Dynamic routing and admission control for virtual circuit networks

The dynamic joint routing and admission control problem in multiple class multiple source-destination virtual circuit networks is considered. A nonlinear dynamic queueing model for virtual circuit networks that considers the dynamic interaction among the virtual circuit and packet processes is introduced. Then a multi-objective cost function of rejecting and maintaining virtual circuits, as well as of delaying and servicing packets is defined. The combined problem is formulated as an optimal control problem. Necessary optimality conditions are provided by Pontryagin's maximum principle. Sufficient optimality conditions based on the convexity of the Hamiltonian function are also given. For the finite horizon, the optimal controls can be found after numerically solving a Two-Point Boundary-Value Problem. For the longrun stationary equilibrium, the state-dependent routing and admission controls are derived.This work was supported by the National Science Foundation under Grant DMC-8452002 together with matching funds from AT&T Information Systems.     

Database audit and control strategies

Database management systems are the primary tools of automated record keeping, reporting, auditing, and control. Although they have significantly improved the efficiency and speed of record keeping, the ability to detect errors and maintain quality has not kept pace. There are three major strategies of error detection, auditing, and control to maintain integrity. The three strategies are introduced, and compared in terms of efficiency and effectiveness in eliminating errors. The optimum timing of audits under each strategy is computed. One strategy is shown to be completely dominated by the others. Hybrid strategies involving various combinations of the three pure strategies are introduced, and their performance computed. Hybrid strategies are shown to outperform the pure strategies under most realistic conditions. Finally, optimum audit strategies are devised, and their parameters computed.     

Prescribed performance adaptive neural output feedback dynamic surface control for a class of strict‐feedback uncertain nonlinear systems with full state constraints and unmodeled dynamics

This paper studies the output feedback tracking control problem for a class of strict‐feedback uncertain nonlinear systems with full state constraints and unmodeled dynamics using a prescribed performance adaptive neural dynamic surface control design approach. A nonlinear mapping technique is employed to address the state constraints. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions. The unmodeled dynamics is addressed by introducing an available dynamic signal. Subsequently, we construct the controller and parameter adaptive laws using a backstepping technique. Based on Lyapunov stability theory, it is shown that all signals in the closed‐loop system are semiglobally uniformly ultimately bounded and that the tracking error always remains within the prescribed performance bound. Simulation results are presented to demonstrate the effectiveness of the proposed control scheme.