This paper compares the identification of step response model coefficients based on either an autoregressive moving average (ARMA) model or a unit pulse response model. The identification of the two model types is compared for speed and accuracy in achieving the ideal step response coefficients in open-loop and closed-loop single-input, single-output systems. This comparison is useful for those wishing to construct an adaptive form of those model-based predictive controllers that use an internal step response process model
Signal modeling is concerned with the representation of signals. The modeled signal consists of par...
This paper describes seven methods to identify a mathematical model for a real process with a time d...
Abstract — While the topic has a long history in research, model structure selection is still one of...
A comparative study of methods used for identification of Linear-Time-Invariant (LTI) systems based ...
In system identification, different methods are often classified as parametric or non-parametric met...
Motivated by the fact that integrating and unstable processes are usually operated in a closed-loop ...
System identification is the experimental approach to deriving process models, which can take many f...
System Identification is used to build mathematical models of a dynamic system based on measured da...
Some methods for transient closed loop step response system identification presented in the literatu...
The paper investigates the relation between the parameters of an autoregressive moving average (ARMA...
The design and implementation of high-performance feedback controllers requires the availability of ...
Step response test is widely practiced for model identification in process industry. A frequency dom...
Nonsteady initial process states, measurement noise and unexpected load disturbance are practical di...
In practice, many systems are modeled by first and second order models including a time delay. These...
Direct identification procedures using raw data seem to face difficulties especially when the data i...
Signal modeling is concerned with the representation of signals. The modeled signal consists of par...
This paper describes seven methods to identify a mathematical model for a real process with a time d...
Abstract — While the topic has a long history in research, model structure selection is still one of...
A comparative study of methods used for identification of Linear-Time-Invariant (LTI) systems based ...
In system identification, different methods are often classified as parametric or non-parametric met...
Motivated by the fact that integrating and unstable processes are usually operated in a closed-loop ...
System identification is the experimental approach to deriving process models, which can take many f...
System Identification is used to build mathematical models of a dynamic system based on measured da...
Some methods for transient closed loop step response system identification presented in the literatu...
The paper investigates the relation between the parameters of an autoregressive moving average (ARMA...
The design and implementation of high-performance feedback controllers requires the availability of ...
Step response test is widely practiced for model identification in process industry. A frequency dom...
Nonsteady initial process states, measurement noise and unexpected load disturbance are practical di...
In practice, many systems are modeled by first and second order models including a time delay. These...
Direct identification procedures using raw data seem to face difficulties especially when the data i...
Signal modeling is concerned with the representation of signals. The modeled signal consists of par...
This paper describes seven methods to identify a mathematical model for a real process with a time d...
Abstract — While the topic has a long history in research, model structure selection is still one of...