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

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.

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Active fault tolerant control using state-space self-tuning control approach

International Journal of Systems Science ◽

10.1080/00207720600879625 ◽

2006 ◽

Vol 37 (11) ◽

pp. 785-797 ◽

Cited By ~ 11

Author(s):

J. Sh.-H. Tsai ◽

Y.-Y. Lee ◽

P. Cofie ◽

L.-S. Shieh ◽

X. M. Chen

Keyword(s):

State Space ◽

Active Fault ◽

Fault Tolerant ◽

Fault Tolerant Control ◽

Control Approach ◽

Self Tuning ◽

Active Fault Tolerant Control

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

Journal of the Franklin Institute ◽

10.1016/j.jfranklin.2014.12.004 ◽

2015 ◽

Vol 352 (3) ◽

pp. 1119-1151 ◽

Cited By ~ 19

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

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Measurement Feedback Self-Tuning Weighted Measurement Fusion Kalman Filter for Systems with Correlated Noises

Journal of Applied Mathematics ◽

10.1155/2012/324296 ◽

2012 ◽

Vol 2012 ◽

pp. 1-16 ◽

Cited By ~ 4

Author(s):

Xin Wang ◽

Shu-Li Sun

Keyword(s):

Kalman Filter ◽

Stochastic Systems ◽

Tracking System ◽

Weighted Least Squares ◽

Full Rank ◽

Global Optimality ◽

Measurement Feedback ◽

Innovation Model ◽

Self Tuning ◽

Noise Statistics

For the linear discrete stochastic systems with multiple sensors and unknown noise statistics, an online estimators of the noise variances and cross-covariances are designed by using measurement feedback, full-rank decomposition, and weighted least squares theory. Further, a self-tuning weighted measurement fusion Kalman filter is presented. The Fadeeva formula is used to establish ARMA innovation model with unknown noise statistics. The sampling correlated function of the stationary and reversible ARMA innovation model is used to identify the noise statistics. It is proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion Kalman filter, which means its asymptotic global optimality. The simulation result of radar-tracking system shows the effectiveness of the presented algorithm.

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STATE-SPACE SELF-TUNING CONTROL FOR NONLINEAR STOCHASTIC AND CHAOTIC HYBRID SYSTEMS

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127401002559 ◽

2001 ◽

Vol 11 (04) ◽

pp. 1079-1113 ◽

Cited By ~ 22

Author(s):

SHU-MEI GUO ◽

LEANG-SAN SHIEH ◽

CHING-FANG LIN ◽

JAGDISH CHANDRA

Keyword(s):

State Space ◽

Hybrid Systems ◽

Continuous Time ◽

Digital Control ◽

Stochastic Systems ◽

Moving Average ◽

Computation Time ◽

Sampling Period ◽

Noise Model ◽

Self Tuning

This paper presents a new state-space self-tuning control scheme for adaptive digital control of continuous-time multivariable nonlinear stochastic and chaotic systems, which have unknown system parameters, system and measurement noises, and inaccessible system states. Instead of using the moving average (MA)-based noise model commonly used for adaptive digital control of linear discrete-time stochastic systems in the literature, an adjustable auto-regressive moving average (ARMA)-based noise model with estimated states is constructed for state-space self-tuning control of nonlinear continuous-time stochastic systems. By taking advantage of a digital redesign methodology, which converts a predesigned high-gain analog tracker/observer into a practically implementable low-gain digital tracker/observer, and by taking the non-negligible computation time delay and a relatively longer sampling period into consideration, a digitally redesigned predictive tracker/observer has been newly developed in this paper for adaptive chaotic orbit tracking. The proposed method enables the development of a digitally implementable advanced control algorithm for nonlinear stochastic and chaotic hybrid systems.

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

Wireless Communications and Mobile Computing ◽

10.1155/2021/9258411 ◽

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.

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Integration of genetic algorithm tuned adaptive fading memory Kalman filter with model predictive controller for active fault-tolerant control of cement kiln under sensor faults with inaccurate noise covariance

Mathematics and Computers in Simulation ◽

10.1016/j.matcom.2021.07.023 ◽

2021 ◽

Author(s):

Gomathi Veerasamy ◽

Ramkumar Kannan ◽

RakeshKumar Siddharthan ◽

Guruprasath Muralidharan ◽

Venkatesh Sivanandam ◽

...

Keyword(s):

Genetic Algorithm ◽

Kalman Filter ◽

Active Fault ◽

Fault Tolerant ◽

Cement Kiln ◽

Fading Memory ◽

Sensor Faults ◽

Model Predictive Controller ◽

Noise Covariance ◽

Predictive Controller

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JOINT ESTIMATION OF STATES AND PARAMETERS OF LINEAR SYSTEMS WITH PARAMETER FAULTS UNDER NON-GAUSSIAN NOISES

Facta Universitatis Series Automatic Control and Robotics ◽

10.22190/fuacr1902113s ◽

2020 ◽

Vol 18 (2) ◽

pp. 113

Author(s):

Vladimir Stojanović ◽

Dragan Pršić ◽

Ljubiša Dubonjić

Keyword(s):

State Space ◽

Stochastic Systems ◽

Fault Tolerant ◽

Linear Time ◽

Practical Importance ◽

Joint Estimation ◽

State Space Models ◽

Time Varying ◽

Linear State ◽

Non Gaussian

Joint estimation of states and time-varying parameters of linear state space models is of practical importance for the fault diagnosis and fault tolerant control. Previous works on this topic consider the joint estimation in the Gaussian noise environment, but not in the presence of outliers. The known fact is that the measurements have inconsistent observations with the largest part of the observation population (outliers). They can significantly make worse the properties of linearly recursive algorithms which are designed to work in the presence of Gaussian noises. This paper proposes the strategy of the joint parameter-state robust estimation of linear state space models in the presence of non-Gaussian noises. The case of parameter-dependent matrices is considered. Because of its good features in robust filtering, the extended Masreliez-Martin filter represents a cornerstone for realization of the robust algorithms for joint state-parameter estimation of linear time-varying stochastic systems in the presence of non-Gaussian noises. The good features of the proposed robust algorithm for joint estimation of linear time-varying stochastic systems are illustrated by intensive simulations.

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