Konstantin Amelin

Randomized Algorithm for UAVs Group Flight Optimization

Abstract The problem of small UAVs flight optimization is considered. To solve this problem thermal updrafts are used. For the precise detection of the thermal updrafts center the simultaneous perturbation stochastic approximation (SPSA) type algorithm is proposed. If UAVs use thermal updrafts so they can save the energy during the flight. Therefore the flight time will be vary for different UAVs. In order to optimize the area monitoring, the consensus approach has been proposed.

Approximate consensus in multi-agent stochastic systems with switched topology and noise

In this paper the approximate consensus problem in multi-agent stochastic systems with noisy information about the current state of the nodes and randomly switched topology for agents with nonlinear dynamics is considered. The control is formed by the local voting protocol with step size not tending to zero. To analyze closed loop system we propose to use method of continuous models (ODE approach or Derevitskii-Fradkov-Ljung (DFL)-scheme). The usage of this method allows one to reduce the computation load. The bounds of the mean proximity of trajectories of the discrete stochastic system to its continuous deterministic model are obtained. Based on those bounds the conditions for achieving mean square e-consensus are established.

Multi-agent Stochastic Systems with Switched Topology and Noise

In this paper the approximate consensus problem in multi-agent stochastic systems with noisy information about the current state of the nodes and randomly switched topology for agents with nonlinear dynamics is considered. The control is formed by the local voting protocol with step size not tending to zero. To analyze closed loop system we propose to use method of continuous models (ODE approach or Derevitskii-Fradkov-Ljung (DFL)-scheme). The usage of this method allows one to reduce the computation load. The bounds of the mean proximity of trajectories of the discrete stochastic system to its continuous deterministic model are obtained. Based on those bounds the conditions for achieving mean square ε-consensus are established. The method is applied to the load balancing problem in decentralized stochastic dynamic network with incomplete information about the current state of nodes and changing set of communication links is considered. The load balancing problem is reformulated as consensus problem in noisy model with switched topology. The conditions to achieve the optimal level of nodes load are obtained. The performance of the system is evaluated both analytically and by simulation. Obtained results are important for control of production networks, multiprocessor or multicomputer networks, etc.

Randomized controls for linear plants and confidence regions for parameters under external arbitrary noise

This paper considers the possibilities of randomized controls for designing confidence regions for unknown parameters. The assumptions about external noise that affect a linear plant are reduced to a minimum: external noise can virtually be arbitrary, but, independently of it, the user must be able to add test perturbations through the input channel. Based on a finite set of observations, we suggest a new procedure which can be used in adaptive control schemes. It has been developed in the general framework of “counting of leave-out sign-dominant correlation regions” (LSCR), which is mostly being promoted by M. Campi et al. for identification problems. The procedure returns confidence regions which are guaranteed to contain true parameters with a user-chosen probability. The theoretical results are illustrated by an example of a nonminimum-phase second-order plant.

Task Allocation Algorithm for the Cooperating Group of Light Autonomous Unmanned Aerial Vehicles

Abstract In this paper a task allocation problem for multi-UAV systems was considered. The possibility of applying a consensus approach to organize a distribution of tasks in an autonomous group of small Unmanned Aerial Vehicles (UAVs) is studied. The use of several small UAVs with autonomous distribution of tasks gives a significant advantage over the use of a single UAV. The well-known consensus protocol — local voting protocol is suggested to solve this problem. To provide the communication in the network multi-agent technologies are used.

Exact confidence regions for linear regression parameter under external arbitrary noise

The paper propose new method for identifying non-asymptotic confidence regions for linear regression parameter under external arbitrary noise. This method called Modified Sign-Perturbed Sums (MSPS) method and it is a modification of previously proposed one, called Sign-Perturbed Sums which is applicable only in case of symmetrical centred noise. MSPS algorithm correctness and obtained confidence region convergence are proved theoretically under some additional assumptions. SPS and MSPS methods are compared basing on simulated data. Few advantages of MSPS method in case of biased and asymmetric noise are illustrated.

Simple adaptive control of quadrotor attitude. Algorithms and experimental results

In the paper the simple adaptive control approach is employed to designing the adaptive controllers of quadrotor angular motion. The adaptive controllers are synthesized based on the Implicit Reference Model (IRM) design technique. The “shunting method” (parallel feedforward compensation) is used to cope with the unmodelled plant dynamics. Analytical justification of system stability is made by employing the Passification method. Quality of the closed-loop IRM adaptive control system is studied and compared with that of the proportionalderivative (PD) non-adaptive control system experimentally on two degrees of freedom (2DOF) quadrotor testbed and in-flight tests of the QuadRoy quadrotor for nominal and parametrically perturbed cases.

Two procedures with randomized controls for the parameters' confidence region of linear plant under external arbitrary noise

In this paper we join the previous results about asymptotic properties of a randomized control strategy with new one based on a non-asymptotic approach of LSCR (Leave-out Sign-dominant Correlation Regions) methods. Our consideration is focused on problems of the identification or adaptive optimal control for the linear plant with unknown parameters and dynamics disturbed by an external noise. The assumptions about the noise are reduced to a minimum: it can virtually be arbitrary, but, independently of it, the user must be able to add test perturbations through the input channel. The theoretical results are illustrated by simulations.

Combined procedure with randomized controls for the parameters' confidence region of linear plant under external arbitrary noise

The new algorithm is proposed for the estimating of linear plants unknown parameters in the case of observations with arbitrary external noises. It is based on adding of randomized inputs (test perturbations) through the feedback channel. The assumptions about the noise are reduced to a minimum: it can virtually be arbitrary but independently of it the user must be able to add test perturbations. We combine the previous result about asymptotic properties of randomized control strategy with the new one which is followed by a non-asymptotic approach of LSCR (Leave-out Sign-dominant Correlation Regions) method. The new algorithm gives confidence regions for series of finite sets of observations. These regions shrink to the true values of an unknown parameters when number of observations tents to infinity while the algorithm complexity does not increases.

Plagioclase twin associations from the basic volcanic rocks of the Kamchatka, Russia: growth conditions and formation mechanisms

The plagioclase twin associations found as crystal-lapilli in tephra and megacrysts in high-alumina basalt erupted from volcanoes of the Klyuchevskoy group (Kamchatka, Russia) reveal at least eight twin laws (Albite, Carlsbad, Albite-Carlsbad, Pericline, Baveno- d , - l , Prismatic- d , - l , and Periclinic twins), whose development and hierarchy can be described with the triad theory of L.A. Vardanianz. There are two genetic types of twins: (1) post-growth twins resulted from the thermal stress (Pericline twins and small twins of Albite-Carlsbad triad) and (2) growth twins (large twins of Albite-Carlsbad triad, Baveno and Prismatic twins), whose origin is explained by the action of the autodeformation mechanism. This mechanism implies deformation or stress-induced nucleation of small twin subunits in the subsurface layer of the growing crystal. The high level of stress is provided by the lattice mismatch on the zoning boundaries. Once nucleated, twin subunits grow independently of the original crystal that is favoured by the high growth-rate anisotropy.