A language measure for supervisory control

This paper formulates a signed real measure for sublanguages of regular languages based on the principles of automata theory and real analysis. The measure provides total ordering on the controlled behavior of a finite-state automaton plant under different supervisors. Total variation of the measure serves as a metric for the infinite-dimensional vector space of the sublanguages of a regular language over the finite field GF(2). The computational complexity of the language measure is of polynomial order in the number of plant states.     

A supervisory control paradigm for real-time control of flexible manufacturing systems

Most of the current academic flexible manufacturing system (FMS) scheduling research has focused on the derivation of algorithms or knowledge-based techniques for efficient FMS real-time control. Here, the limitations of this view are outlined with respect to effective control of actual real-time FMS operation. A more realistic paradigm for real-time FMS control is presented, based on explicit engineering of human and automated control functions and system interfaces. To illustrate design principles within the conceptual model, an example of algorithmic and operator function models for a specific real-time FMS control problem are developed.Portions of this paper have appeared in: Proc. 2nd ORSA/TIMS Conf. on Flexible Manufacturing Systems: Operations Research Models and Applications, Ann Arbor, Michigan, August 12–15, 1986, and Proc. 1986 Int. Conf. on Systems, Man, and Cybernetics, Atlanta, Georgia, October 14–17, 1986.This research was supported in part by the New Faculty Research Program of the Georgia Institute of Technology.     

On-line identification of parameters of a linear multivariable discrete-time system and their convergence characteristics

A recursive algorithm for on-line identification of the parameters of linear, discrete-time, multi-input, multi-output nondynamical, and dynamical systems using noisy input and output measurements is presented in detail. Necessary and sufficient conditions for the convergence of the recursive algorithm, under certain restrictive assumptions, for arbitrary choice of initial values of the matrices described in the sequel are explicitly derived, which is one of the new results of this paper.     

Multicriteria approach for intelligent decision support in supervisory control

In the framework of integrated automation, this work concerns the top level of the management and supervision of complex automated systems. When a process is being disturbed, the supervisory function modifies the established production planning, in accordance with different norms and constraints. The operator remains beside the regulated process controls to perform manual operations. The number of potential actions and the conflicting nature of some objectives make his task complex: he must reach quantitative and qualitative objectives with imperfect and temporal information. To assist him, we study a decision support model following a multicriteria approach involving the supervision problem. AI techniques and DSS are used to develop the aid tool. The Spinning Reserve problem encountered by Electricité de France is studied and used as support. To test our concepts, we develop the CASTART experimental support based on a synergy between the user, the problem, and the resolution models.This study is co-financed by the Conseil Régional Nord-Pas de Calais (France) and the University of Valenciennes.     

On-line control model for network construction projects

An activity-on-arc network project of the PERT type with random activity durations is considered. For each activity, its accomplishment is measured in percentages of the total project. When operated, each activity utilizes resources of a pregiven capacity and no resource reallocation is undertaken in the course of the project's realization. Each activity can be operated at several possible speeds that are subject to random disturbances and correspond to one and the same resource capacity; that is, these speeds depend only on the degree of intensity of the project's realization. For example, in construction projects partial accomplishments are usually measured in percentages of the total project, while different speeds correspond to different hours a day per worker. The number of possible speeds is common to all activities. For each activity, speeds are sorted in ascending order of their average values—namely speeds are indexed. It is assumed that at any moment t>0 activities, in operation at that moment, have to apply speeds of one and the same index that actually determines the project's speed. The progress of the project can be evaluated only via inspection at control points that have to be determined. The project's due date and the chance constraint to meet the deadline are pregiven. An on-line control model is suggested that, at each control point, faces a stochastic optimization problem. Two conflicting objectives are imbedded in the model:(1) to minimize the number of control points, and(2) to minimize the average index of the project's speeds which can be changed only at a control point.At each routine control point, decision-making centers on determining the next control point and the new index of the speeds (for all activities to be operated) up to that point. The model's performance is verified via simulation.The developed on-line control algorithm can be used for various PERT network projects which can be realized with different speeds, including construction projects and R&D projects.     

