In the paper, the problem of multiple controlled synchronization of a pair of unbalanced rotors is considered. To ensure the desired system behavior in the face of uncertainty and variations of the system parameters a novel adaptive control law based on the Implicit Reference Model (IRM) approach taking into account the discrete-time implementation is proposed and studied both by the computer simulations and experiments on the Multiresonance Mechatronic Laboratory Setup (MMLS) SV-2M of the IPME RAS, demonstrating the efficiency of the proposed approach and revealing its application scope.

In this paper, a method for forming the neuro-feedback (NFB) signal is suggested. The implementation of the method is based on the design of an adaptive model of subjects’ neural activity in the form of a vector autoregression (VAR) model for capturing the spatio-temporal dynamics of an electroencephalogram (EEG). The adaptive model parameter adjustment algorithm is iterative and is designed using gradient methods for solving goal inequalities. At each stage of training, the control signal is first calculated. Then, based on the control signal, the model coefficients are modified. With the improvement of the EEG recording in the sense of its proximity to the desired one, the coefficients of the EEG model are recalculated. The proposed scheme has physiological analogues and is not directly related to the EEG model used in calculating the control effect. The proposed algorithm has advantages over other known algorithms because of its adaptability to each specific subject.

The paper deals with the problem of controlling the pendulum mechanisms. To describe the dynamics of controllable systems, the Hamiltonian formalism is used. An algorithm is proposed for achieving equal energy values by means of feedback control based on the Speed-gradient method. The conditions of attainability of the control goal are obtained. A relation is established between the energy synchronization and oscillation frequency. The results of the computer simulations are presented showing achievement of the control goal and demonstrating the dynamical properties of the closed-loop system.

This paper is devoted to the approximate consensus problem for stochastic networks of nonlinear agents with switching topology, noisy, and delayed information about agent states. A local voting protocol with nonvanishing (e.g., constant) step size is examined under time-varying environments of agents. To analyze dynamics of the closed-loop system, the so-called method of averaged models is used. It allows us to reduce analysis complexity of the closed-loop stochastic system. We derive the upper bounds for mean square distance between states of the initial stochastic system and its approximate averaged model. These upper bounds are used to obtain conditions for approximate consensus achievement. An application of general theoretical results to the load balancing problem in stochastic dynamic networks with incomplete information about the current states of agents and with changing set of communication links is considered. The conditions to achieve the optimal level of load balancing are established. The performance of the system is evaluated both analytically and by simulation.