Quality estimation of stochastic control system using simulation methods

The stochastic control system is investigated. The article solves the problem of human-robot humanoid communication. The problem arises in the development of human behavior formation management systems. They describe human behavior in terms of probabilistic characteristics. Such control systems are stochastic. The management system of human behavior formation and its functioning is described quality assessment. The problem is solved by simulation modeling. The software implementing the method is described. The results of experimental research are presented.

A Novel Method of Optimization for Stochastic Control System

Stochastic phenomena widely exist in the nature and real dynamic systems. The existence of random phenomena will make the system performance degrade greatly, and even cause instability. For the sake of improving the stability of stochastic control system, this paper proposed a novel method of optimization for stochastic control system by control model and max-plus algebraic algorithm. The simulation results indicate that the optimization method can effectively optimize the stochastic system. The input of the stochastic control system is stable to a certain extent, which weakens the random interference of the input signal in the external environment, thus improving the stability of the stochastic control system.

Maximum Principle of Discrete Stochastic Control System Driven by Both Fractional Noise and White Noise

In this paper, we investigate the necessary optimality conditions of the discrete stochastic optimal control problems driven by both fractional noise and white noise. Here, the admissible control region is not necessarily convex. The corresponding variational inequalities are obtained by applying the classical variation method and Malliavin calculus. We also apply the stochastic maximum principle to a linear-quadratic optimal control problem to illustrate the main result.

A Necessary Condition for Optimal Control of Forward-Backward Stochastic Control System with Lévy Process in Nonconvex Control Domain Case

This paper analyzes one kind of optimal control problem which is described by forward-backward stochastic differential equations with Lévy process (FBSDEL). We derive a necessary condition for the existence of the optimal control by means of spike variational technique, while the control domain is not necessarily convex. Simultaneously, we also get the maximum principle for this control system when there are some initial and terminal state constraints. Finally, a financial example is discussed to illustrate the application of our result.

AN OPTIMIZATION OF A DIGITAL CONTROLLER FOR A STOCHASTIC CONTROL SYSTEM

The article deals with the synthesis of an optimal digital controller for a stochastic plant. In order to optimize the control system, an integral quality criterion together with minimization of the controlled parameter variance that is offered here, besides the control actions the control objects are taken into account. Using the methods of optimization theory for a single-loop control system, the structure and parameters of the digital regulator are determined. The structure of the digital controller is analysed from the parameters of the quality criterion and the mathematical description of the control object. An example of the synthesis of a digital controller for an automatic control system for the air temperature at the output of the CV-P°2L°N-63B/F-N ceiling conditioner, which showed the efficiency of the digital controller when compensating for random disturbances, is given here. The obtained results are recommended to be used by the specialists on automation for the control system design as well as optimization of the existing ones.

Approximate controllability of semilinear fractional stochastic control system

The objective of this paper is to present some sufficient conditions for approximate controllability of semilinear fractional stochastic control system with delay. The results hold when the nonlinear function is Lipschitz continuous. Sufficient conditions are obtained by separating the given fractional semilinear stochastic system into two systems namely a semilinear fractional system and a fractional linear stochastic system. To prove our results, the Schauder fixed point theorem is applied. At the end, one example is given to illustrate the results.