A novel on-line observer/Kalman filter identification method and its application to input-constrained active fault-tolerant tracker design for unknown stochastic systems

2015 ◽  
Vol 352 (3) ◽  
pp. 1119-1151 ◽  
Author(s):  
C.-Y. Wu ◽  
J.S.-H. Tsai ◽  
S.-M. Guo ◽  
L.-S. Shieh ◽  
J.I. Canelon ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Active Fault ◽  
Stochastic Systems ◽  
Fault Tolerant ◽  
Identification Method ◽  
On Line

scholarly journals An Active Fault-Tolerant PWM Tracker for Unknown Nonlinear Stochastic Hybrid Systems: NARMAX Model and OKID-Based State-Space Self-Tuning Control

2010 ◽  
Vol 2010 ◽  
pp. 1-27 ◽  
Author(s):  
Chu-Tong Wang ◽  
Jason S. H. Tsai ◽  
Chia-Wei Chen ◽  
You Lin ◽  
Shu-Mei Guo ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Space ◽  
Active Fault ◽  
Stochastic Systems ◽  
Fault Tolerant ◽  
Initial Guess ◽  
Process Time ◽  
Parameter Estimates ◽  
Unknown Parameters ◽  
Self Tuning

An active fault-tolerant pulse-width-modulated tracker using the nonlinear autoregressive moving average with exogenous inputs model-based state-space self-tuning control is proposed for continuous-time multivariable nonlinear stochastic systems with unknown system parameters, plant noises, measurement noises, and inaccessible system states. Through observer/Kalman filter identification method, a good initial guess of the unknown parameters of the chosen model is obtained so as to reduce the identification process time and enhance the system performances. Besides, by modifying the conventional self-tuning control, a fault-tolerant control scheme is also developed. For the detection of fault occurrence, a quantitative criterion is exploited by comparing the innovation process errors estimated by the Kalman filter estimation algorithm. In addition, the weighting matrix resetting technique is presented by adjusting and resetting the covariance matrix of parameter estimates to improve the parameter estimation for faulty system recovery. The technique can effectively cope with partially abrupt and/or gradual system faults and/or input failures with fault detection.

scholarly journals Active Fault-Tolerant/Active Passive Intrusion-Tolerant H ∞ Cooperative Control of Discrete NCS under the Background of Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Wang Jun ◽  
Meng Xiao-li
Keyword(s):  
Kalman Filter ◽  
Cooperative Control ◽  
Active Fault ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Discrete Event ◽  
Networked Control System ◽  
Adaptive Kalman Filter ◽  
Network Attack ◽  
Intrusion Tolerant

For the linear discrete networked control system (NCS) which may suffer DoS attack on both sides of the controller, when the actuator has time-varying failure, the intelligent sensor unit uses wireless sensors to collect data. According to the large amount of data collected, the active fault tolerance/active passive capacity of linear discrete NCS under the discrete event-triggered communication mechanism (DETCS) is studied. The problem of cooperative controller design is discussed. Firstly, a linear discrete NCS model integrating DETCS, actuator fault, and network attack is established. Then, based on the idea of integral sliding mode control, an active fault-tolerant/attack active passive intrusion-tolerant cooperative controller is designed, and the actuator attack side network attack and sensor side network attack are extended to the state to obtain a new state vector. Then, an adaptive Kalman filter estimator (AKF) estimates the fault and attack information and then adjusts the initial fault-tolerant/intrusion-tolerant cooperative controller in real time according to the estimated information obtained by the adaptive Kalman filter estimator; finally, the MATLAB simulation example is used to verify the improvement of system performance by the designed control law and the saving of network resources by the introduction of DETCS.

A novel on-line OCID method and its application to input-constrained active fault-tolerant tracker design for unknown nonlinear systems

2019 ◽  
Vol 50 (14) ◽  
pp. 2632-2662 ◽  
Author(s):  
Jason Sheng-Hong Tsai ◽  
Tzu-Hsien Yu ◽  
Te Jen Su ◽  
Shu-Mei Guo ◽  
Leang-San Shieh ◽  
...  
Keyword(s):  
Nonlinear Systems ◽  
Active Fault ◽  
Fault Tolerant ◽  
On Line

Active fault tolerant control of turbofan engines with actuator faults under disturbances

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Yan-Hua Ma ◽  
Xian Du ◽  
Lin-Feng Gou ◽  
Si-Xin Wen
Keyword(s):  
Active Fault ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Feedback Controller ◽  
Actuator Faults ◽  
Error Feedback ◽  
Turbofan Engine ◽  
Turbofan Engines ◽  
Interval Observer ◽  
State Error

AbstractIn this paper, an active fault-tolerant control (FTC) scheme for turbofan engines subject to simultaneous multiplicative and additive actuator faults under disturbances is proposed. First, a state error feedback controller is designed based on interval observer as the nominal controller in order to achieve the model reference rotary speed tracking control for the fault-free turbofan engine under disturbances. Subsequently, a virtual actuator based reconfiguration block is developed aiming at preserving the consistent performance in spite of the occurrence of the simultaneous multiplicative and additive actuator faults. Moreover, to improve the performance of the FTC system, the interval observer is slightly modified without reconstruction of the state error feedback controller. And a theoretical sufficiency criterion is provided to ensure the stability of the proposed active FTC system. Simulation results on a turbofan engine indicate that the proposed active FCT scheme is effective despite of the existence of actuator faults and disturbances.

scholarly journals Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

2009 ◽  
Vol 132 (3) ◽  
Author(s):  
Donald L. Simon ◽  
Sanjay Garg
Keyword(s):  
Kalman Filter ◽  
Estimation Error ◽  
Engine Performance ◽  
Aircraft Engine ◽  
Operating Conditions ◽  
Performance Estimation ◽  
Experimental Simulation ◽  
Tuning Parameter ◽  
Estimation Accuracy ◽  
On Line

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multivariable iterative search routine that seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared with the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy.

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