Recursive Immuno-Inspired Algorithm for Time Variant Discrete Multivariable Dynamic System State Space Identification

2015 ◽  
Vol 5 (2) ◽  
pp. 69-100 ◽  
Author(s):  
Mateus Giesbrecht ◽  
Celso Pascoli Bottura
Keyword(s):  
Dynamic System ◽  
State Space ◽  
System Modeling ◽  
State Space Model ◽  
Small Time ◽  
Time Interval ◽  
System State ◽  
Space Model ◽  
Starting Point ◽  
Benchmark System

In this paper a recursive immuno inspired algorithm is proposed to identify time variant discrete multivariable dynamic systems. The main contribution of this paper has as starting point the idea that a multivariable dynamic system state space model can be seen as a point in a space defined by all possible matrices quadruples that define a state space model. With this in mind, the time variant discrete multivariable dynamic system modeling is transformed in an optimization problem and this problem is solved with an immuno inspired algorithm. To do that the inputs given to the system and the resulting outputs are divided in small sets containing data from small time intervals. These sets are defined as time windows, and for each window an immuno inspired optimization algorithm is applied to find the state space model that better represents the system at that time interval. The initial candidate solutions of each time interval are the ones of the last interval. The immuno inspired algorithm proposed in this paper has some modifications to the original Opt-AINet algorithm to deal with the constraints that are natural from the system identification problem and these modifications are also contributions of this paper. The method proposed in this paper was applied to identify a time variant benchmark system, resulting in a time variant model. The outputs estimated with this model are closer to the benchmark system outputs than the outputs estimated with models obtained by other known identification methods. The Markov parameters of the variant benchmark system are also reproduced by the time variant model found with the new method.

The Design of Space Scanning Mirror Control System Based on Optimal Tracking Controller

2012 ◽  
Vol 546-547 ◽  
pp. 790-794
Author(s):  
Wen Bo Sui ◽  
Ke Fei Song ◽  
Pei Jie Zhang
Keyword(s):  
Control System ◽  
State Space ◽  
Pid Controller ◽  
Simulation Experiment ◽  
State Space Model ◽  
System State ◽  
Scanning Mirror ◽  
Optimal Tracking ◽  
Space Model ◽  
Tracking Controller

Control system of space scanning mirror has high requirement of scanning accuracy. The use of optimal tracking controller, instead of traditional PID controller, can effectively improve the scanning accuracy of space scanning mirror control system. State space model of the control system is established; the control system based on optimal tracking controller is designed; simulation experiment of the control system based on optimal tracking controller is carried out. The simulation result, in comparison with the system based on a PID controller, shows that the scanning mirror control system using optimal tracking controller instead of PID controller has higher scanning accuracy and faster response.

Output Prediction Method Based on Model and its Application in Power Systems

2011 ◽  
Vol 219-220 ◽  
pp. 986-989
Author(s):  
Ke Luo ◽  
Hong Li Lv
Keyword(s):  
Power Systems ◽  
State Space ◽  
Prediction Error ◽  
State Space Model ◽  
Prediction Method ◽  
Open Loop ◽  
System Model ◽  
System State ◽  
Space Model ◽  
Wide Area Damping Controller

An output prediction method based on state space model is proposed to overcome the poor reliability of the prediction method that does not use system model. The open-loop state observer is established based on the system state space model. Then the output prediction model is built in which the prediction error is used as input and a correction module is also contained. The module is used to correct the prediction error, and then the current predicted output can be obtained by the model from the delayed output. At last, a wide-area damping controller in power systems based on output prediction is designed to verify the effectiveness of the method.

scholarly journals Dynamic system-optimal traffic assignment using a state space model

1993 ◽  
Vol 27 (6) ◽  
pp. 451-472 ◽  
Author(s):  
Stéphane Lafortune ◽  
Raja Sengupta ◽  
David E. Kaufman ◽  
Robert L. Smith
Keyword(s):  
Dynamic System ◽  
State Space ◽  
Traffic Assignment ◽  
State Space Model ◽  
Space Model ◽  
System Optimal

State-Space Model and Kalman Filter Gain Identification by a Kalman Filter of a Kalman Filter

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Minh Q. Phan ◽  
Francesco Vicario ◽  
Richard W. Longman ◽  
Raimondo Betti
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
State Space ◽  
Kalman Filters ◽  
State Space Model ◽  
Model Structure ◽  
System State ◽  
Input Output ◽  
Output Data ◽  
Space Model

This paper describes an algorithm that identifies a state-space model and an associated steady-state Kalman filter gain from noise-corrupted input–output data. The model structure involves two Kalman filters where a second Kalman filter accounts for the error in the estimated residual of the first Kalman filter. Both Kalman filter gains and the system state-space model are identified simultaneously. Knowledge of the noise covariances is not required.

Development of L1-norm sliding mode observer for sensor fault diagnosis of an industrial gas turbine

2021 ◽  
pp. 095965182199617
Author(s):  
Mahyar Akbari ◽  
Abdol Majid Khoshnood ◽  
Saied Irani
Keyword(s):  
Fault Detection ◽  
State Space ◽  
Gas Turbine ◽  
Sliding Mode ◽  
State Space Model ◽  
Sensor Fault ◽  
Space Model ◽  
Decision Logic ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

In this article, a novel approach for model-based sensor fault detection and estimation of gas turbine is presented. The proposed method includes driving a state-space model of gas turbine, designing a novel L1-norm Lyapunov-based observer, and a decision logic which is based on bank of observers. The novel observer is designed using multiple Lyapunov functions based on L1-norm, reducing the estimation noise while increasing the accuracy. The L1-norm observer is similar to sliding mode observer in switching time. The proposed observer also acts as a low-pass filter, subsequently reducing estimation chattering. Since a bank of observers is required in model-based sensor fault detection, a bank of L1-norm observers is designed in this article. Corresponding to the use of the bank of observers, a two-step fault detection decision logic is developed. Furthermore, the proposed state-space model is a hybrid data-driven model which is divided into two models for steady-state and transient conditions, according to the nature of the gas turbine. The model is developed by applying a subspace algorithm to the real field data of SGT-600 (an industrial gas turbine). The proposed model was validated by applying to two other similar gas turbines with different ambient and operational conditions. The results of the proposed approach implementation demonstrate precise gas turbine sensor fault detection and estimation.

Modeling of CCLP in koryo extract production

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ji Chol ◽  
Ri Jun Il
Keyword(s):  
Process Control ◽  
Medical Care ◽  
State Space ◽  
State Space Model ◽  
The State ◽  
Space Model ◽  
Effective Components ◽  
Counter Current

Abstract The modeling of counter-current leaching plant (CCLP) in Koryo Extract Production is presented in this paper. Koryo medicine is a natural physic to be used for a diet and the medical care. The counter-current leaching method is mainly used for producing Koryo medicine. The purpose of the modeling in the previous works is to indicate the concentration distributions, and not to describe the model for the process control. In literature, there are no nearly the papers for modeling CCLP and especially not the presence of papers that have described the issue for extracting the effective components from the Koryo medicinal materials. First, this paper presents that CCLP can be shown like the equivalent process consisting of two tanks, where there is a shaking apparatus, respectively. It allows leachate to flow between two tanks. Then, this paper presents the principle model for CCLP and the state space model on based it. The accuracy of the model has been verified from experiments made at CCLP in the Koryo Extract Production at the Gang Gyi Koryo Manufacture Factory.

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