this article deals with the issues associated with developing a new design methodology for the nonlinear model-predictive control (mpc) of a chemical plant. a combination of multiple neural networks is selected and used to model a nonlinear multi-input multi-output (mimo) process with time delays.  an optimization procedure for a neural mpc algorithm based on this model is then developed. the p...

In this paper, a novel MIMO Neural NARX model is used for simultaneously modeling and identifying both joints of the 2-axes PAM robot arm’s inverse and forward dynamic model. The highly nonlinear cross effect of both links of the 2-axes PAM robot arm are thoroughly modeled through an Inverse and Forward Neural MIMO NARX Model-based identification process using experimental input-output training...

This paper developed an artificial neural network (ANN) model with hybrid Taguchi-genetic algorithm (HTGA) for the nonlinear multiple-input multiple-output (MIMO) model of machining processes. The HTGA in the MIMO ANN model finds the optimal parameters (i.e., weights of links and biases govern the input-output relationship of an ANN) by directly minimizing root-mean-squared error (RMSE), which ...

Many real time processes have complex, uncertain and nonlinear dynamics. Boilers are nonlinear, time varying, multi-input multi-output (MIMO) systems, whose states generally vary with operating conditions. The major problem in controlling that system is that its drum water pressure and steam flow dynamics include an integrator that results a critically stable behavior. Conventional controller p...

In this paper, we propose a design method for a robust adaptive output feedback control system for uncertain MIMO nonlinear systems with a higher order relative degree and an uncertain controlled system order. By using a virtual input filter method, this method allows an adaptive output feedback control of MIMO nonlinear systems without constructing a state observer in the controller. Copyright...

In this paper, robust stability and output tracking performance of multi-input multi-output (MIMO) nonlinear feedback system are considered by using operator based robust right coprime factorization approach. Sufficient conditions for the MIMO nonlinear system to be robustly stable are derived, and tracking filters are designed to realize the desired tracking performance. Finally, a simulation ...

The combination of Multiple Input Multiple Output (MIMO) with Orthogonal Frequency Division Multiplexing (OFDM) is a promising technique for high performance with very high data rates in 4G broadband wireless communications. However, the non-linearities caused by the High Power Amplifier (HPA) degrade the performance of MIMO-OFDM systems. In this paper, we present a theoretical analysis of nonl...

Data-Driven Inversion-Based Control (D-IBC) is a recently introduced control design method for uncertain nonlinear systems, relying on a two degree-of-freedom architecture, with a nonlinear controller and a linear controller running in parallel. In this paper, extending to the MIMO case a previous result holding for the SISO case, we derive a finite-gain stability sufficient condition for a clo...

this paper deals with leader-following and leaderless consensus problems of high-order multi-input/multi-output (mimo) multi-agent systems with unknown nonlinear dynamics in the presence of uncertain external disturbances. the agents may have different dynamics and communicate together under a directed graph. a distributed adaptive method is designed for both cases. the structures of the contro...

In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, se- curing low-level and high-level ...