Broadcast stochastic receding horizon control of multi-agent systems

Optimal regulation of stochastically behaving agents is essential to achieve a robust aggregate behavior in a swarm of agents. How optimally these behaviors are controlled leads to the problem of designing optimal control architectures. In this paper, we propose a novel broadcast stochastic receding horizon control architecture as an optimal strategy for stabilizing a swarm of stochastically behaving agents. The goal is to design, at each time step, an optimal control law in the receding horizon control framework using collective system behavior as the only available feedback information and broadcast it to all agents to achieve the desired system behavior. Using probabilistic tools, a conditional expectation based predictive model is derived to represent the ensemble behavior of a swarm of independently behaving agents with multi-state transitions. A stochastic finite receding horizon control problem is formulated to stabilize the aggregate behavior of agents. Analytical and simulation results are presented for a two-state multi-agent system. Stability of the closed-loop system is guaranteed using the supermartingale theory. Almost sure (with probability 1) convergence of the closed-loop system to the desired target is ensured. Finally, conclusions are presented.     

Robustness of exponential stability of a class of stochastic functional differential equations with infinite delay

We regard the stochastic functional differential equation with infinite delay as the result of the effects of stochastic perturbation to the deterministic functional differential equation , where is defined by x_t(θ)=x(t+θ),θ(−∞,0]. We assume that the deterministic system with infinite delay is exponentially stable. In this paper, we shall characterize how much the stochastic perturbation can bear such that the corresponding stochastic functional differential system still remains exponentially stable.     

On the exponential stability in mean square of neutral stochastic functional differential equations

In [1 and 2], some efforts have been devoted to the investigation of exponential stability in mean square of neutral stochastic functional differential equations. However, the results derived there are either difficult to demonstrate in a straightforward way for practical situations or somewhat too restricted to be applied to general neutral stochastic functional differential equations, for instance, nonautonomous cases. In this paper, we shall establish some results which are more effective and relatively easy to verify to obtain the required stability.     

Global stabilizer of a general class of feedback nonlinear systems and its exponential convergence

1IntroductionThe problem of stabilizing feedback system has beenstudied extensively,such as[1~3],etc.For a generalfeedback nonlinear systems,there’s no workablestabilization theory when priori information on systemnonlinearities is known.However,inthis paper,we proposean universal global stabilizer for a general class of feedbacksystems,which does not require any priori knowledge ofsystemnonlinearities.We consider a general class of feedback nonlinearsystems:dx1dt=f1(u),dx2dt=f2(x1,u),┇dxn-1…     

Stabilization and destabilization of hybrid systems of stochastic differential equations

This paper aims to determine whether or not a stochastic feedback control can stabilize or destabilize a given nonlinear hybrid system. New methods are developed and sufficient conditions on the stability and instability for hybrid stochastic differential equations are provided. These results are then used to examine stochastic stabilization and destabilization.     

Composition schemes for the stochastic differential equation describing collisional pitch-angle diffusion

Two new second order accurate Monte Carlo integration schemes are derived for the stochastic differential equation describing pitch-angle scattering by Coulomb collisions in magnetized plasmas. Here the pitch-angle is the angle between the magnetic field and the particle velocity vectors. Mathematically this collision process corresponds to diffusion in the polar angle of a spherical coordinate system. The schemes are simple to implement, they are naturally bounded to the solution domain and their convergences are shown to compare favourably against commonly used alternative integration schemes.     

Delay-dependent exponential stability of the backward Euler method for nonlinear stochastic delay differential equations

Recently, several scholars discussed the question of under what conditions numerical solutions can reproduce exponential stability of exact solutions to stochastic delay differential equations, and some delay-independent stability criteria were obtained. This paper is concerned with delay-dependent stability of numerical solutions. Under a delay-dependent condition for the stability of the exact solution, it is proved that the backward Euler method is mean-square exponentially stable for all positive stepsizes. Numerical experiments are given to confirm the theoretical results.     

Infinite horizon linear quadratic stochastic Nash differential games of Markov jump linear systems with its application

In this paper, we deal with the problem of stochastic Nash differential games of Markov jump linear systems governed by Itô-type equation. Combining the stochastic stabilizability with the stochastic systems, a necessary and sufficient condition for the existence of the Nash strategy is presented by means of a set of cross-coupled stochastic algebraic Riccati equations. Moreover, the stochastic H₂/H_∞ control for stochastic Markov jump linear systems is discussed as an immediate application and an illustrative example is presented.     

Stability of stochastic interval systems with time delays

Consider a given exponentially stable system undergoing a random perturbation which is dependent on a past state of the solution of the system. Suppose this stochastically perturbed system is described by a stochastic differential-functional equation. In this paper, we establish a sufficient condition that the perturbed system remains exponentially stable. Using a specific example, we show how this condition may be used, and we extend it to deal with multiple time delays.     

