Adaptive neural dynamic surface control with fixed-time prescribed performance for uncertain nonstrict-feedback stochastic switched systems

An adaptive neural dynamic surface control (DSC) problem with fixed-time prescribed performance (FTPP) is investigated for a class of nonstrict-feedback stochastic switched systems. Differently from the existing works for FTPP problem, the stochastic switched systems with nonstrict-feedback form and completely unknown systems are considered in this paper, and the unknown functions are approximated by some radial basis function (RBF) neural networks (NNs). The desired adaptive neural controller is designed by using common Lyapunov function method and defining fixed-time prescribed performance function (PPF). And based on the adaptive DSC scheme with the nonlinear filter, the “explosion of complexity” problem is avoided. Besides, the constructed fixed-time PPF just need to meet the requirement of second derivative exists. According to the Lyapunov stability theory, the FTPP of output tracking error is achieved, and all signals of closed-loop system remain bounded in probability. Finally, simulation results are presented to verify the availability of the designed control strategy.     

Linear–quadratic stochastic two-person nonzero-sum differential games: Open-loop and closed-loop Nash equilibria

In this paper, we consider a linear–quadratic stochastic two-person nonzero-sum differential game. Open-loop and closed-loop Nash equilibria are introduced. The existence of the former is characterized by the solvability of a system of forward–backward stochastic differential equations, and that of the latter is characterized by the solvability of a system of coupled symmetric Riccati differential equations. Sometimes, open-loop Nash equilibria admit a closed-loop representation, via the solution to a system of non-symmetric Riccati equations, which could be different from the outcome of the closed-loop Nash equilibria in general. However, it is found that for the case of zero-sum differential games, the Riccati equation system for the closed-loop representation of an open-loop saddle point coincides with that for the closed-loop saddle point, which leads to the conclusion that the closed-loop representation of an open-loop saddle point is the outcome of the corresponding closed-loop saddle point as long as both exist. In particular, for linear–quadratic optimal control problem, the closed-loop representation of an open-loop optimal control coincides with the outcome of the corresponding closed-loop optimal strategy, provided both exist.     

On the solution of the facility location problem when the dominance method fails

If the underlying distribution in a stochastic symmetric minimax location problem possesses a dominating point then the symmetry center is known to be an optimal location. It is shown in this paper that the symmetry center is a solution also in those cases where no dominating point exists. Furthermore neither independence of the stochastic terms occuring in the problem nor identical distribution is required.     

Memory feedback controller design for stochastic Markov jump distributed delay systems with input saturation and partially known transition rates

This paper considers the problem of stabilization for a class of stochastic Markov jump distributed delay systems with partially known transition rates subject to saturating actuators. By employing local sector conditions and an appropriate Lyapunov function, a state memory feedback controller is designed to guarantee that the resulted closed-loop constrained systems are mean-square stochastic asymptotically stable. Some sufficient conditions for the solution to this problem are derived in terms of linear matrix inequalities. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method.     

Locally Optimal Risk-Sensitive Controllers for Strict-Feedback Nonlinear Systems1,2

For a class of risk-sensitive nonlinear stochastic control problems with dynamics in strict-feedback form, we obtain through a constructive derivation state-feedback controllers which (i) are locally optimal, (ii) are globally inverse optimal, and (iii) lead to closed-loop system trajectories that are bounded in probability. The first feature implies that a linearized version of these controllers solve a linear exponential-quadratic Gaussian (LEQG) problem, and the second feature says that there exists an appropriate cost function according to which these controllers are optimal.     

Global Attractors for Multivalued Random Dynamical Systems Generated by Random Differential Inclusions with Multiplicative Noise

In this paper we consider a stochastic differential inclusion with multiplicative noise. It is shown that it generates a multivalued random dynamical system for which there also exists a global random attractor.     

N-person differential games governed by semilinear stochastic evolution systems

In this paper the problem ofN-person infinite-dimensional stochastic differential games governed by semilinear stochastic evolution control systems is discussed. First the minimax principle which is the necessary condition for the existence of open-loop Nash equilibrium is proved. Then the necessary and sufficient conditions of open-loop and closed-loop Nash equilibrium for linear quadratic infinite-dimensional stochastic differential games are derived.     

交通网络下的多厂商两阶段随机非合作博弈问题——基于随机变分不等式

研究集生产、运输和销售为一体的多个制造商在随机市场环境下的两阶段随机非合作博弈问题.首先,建立了该两阶段随机非合作博弈问题的模型,然后将其转化为两阶段随机变分不等式(Stochastic Variational Inequality,简称SVI).在温和的假设条件下,证明了该问题存在均衡解,并通过Progressive Hedging Method(简称PHM)进行求解.最后,通过改变模型中随机变量的分布和成本参数,分析与研究厂商的市场行为.     

一类二维Markov跳跃非线性时滞系统的镇定控制

研究一类二维Markov跳跃非线性时滞系统的镇定控制问题.给出了Markov跳跃非线性时滞系统解的存在唯一性的一个充分条件,以及系统依概率全局渐近稳定的判别准则.通过构造适当形式的Lyapunov函数,采用积分反推方法给出了一类二维Markov跳跃非线性时滞系统的无记忆状态反馈控制器.证明了在该控制律的作用下,闭环系统平衡点依概率全局渐近稳定.     

