via Euler-Approximate Proportional Integral Observers

The problem of observer-based robust fault reconstruction for a class of nonlinear sampled-data systems is investigated. A discrete-time Lipschitz nonlinear system is first established, and its Euler-approximate model is described; then, an Euler-approximate proportional integral observer (EPIO) is constructed such that simultaneous reconstruction of system states and actuator faults are guaranteed. The presented EPIO possesses the disturbance-decoupling ability because its architecture is similar to that of a nonlinear unknown input observer. The robust stability of the EPIO and convergence of fault-reconstructing errors are proved using Lyapunov stability theory together with techniques. The design of the EPIO is reformulated into convex optimization problem involving linear matrix inequalities (LMIs) such that its gain matrices can be conveniently calculated using standard LMI tools. In addition, to guarantee the implementation of the EPIO on the exact model, sufficient conditions of its semiglobal practical convergence are provided explicitly. Finally, a single-link flexible robot is employed to verify the effectiveness of the proposed fault-reconstructing method.

A new strategy for fault estimation in Takagi-Sugeno fuzzy systems via a fuzzy learning observer

This paper is to suggest a new strategy for fault estimation in Takagi-Sugeno (T-S) fuzzy systems. A fuzzy Learning Observer (FLO) is constructed to achieve simultaneous estimation of system states and actuator faults. The FLO is able to estimate both constant and time-varying faults accurately, and a systematic method is also proposed to select gain matrices for the FLOs. Stability and convergence of the proposed observer is proved using Lyapunov stability theory. The design of FLOs can be formulated in terms of Linear Matrix Inequalities (LMIs) that can be conveniently solved using LMI optimization technique. A single-link flexible manipulator is employed to verify the effectiveness of the proposed fault-estimating approaches.

Research on the Algorithm of On-Orbit Calibration Based on Gyro/Star-Sensor

In order to control satellites maneuver accurately from one attitude to another, onboard rate sensing gyro usually need be calibrated again after launch. A new on-orbit calibration algorithm is proposed to correct the drift errors of the gyro based on Star-Sensor in this paper. Firstly, the exact dynamic and kinematic models of the gyro are built by error analysis. Then the error model is linearized under reasonable condition, and the state equation of system is educed in detail. The information of attitude Modified Rodrigues Parameters measured by Star-Sensor in real time is used to calibrate the installation error and the calibration error based on the extended Kalman filter. The numerical simulation results show that the new method is effective practically to improve the navigation precision, longevity of gyro effectively and to provide better reliability and adaptability

Fault tolerant sliding mode control for T-S fuzzy stochastic time-delay system via a novel sliding mode observer approach

ABSTRACT In this paper, a fault estimation and fault-tolerant control problem for a class of T-S fuzzy stochastic time-delay systems with actuator and sensor faults is investigated. A novel sliding mode observer is proposed, which can simultaneously estimate the system states, actuator and sensor faults with good accuracy. Based on the state and actuator fault estimation, a new sliding mode control scheme is developed, which can effectively eliminate the influence of actuator fault. Sufficient conditions for the existence of the proposed observer and fault-tolerant sliding mode controller are provided in terms of linear matrix inequality, and moreover, the reachability of the sliding mode surface can be guaranteed under the proposed control scheme. The propose sliding mode observer and fault-tolerant sliding mode controller can overcome the restrictive assumption that the input matrix of all local modes is the same. Finally, a numerical example is provided to verify the effectiveness of the proposed sliding mode observer and fault-tolerant sliding mode control technique.

Fault reconstruction for Takagi–Sugeno fuzzy systems via learning observers

ABSTRACT This paper addresses the problem of observer-based fault reconstruction for Takagi–Sugeno fuzzy systems. Two types of fuzzy learning observers are constructed to achieve simultaneous reconstruction of system states and actuator faults. Stability and convergence of the proposed observers are proved using Lyapunov stability theory, and necessary conditions for the existence of the observers are further discussed. The design of fuzzy learning observers can be formulated in terms of a series of linear matrix inequalities that can be conveniently solved using convex optimisation technique. A single-link flexible manipulator is employed to verify the effectiveness of the proposed fault-reconstructing approaches.