The paper is concerned with the synchronization problem of a general class of multi-input multi-output (MIMO) nonlinear continuous-time systems under sampled-data output feedback control. The main contributions of the present paper are twofold: (i) we provide a unified synthesis method and synchronization criteria for MIMO Lipschitz nonlinear continuous-time systems; (ii) we present a systemati...

A brain emotional cerebellar model articulation controller (BECMAC) is developed, which is a mathematical model that approximates the judgmental and emotional activity of a brain. A fuzzy inference system is incorporated into the BECMAC, to give the novel fuzzy brain emotional cerebellar model articulation controller (FBECMAC) that is also proposed in this paper. The developed FBECMAC has the b...

The paper deals with the problem of output regulation for the class of multiinput multi-output square nonlinear systems satisfying a minimum-phase assumption and a “positivity” condition on the high-frequency gain matrix. By following a design paradigm proposed in [12] for single-input single-output nonlinear systems, it is shown how an internal model-based controller can be obtained, whose dim...

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...

The introduction of new in situ sensing creates the possibility of directly controlling critical process variables in plasma enhanced chemical vapor deposition systems (PECVD). The complexity of this process makes it necessary to develop empirical models of the system dynamics. This paper describes the experimental and numerical procedures for identifying both transfer function and recurrent ne...

The introduction of new in situ sensing creates the possibility of directly controlling critical process variables in plasma enhanced chemical vapor deposition systems (PECVD). The complexity of this process makes it necessary to develop empirical models of the system dynamics. This paper describes the experimental and numerical procedures for identifying both transfer function and recurrent ne...

Black-box modeling techniques based on artificial neural networks are opening new horizons for modeling and controlling nonlinear processes in biotechnology and chemical process industries. The link between dynamic process models and actual process control is provided by the concept of model based control (MBC), e.g. Internal Model Control (IMC) or Model Based Predictive Control (MBPC). To avoi...

in this study, we modify an iterative non-optimal without memory method, in such a way that is becomes optimal. therefore, we obtain convergence order eight with the some functional evaluations. to justify our proposed method, some numerical examples are given.

Control of a bio-reactor is a complex task due to inherent non-linearities and unavailability of measurements of the quality variables at regular sampling intervals. In this work, it proposed to identify Wiener-Hammerstein type fast-rate time series models for the quality variables directly from the irregularly sampled multi-rate input-output data. The identified models are further used to deve...

In this thesis, Some adaptive neural control design ways are presented for a class of multi-input multi-output (MIMO) nonlinear systems in block-triangular form with disturbance input and state time-varying delay. Neural networks are employed to approximate the unknown continuous functions. By combining the use of a novel quadratictype Lyapunov-Krasovskii functionals and adaptive NN backsteppin...