Guaranteed Cost Control for Uncertain Neutral Stochastic Systems via Dynamic Output Feedback Controllers

This paper deals with the problem of guaranteed cost control for uncertain neutral stochastic systems. The parameter uncertainties are assumed to be time-varying but norm-bounded. Dynamic output feedback controllers are designed such that, for all admissible uncertainties, the resulting closed-loop system is mean-square asymptotically stable and an upper bound on the closed-loop value of the cost function is guaranteed. By employing a linear matrix inequality (LMI) approach, a sufficient condition for the solvability of the underlying problem is obtained. A numerical example is provided to demonstrate the potential of the proposed techniques.     

不确定离散模糊随机系统的鲁棒方差约束输出反馈控制

对一类具有范数有界不确定性的离散T-S模糊随机系统。研究不仅使整个闭环模糊系统全局渐近稳定。而且每个模糊子系统的稳态状态方差满足给定上界性能指标约束的输出反馈鲁棒方差控制律的设计问题。利用线性矩阵不等式(LMI)技术，导出输出反馈鲁棒方差控制律的存在条件，并基于矩阵相似变换给出其可解性条件，同时用一组线性矩阵不等式的可行解。给出输出反馈鲁棒方差控制律的一个参数化表达形式。     

A note on clemhout and Wan's dynamic games of common property resources

Recently, Clemhout and Wan (Ref. 1) provided solutions to a class of stochastic differential game models concerning common property resources. However, they only established closed-loop strategies for the infinite-horizon problem using the Kushner test. In this paper, we provide closed-loop feedback solutions to their game in a finite-time horizon with deterministic evolution dynamics.The helpful comments of an anonymous referee are gratefully acknowledged.     

Control with Partial Observations and an Explicit Solution of Mortensen’s Equation

We formulate a stochastic control problem with a general information structure, and show that an optimal law exists and is characterized as the unique solution of a recursive stochastic equation. For a special information structure of the signal-plus-noise type and with quadratic cost-functions, this recursive equation is solved for the value function of the control problem. This value function is then shown to satisfy the Mortensen equation of Dynamic Programming in function-space.     

Generalized stochastic processes in algebras of generalized functions

Stochastic processes with paths in a generalized function algebra are defined and it is shown that there exists an embedding of generalized functional stochastic processes into such ones. Gaussian stochastic processes with paths in an algebra of generalized functions are characterized by their first and second moments and an application to stochastic differential equations is given.     

Finite-time stability and stabilization of nonlinear stochastic hybrid systems

This paper deals with the problem of finite-time stability and stabilization of nonlinear Markovian switching stochastic systems which exist impulses at the switching instants. Using multiple Lyapunov function theory, a sufficient condition is established for finite-time stability of the underlying systems. Furthermore, based on the state partition of continuous parts of systems, a feedback controller is designed such that the corresponding impulsive stochastic closed-loop systems are finite-time stochastically stable. A numerical example is presented to illustrate the effectiveness of the proposed method.     

Deterministic equivalent for a continuous-time linear-convex stochastic control problem

We consider a finite-horizon control model with additive input. There are two convex functions which describe the running cost and the terminal cost within the system. The cost of input is proportional to the input and can take both positive and negative values. It is shown that there exists a deterministic control problem whose optimal cost is the same as the one in the stochastic control problem. The optimal policy for the stochastic problem consists of keeping the process as close to the optimal deterministic trajectory as possible.This research is supported by NSERC Grant A4619, MRCO, NSF Grant DMS-86-01510, and AFOSR Grant 87-0278.     

随机需求下考虑零售商竞争的闭环供应链定价与协调

在随机需求环境下考虑两零售商间存在竞争的闭环供应链定价与协调问题, 其中两个零售商各自面临不同的随机市场需求,并且各自的回收量都受到对方回收价格的影响。在集中决策和分散决策两种情形下分析了制造商和两零售商的定价决策问题,并运用收入费用共享契约实现了闭环供应链的协调。     

随机线性系统保成本控制的线性矩阵不等式方法

考虑具有二次成本函数的随机线性系统,研究了状态反馈控制的保证成本控制问题.依据线性矩阵不等式得到了保证成本控制器存在的充分条件,最后得到了随机线性闭环系统保证成本最小的最优保证成本控制律的表达式.     

Optimal Guaranteed Cost Control of Stochastic Discrete-Time Systems with States and Input Dependent Noise Under Markovian Switching

A problem of robust guaranteed cost control of stochastic discrete-time systems with parametric uncertainties under Markovian switching is considered. The control is simultaneously applied to both the random and the deterministic components of the system. The noise (the random) term depends on both the states and the control input. The jump Markovian switching is modeled by a discrete-time Markov chain and the noise or stochastic environmental disturbance is modeled by a sequence of identically independently normally distributed random variables. Using linear matrix inequalities (LMIs) approach, the robust quadratic stochastic stability is obtained. The proposed control law for this quadratic stochastic stabilization result depended on the mode of the system. This control law is developed such that the closed-loop system with a cost function has an upper bound under all admissible parameter uncertainties. The upper bound for the cost function is obtained as a minimization problem. Two numerical examples are given to demonstrate the potential of the proposed techniques and obtained results.