A statistical process control approach to selecting a warm-up period for a discrete-event simulation

The selection of a warm-up period for a discrete-event simulation continues to be problematic. A variety of selection methods have been devised, and are briefly reviewed. It is apparent that no one method can be recommended above any other. A new approach, based upon the principles of statistical process control, is described (SPC method). Because simulation output data are often highly autocorrelated and potentially non-normal, the batch means method is employed in constructing the control chart. The SPC method is tested on seven data sets and encouraging results are obtained concerning its accuracy. The approach is also discussed with respect to its ease of implementation, simplicity, generality of use and requirements for parameter estimation.     

On-line control of trucking. I

Translated from Issledovaniya po Prikladnoi Matematike, No. 12, pp. 17–24, 1984.     

Automated selection of the number of replications for a discrete-event simulation

Selecting an appropriate number of replications to run with a simulation model is important in assuring that the desired accuracy and precision of the results are attained with minimal effort. If too few are selected then accuracy and precision are lost; if too many are selected then valuable time is wasted. Given that simulation is often used by non-specialists, it seems important to provide guidance on the number of replications required with a model. In this paper an algorithm for automatically selecting the number of replications is described. Test results show the algorithm to be effective in obtaining coverage of the expected mean at a given level of precision and in reducing the bias in the estimate of the mean of the simulation output. The algorithm is consistent in selecting the expected number of replications required for a model output.     

New approach for the identification of power system economic dispatch parameters

It is well known that the coefficients of the input-output characteristics of the thermal steam-turbine model as well as the network model parameters have a great effect on the optimal economic operation of all thermal-electric power systems. Until today, these coefficients, the loss formula coefficients, theB-coefficients, and the active-reactive power loss models have been estimated using the well-known least-square estimation algorithm.In this paper, we present a new algorithm to estimate the power system parameters for economic dispatch calculation (EDC); this algorithm is based on the least absolute-value approximation (LAV)l ₁-norm. We compare the results obtained using the proposed algorithms with those obtained using the least-square error algorithm (LS). Optimal costs as well as overall network performance resulting from the implementation of each technique provide the basis of our conclusion.This work was supported by the Natural Science and Engineering Research Council of Canada, Grant No. A4146.     

Maximum principles for control systems described by measure functional differential equations

Three optimal control problems involving measure functional differential equations are considered. The necessary conditions, in the form of the Pontryagin maximum principle, for an optimal control are obtained. This is accomplished by the application of a theorem by Debovistskii-Milyutin. A simple example is also illustrated to show the applicability of the results obtained.     

Invariant imbedding and the identification of aquifer parameters

The technique of invariant imbedding is applied to the problem of identifying the parameters in an unconfined aquifer system. This new technique is shown to be a very effective way of converting field observations based upon pumping tests into the desired aquifer parameters. The procedure is straightforward as it requires neither curve plotting nor graphical matching. The parameters to be identified are the hydraulic conductivity and specific storage in an extensive unconfined aquifer system. Results and numerical experiments are presented.Identification is an inverse process whereby the parameters embedded in a differential equation are determined from observations of systems input and output along with appropriate initial and boundary conditions. These parameters are usually noy physically measurable. In general, the governing equation is nonlinear with no closed-form solution.In this paper, this inverse problem is solved by invariant imbedding and quasilinearization. A comparison is made between these methods. The problem of convergence and stability is discussed and demonstrated by numerical experimentation.     

Optimum parameters of hierarchical control structures

The use of multilevel hierarchical structures to effectively conduct inspections and control corruption under the conditions of a shortage of authorized representatives is proposed. Game theory models of such structures are considered and the problem of their organization is reviewed. The optimum strategy for checking in a hierarchy is determined. The results from numerical modeling for the example of taxation of small businesses are given.     

