SIMULATION: Transactions of The Society for Modeling and Simulation International

Pratt, Charles A.

doi: 10.1177/003754978704800101pmid: N/A

Siebert, J. Paul; Winning, David J.

doi: 10.1177/003754978704800102pmid: N/A

A Control Oriented Language (COL) has been designed to assist engineers in specifying self-contained real-time programs targeted for embedded microprocessor controllers. The language com prises concurrent CSSL-based task segments which support continuouslsequential control and simulation models, and sub- models. A real-time operating system kernel is provided to sched ule these tasks. Low-level hardware access statements and a real- time debugging facility have also been incorporated within the COL.

Riggs, Tom L.; Phillips, Charles L.

doi: 10.1177/003754978704800103pmid: N/A

Physical systems often contain subsystems that are "best" model ed as continuous random processes. When these processes are included in digital simulations, the statistics of the random num ber generator used in the simulation must be selected such that the continuous random process is modeled faithfully. In general the statistics of the random number generator will be drastically different from the statistics of the continuous random process. This paper presents the functional relationship between the sta tistics of the random number generator and the statistics of the continuous random process for simulations using two commonly employed integration methods.

Mamalis, A.G.; Bilalis, N.G.; Konstantinidis, M.J.

doi: 10.1177/003754978704800104pmid: N/A

A simulation model capable of representing a large variety of flexible manufacturing systems (FMS) has been developed. The software, written entirely in FORTRAN, can be used during the design and control stages of the FMS development. The several modules of the model are described. The model's validation is primarily examined by comparing its results with those obtained using a model written in a specialized simulation language, namely ECSL. Fifteen control strategies are defined and simulation results discussed.

Nowack, Stefan; Feil, Georg

doi: 10.1177/003754978704800105pmid: N/A

Numerical (digital) integration in a real-time simulation is often performed using explicit Runge-Kutta methods. Real-time system simulation places high demands on computers and integration routines, if data must be sampled from input signals and incor porated into the numerical integration algorithm in order to evaluate the derivatives of the state variables.A modified fourth-order Runge-Kutta (RK4) method, which meets these requirements particularly well is proposed. As an example the routine is applied to a complex flight simulation problem. The results of the modified routine and of the RK4 standard are compared and the specific advantages of the new routine demonstrated.

Smith, Richard L.; Platt, Lucille

doi: 10.1177/003754978704800106pmid: N/A

doi: 10.1177/003754978704800107pmid: N/A

McLeod, John; McLeod, Suzette

doi: 10.1177/003754978704800108pmid: N/A