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...

The purpose of this work is to study machine-learning-based model predictive control nonlinear systems with time-delays. proposed approach involves initially building a machine learning (i.e., Long Short Term Memory (LSTM)) capture the process dynamics in absence time delays. Then, an LSTM-based controller (MPC) designed stabilize system without Closed-loop stability results are then presented,...

This paper develops a fault-tolerant variable sampling control (VSC) scheme for a class of nonlinear networked control systems (NCSs) with time-varying state and random network delays. An uncertain continuous Takagi–Sugeno (T–S) fuzzy system with both state and input varying delays, in the presence of possible actuator faults, is obtained equivalently on the basis of the input delay methodology...

This paper is pertained with the problem of admissibility analysis of uncertain discrete-time nonlinear singular systems by adopting the state-space Takagi-Sugeno fuzzy model with time-delays and norm-bounded parameter uncertainties. Lyapunov Krasovskii functionals are constructed to obtain delay-dependent stability condition in terms of linear matrix inequalities, which is dependent on the low...

prediction the river flow discharge values are important in the surface water resources management. find an appropriate model to accurately predictionof this parameter is one of the most important ways to simulation and prediction. in this study three anfis, svm and gp models were evaluated and compared to modeling the monthly flow discharge of nazloochi river in tapik hydrometric station that ...