Initial data truncation for multivariate output of discrete-event simulations using the Kalman filter

Data truncation is a commonly accepted method of dealing with initialization bias in discrete-event simulation. An algorithm for determining the appropriate initial-data truncation point for multivariate output is proposed. The technique entails averaging across independent replications and estimating a steady-state output model in a state-space framework. A Bayesian technique called Multiple Model Adaptive Estimation (MMAE) is applied to compute a time varying estimate of the output's steady-state mean vector. This MMAE implementation features the use, in paralle, of a bank of Kalman filters. Each filter is constructed under a different assumption concerning the output's steady-state mean vector. One of the filters assumes that the steady-state mean vector is accurately reflected by an estimate, called the assumed steady-state mean vector, taken from the last half of the simulation data. As the filters process the output through the effective transient, this particular filter becomes more likely (in a Bayesian sense) to be the best filter to represent the data and the MMAE mean estimator is influenced increasingly towards the assumed steady-state mean vector. The estimated truncation point is selected when a norm of the MMAE mean vector estimate is within a small tolerance of the assumed steady-state mean vector. A Monte Carlo analysis using data from simulations of open and closed queueing models is used to evaluate the technique. The evaluation criteria include the ability to construct accurate and reliable confidence regions for the mean response vector based on the truncated sequences.     

Adaptive finite element methods for the identification of distributed parameters in elliptic equation

In this paper, adaptive finite element method is developed for the estimation of distributed parameter in elliptic equation. Both upper and lower error bound are derived and used to improve the accuracy by appropriate mesh refinement. An efficient preconditioned project gradient algorithm is employed to solve the nonlinear least-squares problem arising in the context of parameter identification problem. The efficiency of our error estimators is demonstrated by some numerical experiments.      

参数识别问题混合有限元解的最大模误差估计

研究了参数识别问题混合有限元解的最大模误差估计.利用1阶Raviart-Thomas混合有限元离散状态和对偶状态变量,利用分片线性函数逼近控制变量,获得了状态变量和控制变量的最大模误差估计,这里控制变量的收敛阶是h~2,状态变量的收敛阶是h3/2|lnh|1/2.最后利用数值算例验证了理论结果.     

Application of moving averaging for automated control of sinusoidally varying parameters

Journal of Mathematical Sciences -     

Informational measure of certainty and its use in the identification of plants

This paper introduces a measure of certainty, the characteristic of the similarity of the mathematical model to the actual plant, based on the basic concepts of information theory. The properties of the newly introduced quantity are investigated, and so is its relation to the dispersional measure of certainty. An example is considered where the economic efficiency of the control system is computed by its informational characteristics.     

Bilevel parameters identification for the multi-stage nonlinear impulsive system in microorganisms fed-batch cultures

Considering the abrupt jump of the substrate and different characters of the bacterial in the lag phase, the exponential phase and the stationary phase this paper proposes the multi-stage nonlinear impulsive system for the fed-batch fermentation from glycerol to 1,3-propanediol (1,3-PD) and establishes the bilevel identification system for its sensitive parameters. The properties of the solutions for the nonlinear multi-stage dynamical system are investigated and identifiability of the parameters is proved. Finally an optimal algorithm is constructed to obtain the optimal solution of the identification model and the numerical example is then discussed to illustrate the algorithm.     

On joint identification of the feedback parameters for hyperchaotic systems: An optimization-based approach

We propose an optimization-based scheme for parameter estimation in high-dimensional chaotic systems, and the symbolic time series analysis (STSA) based method is adopted to address the estimation problem. It is shown that, when the system structure and the corresponding time series are known, the STSA-based method works better with respect to the autocorrelation function (ACF) and the mutual information (MI) technologies. Most importantly, the time delay and the feedback strength of two test systems, i.e., the Mackey–Glass system and an external-cavity semiconductor laser system, can be successfully identified using the proposed scheme. To explore the noise immunity, the influence of certain levels of noise on the STSA-based method is tested.