System identification uses system inputs and outputs to raise mathematical models. Various techniques of system identification exist that offer a nominal model and an uncertainty bound. Many practical systems such as ...System identification uses system inputs and outputs to raise mathematical models. Various techniques of system identification exist that offer a nominal model and an uncertainty bound. Many practical systems such as thermal processes ＆ chemical processes have inbuilt time delay. If the time delay used in the system model for controller design does not concur with the actual process time delay, a closed-loop system may be unstable or demonstrate unacceptable transient response characteristics so here the time delay is assumed to be time-invariant. This paper proposes on-line identification of delayed complex/uncertain systems using instrumental variable （IV） method. Parametric uncertainty has been considered which may be represented by variations of certain system parameters over some possible range. This method allows consistent estimation when the system parameters are associated with the noise terms, as the IV methods （IVM＇s） usually make no assumption on the noise correlation configuration. The faster convergence of the parameters including noise terms has been proved in this paper. Iterative prefiltering （IP） method has also been used for the identification of the delayed uncertain system and the graphical results given in this paper demonstrate that the convergence results are inferior to the instrumental variable method.展开更多
文摘System identification uses system inputs and outputs to raise mathematical models. Various techniques of system identification exist that offer a nominal model and an uncertainty bound. Many practical systems such as thermal processes ＆ chemical processes have inbuilt time delay. If the time delay used in the system model for controller design does not concur with the actual process time delay, a closed-loop system may be unstable or demonstrate unacceptable transient response characteristics so here the time delay is assumed to be time-invariant. This paper proposes on-line identification of delayed complex/uncertain systems using instrumental variable （IV） method. Parametric uncertainty has been considered which may be represented by variations of certain system parameters over some possible range. This method allows consistent estimation when the system parameters are associated with the noise terms, as the IV methods （IVM＇s） usually make no assumption on the noise correlation configuration. The faster convergence of the parameters including noise terms has been proved in this paper. Iterative prefiltering （IP） method has also been used for the identification of the delayed uncertain system and the graphical results given in this paper demonstrate that the convergence results are inferior to the instrumental variable method.
