Near optimality conditions in stochastic control of jump diffusion processes

This paper is concerned with necessary as well as sufficient conditions for near-optimality of controlled jump diffusion processes. Necessary conditions for a control to be near-optimal are derived, using Ekeland’s variational principle and some stability results on the state and adjoint processes, with respect to the control variable. In a second step, we show that the necessary conditions for near-optimality, are in fact sufficient for near-optimality provided some concavity conditions are fulfilled. Finally, as an illustration some examples are solved explicitly.     

Separating the Drift from Affine Control Systems via Feedbacks

This paper investigates the use of state feedbacks to transform an affine control system into a special form with a driftless property. First, we consider the regular dynamic state feedback for an affine control system. Both local and global sufficient and necessary conditions are given on the affine control system such that it admits the driftless property. An algorithm is provided to compute explicitly the desired coordinate transformation. Then, the nonregular cases are studied. Some necessary conditions, as well as sufficient conditions, for nonregular feedback are presented. We illustrate with some examples.     

Output stabilizability of discrete-event dynamic systems

The authors investigate the problem of designing stabilizing feedback compensators for discrete-event dynamic systems (DEDS) modeled as finite-state automata in which some transition events are controllable and some events are observed. The problem of output stabilization is defined as the construction of a compensator such that all state trajectories in the closed-loop system go through a given set E infinitely often. The authors also define a stronger notion of output stabilizability which requires that the state not only pass through E infinitely often but that the set of instants when the state is in E and one knows it is in E is also infinite. Necessary and sufficient conditions are presented for both notions. The authors also introduce and characterize a notion of resiliency that corresponds to the system being able to recover from observation errors. In addition, they provide some general bounds for the algorithms considered and discuss several conditions under which far smaller bounds can be achieved     

On-line sensitivity analysis of Markov chains

Discrete-event systems modeled as continuous-time Markov processes and characterized by some integer-valued parameter are considered. The problem addressed is that of estimating performance sensitivities with respect to this parameter by directly observing a single sample path of the system. The approach is based on transforming the nominal Markov chain into a reduced augmented chain, the stationary-state probabilities which can be easily combined to obtain stationary-state probability sensitivities with respect to the given parameter. Under certain conditions, the reduced augmented chain state transitions are observable with respect to the state transitions of the system itself, and no knowledge of the nominal Markov-chain state of the transition rates is required. Applications for some queueing systems are included. The approach incorporates estimation of unknown transition rates when needed and is extended to real-valued parameters     

Output regulation of linear plants subject to constraints

Output regulation problems for continuous-time linear systems with state and/or input constraints are studied. The problems are formulated in global and semi-global setting by using state or full information feedback. The goal of this paper is to develop solvability conditions for the posed problems. Moreover, appropriate regulators are constructed under the solvability conditions. To state the solvability conditions clearly, a taxonomy of constraints is introduced which delineates the constraints into several categories. Such a taxonomy of constraints provides a classification of linear plants with constraints and identifies what types of output regulation problems are solvable. Results developed here include as a special case the results obtained in the literature for systems with only input constraints. The constraint taxonomy also identifies some intrinsically hard constraints (non-right invertible constraints) for which the solvability conditions of global/semi-global output regulation problems are not clear yet. As a special case of output regulation, we also consider tracking problems with constraints. It is shown that if there exists a state feedback controller with a stabilizing domain of attraction, then one can find a regulator with a tracking domain of attraction arbitrarily close to the stabilizing domain.     

On designing observers for time-delay systems with non-linear disturbances

In this paper, the observer design problem is studied for a class of time-delay non-linear systems. The system under consideration is subject to delayed state and non-linear disturbances. The time-delay is allowed to be time-varying, and the non-linearities are assumed to satisfy global Lipschitz conditions. The problem addressed is the design of state observers such that, for the admissible time-delay as well as non-linear disturbances, the dynamics of the observation error is globally exponentially stable. An effective algebraic matrix inequality approach is developed to solve the non-linear observer design problem. Specifically, some conditions for the existence of the desired observers are derived, and an explicit expression of desired observers is given in terms of some free parameters. A simulation example is included to illustrate the practical applicability of the proposed theory.     

Dissipative control for state-saturated discrete time-varying systems with randomly occurring nonlinearities and missing measurements

In this paper, the dissipative control problem is investigated for a class of discrete time-varying systems with simultaneous presence of state saturations, randomly occurring nonlinearities as well as multiple missing measurements. In order to render more practical significance of the system model, some Bernoulli distributed white sequences with known conditional probabilities are adopted to describe the phenomena of the randomly occurring nonlinearities and the multiple missing measurements. The purpose of the addressed problem is to design a time-varying output-feedback controller such that the dissipativity performance index is guaranteed over a given finite-horizon. By introducing a free matrix with its infinity norm less than or equal to 1, the system state is bounded by a convex hull so that some sufficient conditions can be obtained in the form of recursive nonlinear matrix inequalities. A novel controller design algorithm is then developed to deal with the recursive nonlinear matrix inequalities. Furthermore, the obtained results are extended to the case when the state saturation is partial. Two numerical simulation examples are provided to demonstrate the effectiveness and applicability of the proposed controller design approach.     

一类奇异时滞系统的奇异二次指标最优控制问题

利用基本的代数等价变换，将一类奇异滞后系统的奇异二次指标最优控制问题转化为正常状态滞后系统的非奇异二次指标最优控制问题，并讨论了二的等价性，在一些常规条件下，给出了问题的解，并把最优控制综合为最优状态反馈。     

多项式非线性系统的并行分布辨识

在状态空间方程中引入输入和状态的多项式函数,以此多项式函数表示非线性因素.为了辨识多项式非线性系统中的各系统矩阵,对于矢量化各系统矩阵组成的未知参数矢量,分别在无约束和有约束条件下采用两并行分布算法求解.在以状态方程等式为约束条件时,将各状态瞬时刻值与由系统矩阵组成的未知参数矢量合并为一个新的优化矢量.对于优化矢量的辨识,给出了并行分布算法的求解过程和迭代式.最后,通过仿真算例验证了所提出方法的有效性.