A new approach for trajectory tracking on uncertain complex networks is proposed. To achieve this goal, a neural controller is applied to a small fraction of nodes (pinned ones). Such controller is composed of an on-line identifier based on a recurrent high-order neural network, and an inverse optimal controller to track the desired trajectory; a complete stability analysis is also included. In order to verify the applicability and good performance of the proposed control scheme, a representative example is simulated, which consists of a complex network with each node described by a chaotic Lorenz oscillator.

In this technical note, a novel approach to trajectory tracking control of a complex network is presented, based on the idea and technique of inverse optimal pinning control. A control law which stabilizes the tracking error dynamics is developed. This controller allows different coupling strengths, and simultaneously minimizing an associated cost functional. Chaotic systems are considered to illustrate the applicability of the new methodology and the proposed controller effectiveness via simulations.

This paper presents the development of a fuzzy controller for a synchronous generator by means of the exciter, fed by a voltage converter (DC/DC), controlled by pulse width modulation (PWM). The transfer function of the generator based on the data of the input voltages of the exciter and generator output voltage is determined; these data were obtained experimentally. Card data acquisition NI USB-6009* is used for communication with Matlab Simulink** and the implementation of the Fuzzy controller; the output of the controller closes the control loop voltage through port A of microcontroller PIC18F452***. The control system is based on voltage level comparison rule, which is injected into the exciter by means of a variable voltage source that depends on the load connected to the generator output.

P53 regulates the cellular response to genotoxic damage and prevents carcinogenic events. Theoretical and experimental studies state that the p53-Mdm2 network constitutes the core module of regulatory interactions activated by cellular stress induced by a variety of signaling pathways. In this paper, a strategy to control the p53-Mdm2 network regulated by p14ARF is developed, based on the pinning control technique, which consists into applying local feedback controllers to a small number of nodes (pinned ones) in the network. Pinned nodes are selected on the basis of their importance level in a topological hierarchy, their degree of connectivity within the network, and the biological role they perform. In this paper, two cases are considered. For the first case, the oscillatory pattern under gamma-radiation is recovered; afterward, as the second case, increased expression of p53 level is taken into account. For both cases, the control law is applied to p14ARF (pinned node based on a virtual leader methodology), and overexpressed Mdm2-mediated p53 degradation condition is considered as carcinogenic initial behavior. The approach in this paper uses a computational algorithm, which opens an alternative path to understand the cellular responses to stress, doing it possible to model and control the gene regulatory network dynamics in two different biological contexts. As the main result of the proposed control technique, the two mentioned desired behaviors are obtained.

A discrete-time neural sliding-mode pinning controller for synchronization of uncertain complex networks with nonidentical nodes is proposed. To achieve synchronization, a neural control scheme constituted by a Recurrent High-Order Neural Network identifier trained with an extended Kalman algorithm, and a discrete-time sliding-mode controller is used in a small fraction of nodes. The applicability of the proposed scheme is illustrated via simulations, using a network constituted by 8-nodes with four different types of chaotic systems.