We consider a simple class of controlled queue length processes in the fluid rescaling, X(t) = 1 n X(nt). Consider the optimal control problem for X(t) which consists of minimizing the integral of an undiscounted positive running cost until the first time that X = 0. Our main result uses weak convergence ideas to show that the optimal value functions V n of the stochastic control problems for X...

This paper proposes a novel adaptive control law for nonlinear systems using Takagi-Sugeno fuzzy system. Takagi-Sugeno fuzzy system is used to identify nonlinear system components theta alpha and theta beta. Stable Indirect Adaptive control law is such that it has two control components one is certainty equivalence control and other is sliding mode control. Sliding mode controller is used to en...

in this paper we first describe the stochastic optimal control algorithm called ((optcon)). the algorithm minimizes an intertemporal objective loss function subject to a nonlinear dynamic system in order to achieve optimal value of control (or instrument) variables. second as an application, we implemented the algorithm by the statistical programming system ((gauss)) to determine the optimal fi...

The certainty equivalence and polynomial approach, widely used for designing adaptive controllers, leads to “simple” adaptive control designs that guarantee stability, asymptotic error convergence, and robustness, but not necessarily good transient performance. Backstepping and tuning functions techniques, on the other hand, are used to design adaptive controllers that guarantee stability and g...

This paper shows implementation of indirect adaptive control law for nonlinear systems using TakagiSugeno fuzzy system. Takagi-Sugeno fuzzy system is used to identify nonlinear system components theta alpha and theta beta. Stable Indirect Adaptive control law is such that it has two control components one is certainty equivalence control and other is sliding mode control. Sliding mode controlle...

Abstract We explore efficient estimation of statistical quantities, particularly rare event probabilities, for stochastic reaction networks. Consequently, we propose an importance sampling (IS) approach to improve the Monte Carlo (MC) estimator efficiency based on approximate tau-leap scheme. The crucial step in IS framework is choosing appropriate change probability measure achieve substantial...

We consider an infinite-horizon linear-quadratic minimax optimal control problem for stochastic uncertain systems with output measurement. A new description of stochastic uncertainty is introduced using a relative entropy constraint. For the stochastic uncertain system under consideration, a connection between the worst-case control design problem and a specially parametrized risk-sensitive sto...

This paper is concerned with linear stochastic control systems in state space. The integral of the squared norm of the system output over a bounded time interval is interpreted as energy. The cumulants of the output energy in the infinite-horizon limit are related to Schatten norms of the system in the Hardy space of transfer functions and the risk-sensitive performance index. We employ a novel...