Neural network identification in nonlinear model predictive control for frequent and infrequent operating points using nonlinearity measure.

In this paper, the problem of optimal system identification in nonlinear model predictive control (NMPC) for highly nonlinear dynamic processes is presented. Due to the short term changes in the operating point, the process may escape from its frequent operating points (FOP) to some infrequent operating points (IOP) for a short period. On the other hand, because the nonlinear model is identified using the operating data, it is mainly accurate for the FOP.

Multi-linear model set design based on the nonlinearity measure and H-gap metric.

This paper proposes a model bank selection method for a large class of nonlinear systems with wide operating ranges. In particular, nonlinearity measure and H-gap metric are used to provide an effective algorithm to design a model bank for the system. Then, the proposed model bank is accompanied with model predictive controllers to design a high performance advanced process controller.

Robust Estimation of ARX Models With Time Varying Time Delays Using Variational Bayesian Approach.

This paper is concerned with robust identification of processes with time-varying time delays. In reality, the delay values do not simply change randomly, but there is a correlation between consecutive delays. In this paper, the correlation of time delay is modeled by the transition probability of a Markov chain.

Nonmonotonic observer-based fuzzy controller designs for discrete time T-S fuzzy systems via LMI.

In this paper, based on the nonmonotonic Lyapunov functions, a new less conservative state feedback controller synthesis method is proposed for a class of discrete time nonlinear systems represented by Takagi-Sugeno (T-S) fuzzy systems. Parallel distributed compensation (PDC) state feedback is employed as the controller structure. Also, a T-S fuzzy observer is designed in a manner similar to state feedback controller design.

Flexible structure multiple modeling using irregular self-organizing maps neural network.

The MMSOM identification method, which had been presented by the authors, is improved to the multiple modeling by the irregular self-organizing map (MMISOM) using the irregular SOM (ISOM). Inputs to the neural networks are parameters of the instantaneous model computed adaptively at every instant. The neural network learns these models.

Flexible structure multiple modeling using irregular self-organizing maps neural network.

The MMSOM identification method, which had been presented by the authors, is improved to the multiple modeling by the irregular self-organizing map (MMISOM) using the irregular SOM (ISOM). Inputs to the neural networks are parameters of the instantaneous model computed adaptively at every instant. The neural network learns these models.