this paper is concerned with the consensus tracking problem of high order mimo nonlinear multi-agent systems. the agents must follow a leader node in presence of unknown dynamics and uncertain external disturbances. the communication network topology of agents is assumed to be a fixed undirected graph. a distributed adaptive control method is proposed to solve the consensus problem utilizing re...

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...

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...

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...

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 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 ...

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...

one of the most important challenges in nonlinear, multi-input multi-output (mimo) and time variant systems (e.g., robot manipulator) is designing a controller with acceptable performance. this paper focused on design a new artificial non linear controller with on line tunable gain applied in the robot manipulator. the sliding mode fuzzy controller (smfc) was designed as 7 rules mamdani’s infer...

In this paper, operator based robust nonlinear control system design for multi-input multi-output nonlinear feedback control systems is proposed, that is, robust stability of the MIMO systems is studied by using operator based robust right coprime factorization approach. Some sufficient conditions for the MIMO nonlinear systems to be robust stable are derived. As a result, robust nonlinear cont...

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. T...