Infinite horizon optimal control of linear discrete time systems with stochastic parameters

The infinite horizon optimal control problem is considered in the general case of linear discrete time systems and quadratic criteria, both with stochastic parameters which are independent with respect to time. A stronger stabilizability property and a weaker observability property than usual for deterministic systems are introduced. It is shown that the infinite horizon problem has a solution if the system has the first property. If in addition the problem has the second property the solution is unique and the control system is stable in the mean square sense. A simple necessary and sufficient condition, explicit in the system matrices, is given for the system to have the stronger stabilizability property. This condition also holds for deterministic systems to be stabilizable in the usual sense. The stronger stabilizability and weaker observability properties coincide with the usual ones if the parameters are deterministic.     

Second-order Taylor expansion for backward doubly stochastic control system

We consider the second-order Taylor expansion for backward doubly stochastic control system. The results are obtained under no restriction on the convexity of control domain. Moreover, the control variable is allowed in the drift coefficient and the diffusion coefficient.     

Almost sure convergence of iterative learning control for stochastic systems

This paper proposes an iterative learning control (ILC) algorithm with the purpose of controling the output of a linear stochastic system presented in state space form to track a desired realizable trajectory. It is proved that the algorithm converges to the optimal one a.s. under the condition that the product input-output coupling matrices are full-column rank in addition to some assumptions on noises. No other knowledge about system matrices and covariance matrices is required.     

Almost sure exponential stabilisation of stochastic systems by state-feedback control

So far, a major part of the literature on the stabilisation issues of stochastic systems has been dedicated to mean-square stability. This paper develops a new class of criteria for designing a controller to stabilise a stochastic system almost surely which is unable to be stabilised in mean-square sense. The results are expressed in terms of linear matrix inequalities (LMIs) which are easy to be checked in practice by using MATLAB Toolbox. Moreover, the control structure in this paper appears not only in the drift part but also in the diffusion part of the underlying stochastic system.     

Strong convergence of split-step theta methods for non-autonomous stochastic differential equations

In this paper, we first prove the strong convergence of the split-step theta methods for non-autonomous stochastic differential equations under a linear growth condition on the diffusion coefficient and a one-sided Lipschitz condition on the drift coefficient. Then, if the drift coefficient satisfies a polynomial growth condition, we further get the rate of convergence. Finally, the obtained results are supported by numerical experiments.     

Maximum principle for the stochastic optimal control problem with delay and application

In this paper, we consider an optimal control problem for the stochastic system described by stochastic differential equations with delay. We obtain the maximum principle for the optimal control of this problem by virtue of the duality method and the anticipated backward stochastic differential equations. Our results can be applied to a production and consumption choice problem. The explicit optimal consumption rate is obtained.     

Detectability and observability of discrete-time stochastic systems and their applications

This paper studies detectability and observability of discrete-time stochastic linear systems. Based on the standard notions of detectability and observability for time-varying linear systems, corresponding definitions for discrete-time stochastic systems are proposed which unify some recently reported detectability and exact observability concepts for stochastic linear systems. The notion of observability leads to the stochastic version of the well-known rank criterion for observability of deterministic linear systems. By using these two concepts, the discrete-time stochastic Lyapunov equation and Riccati equations are studied. The results not only extend some of the existing results on these two types of equation but also indicate that the notions of detectability and observability studied in this paper take analogous functions as the usual concepts of detectability and observability in deterministic linear systems. It is expected that the results presented may play important roles in many design problems in stochastic linear systems.     

Hybrid singular systems of differential equations

This work develops hybrid models for large-scale singular differential system and analyzes their asymptotic properties. To take into consideration the discrete shifts in regime across which the behavior of the corresponding dynamic systems is markedly different, our goals are to develop hybrid systems in which continuous dynamics are intertwined with discrete events under random-jump disturbances and to reduce complexity of large-scale singular systems via singularly perturbed Markov chains. To reduce the complexity of large-scale hybrid singular systems, two-time scale is used in the formulation. Under general assumptions, limit behavior of the underlying system is examined. Using weak convergence methods, it is shown that the systems can be approximated by limit systems in which the coefficients are averaged out with respect to the quasi-stationary distributions. Since the limit systems have fewer states, the complexity is much reduced.     

Convergence and stability of the balanced methods for stochastic differential equations with jumps

This paper deals with the balanced methods which are implicit methods for stochastic differential equations with Poisson-driven jumps. It is shown that the balanced methods give a strong convergence rate of at least 1/2 and can preserve the linear mean-square stability with the sufficiently small stepsize. Weak variants are also considered and their mean-square stability analysed. Some numerical experiments are given to demonstrate the conclusions.