基于牛顿插值算法的模糊控制器

提出一种基于牛顿在线插值算法的模糊控制器,介绍该控制器的设计方法。该方法既简化了合成推理运算,又能满足模糊控制规则的完整性要求,从本质上消除由于量化误差和调节死区给模糊控制系统带来的稳态误差与颤振现象。通过仿真证明系统的性能得到明显改善。     

The multivariate non-homogeneous Markov manpower system in a departmental mobility framework

The present paper proposes a non-homogeneous multivariate Markov manpower system in the general category of mathematical human resource planning. More specifically, we suggest a model, which takes into account the divisions existing in an organization categorizing its employees into several groups (departments). In this context, it considers not only possible transitions within the departments (intra-department transitions), but also, transfers of personnel between departments (inter-department transitions). Additionally, the proposed modeling structure is accompanied by cost and stocks (personnel) objectives which are set and in the sequel could be achieved by controlling either the recruitment policy or the allocation policy of employees transferred to other departments (or both). We use a minmax fuzzy goal-programming approach, under different operating assumptions, in order to keep the operational cost below desired aspiration levels and reach desirable stock structures in the presence of system’s constraints and regulations. The paper concludes with a numerical illustration.     

Necessary conditions for optimality in relaxed stochastic control problems

In this paper, we are concerned with optimal control problems where the system is driven by a stochastic differential equation of the Ito type. We study the relaxed model for which an optimal solution exists. This is an extension of the initial control problem, where admissible controls are measure valued processes. Using Ekeland's variational principle and some stability properties of the corresponding state equation and adjoint processes, we establish necessary conditions for optimality satisfied by an optimal relaxed control. This is the first version of the stochastic maximum principle that covers relaxed controls.     

Risk-sensitive portfolio optimization on infinite time horizon

We consider a continuous time portfolio optimization problems on an infinite time horizon for a factor model, recently treated by Bielecki and Pliska ["Risk-sensitive dynamic asset management", Appl. Math. Optim. , 39 (1990) 337-360], where the mean returns of individual securities or asset categories are explicitly affected by economic factors. The factors are assumed to be Gaussian processes. We see new features in constructing optimal strategies for risk-sensitive criteria of the portfolio optimization on an infinite time horizon, which are obtained from the solutions of matrix Riccati equations.     

Admissibility and controllability of diagonal Volterra equations with scalar inputs

This article studies Volterra evolution equations from the point of view of control theory, in the case that the generator of the underlying semigroup has a Riesz basis of eigenvectors. Conditions for admissibility of the system's control operator are given in terms of the Carleson embedding properties of certain discrete measures. Moreover, exact and null controllability are expressed in terms of a new interpolation question for analytic functions, providing a generalization of results known to hold for the standard Cauchy problem. The results are illustrated by examples involving heat conduction with memory.     

The distance of a subdivision surface to its control polyhedron

For standard subdivision algorithms and generic input data, near an extraordinary point the distance from the limit surface to the control polyhedron after m subdivision steps is shown to decay dominated by the mth power of the subsubdominant (third largest) eigenvalue. Conversely, for Loop subdivision we exhibit generic input data so that the Hausdorff distance at the mth step is greater than or equal to the mth power of the subsubdominant eigenvalue.In practice, it is important to closely predict the number of subdivision steps necessary so that the control polyhedron approximates the surface to within a fixed distance. Based on the above analysis, two such predictions are evaluated. The first is a popular heuristic that analyzes the distance only for control points and not for the facets of the control polyhedron. For a set of test polyhedra this prediction is remarkably close to the true distance. However, a concrete example shows that the prediction is not safe but can prescribe too few steps. The second approach is to first locally, per vertex neighborhood, subdivide the input net and then apply tabulated bounds on the eigenfunctions of the subdivision algorithm. This yields always safe predictions that are within one step for a set of typical test surfaces.     

Control issues for the Beverton-Holt equation in ecology by locally monitoring the environment carrying capacity: Non-adaptive and adaptive cases

This paper proposes a control algorithm to govern the solution of the Beverton-Holt equation (BHE) under the potentially presence of additive disturbances. The BHE to be controlled is defined by certain intrinsic growth rate and environment carrying capacity sequences, the last one being susceptible of local modifications around nominal values. In fact, the control action provides the carrying capacity which makes that the solution of the current BHE tracks a reference sequence given by another BHE defined by appropriate intrinsic growth rate and environment carrying capacity sequences. In this context, the fact that the inverse of the BHE is a discrete time-varying linear system is taken into account where the inverse of the carrying capacity sequence plays the role of control sequence. The current and the reference BHEs have to be close enough to each other in order that local modifications of the carrying capacity be able to meet the tracking objective. A feedback control law is designed to achieve such an objective with a zero tracking-error in the ideal case of known intrinsic growth rate sequence and no presence of disturbances. An adaptive control law, with the associated parameter estimation algorithm, is considered when the intrinsic growth rate is fully or partially unknown and disturbances are present. Such a control strategy guarantees a bounded tracking-error with the error converging asymptotically to zero in case that additive disturbances also converge to zero. Some results obtained from a simulation example illustrate the effectiveness of this control strategy.     

RECOVERY A POSTERIORI ERROR ESTIMATES FOR GENERAL CONVEX ELLIPTIC OPTIMAL CONTROL PROBLEMS SUBJECT TO POINTWISE CONTROL CONSTRAINTS

Superconvergence and recovery a posteriori error estimates of the finite element ap- proximation for general convex optimal control problems are investigated in this paper. We obtain the superconvergence properties of finite element solutions, and by using the superconvergence results we get recovery a posteriori error estimates which are asymptotically exact under some regularity conditions. Some numerical examples are provided to verify the theoretical results.     

A decentralized control mechanism for stream processing networks

Data streaming applications are becoming more and more common due to the rapid development in emerging areas such as sensor networks, multimedia streaming, and on-line data mining, etc. These applications are often running in a decentralized, distributed environment. The requirements for processing large volumes of streaming data at real time have posed many great design challenges. One of the critical issues is to optimize the ongoing resource consumption of multiple, distributed, cooperating processing units. In this paper, we consider a generic model for the general stream data processing systems. We address the resource allocation problem for a collection of processing units so as to maximize the weighted sum of the throughput of different streams. Each processing unit may require multiple input data streams simultaneously and produce one or many valuable output streams. We develop decentralized control mechanisms that maximize the overall system throughput in such data stream processing networks. Performance analysis on the optimality and complexity of these mechanisms are also provided.     

Effects of system parameters on the optimal policy structure in a class of queueing control problems

This paper studies a class of queueing control problems involving commonly used control mechanisms such as admission control and pricing. It is well established that in a number of these problems, there is an optimal policy that can be described by a few parameters. From a design point of view, it is useful to understand how such an optimal policy varies with changes in system parameters. We present a general framework to investigate the policy implications of the changes in system parameters by using event-based dynamic programming. In this framework, the control model is represented by a number of common operators, and the effect of system parameters on the structured optimal policy is analyzed for each individual operator. Whenever a queueing control problem can be modeled by these operators, the effects of system parameters on the optimal policy follow from this analysis.