Upper Bound Estimates of the Cramér Functionals for Markov Processes

We prove that the Cramér functional of a Markov process can be controlled by a function of an integral functional if the transition semigroup is uniformly integrable in L ^p . As an application of this result, a general large deviation upper bound is obtained. Then, the notation of F-Sobolev inequality is extended to general Markov processes by replacing the Dirichlet form with the Donsker–Varadhan entropy. As the other application, it is proved that the uniform integrability of a transition semigroup implies a F-Sobolev inequality.     

Smooth finite-dimensional approximations of distributed optimization problems via control discretization

Approximating finite-dimensional mathematical programming problems are studied that arise from piecewise constant discretization of controls in the optimization of distributed systems of a fairly broad class. The smoothness of the approximating problems is established. Gradient formulas are derived that make use of the analytical solution of the original control system and its adjoint, thus providing an opportunity for algorithmic separation of numerical optimization and the task of solving a controlled initial-boundary value problem. The approximating problems are proved to converge to the original optimization problem with respect to the functional as the discretization is refined. The application of the approach to optimization problems is illustrated by solving the semilinear wave equation controlled by applying an integral criterion. The results of numerical experiments are analyzed.     

On duality for mathematical programs with vanishing constraints

We make a review of several variants of ergodicity for continuous-time Markov chains on a countable state space. These include strong ergodicity, ergodicity in weighted-norm spaces, exponential and subexponential ergodicity. We also study uniform exponential ergodicity for continuous-time controlled Markov chains, as a tool to deal with average reward and related optimality criteria. A discussion on the corresponding ergodicity properties is made, and an application to a controlled population system is shown.     

Viability with Probabilistic Knowledge of Initial Condition, Application to Optimal Control

In this paper we provide an extension of the Viability and Invariance Theorems in the Wasserstein metric space of probability measures with finite quadratic moments in ? ^d for controlled systems of which the dynamic is bounded and Lipschitz. Then we characterize the viability and invariance kernels as the largest viability (resp. invariance) domains. As application of our result we consider an optimal control problem of Mayer type with lower semicontinuous cost function for the same controlled system with uncertainty on the initial state modeled by a probability measure. Following Frankowska, we prove using the epigraphical viability approach that the value function is the unique lower semicontinuous proximal episolution of a suitable Hamilton Jacobi equation.     

On the convergence of the conditional gradient method in distributed optimization problems

A theorem is stated on sufficient conditions for the convergence of the conditional gradient method as applied to the optimization of a nonlinear controlled functional-operator equation in a Banach ideal space. The theory is illustrated by application to the controlled Goursat-Darboux problem.     

A maximum principle for fully coupled stochastic control systems of mean-field type

The present paper considers an optimal control problem for fully coupled forward–backward stochastic differential equations (FBSDEs) of mean-field type in the case of controlled diffusion coefficient. Moreover, the control domain is not assumed to be convex. By virtue of a reduction method, we establish the necessary optimality conditions of Pontryagin's type. As an application, a linear–quadratic stochastic control problem is studied.     

On near-optimal necessary and sufficient conditions for forward-backward stochastic systems with jumps,with applications to finance

We establish necessary and sufficient conditions of near-optimality for nonlinear systems governed by forward-backward stochastic differential equations with controlled jump processes (FBSDEJs in short). The set of controls under consideration is necessarily convex. The proof of our result is based on Ekeland’s variational principle and continuity in some sense of the state and adjoint processes with respect to the control variable. We prove that under an additional hypothesis, the near-maximum condition on the Hamiltonian function is a sufficient condition for near-optimality. At the end, as an application to finance, mean-variance portfolio selection mixed with a recursive utility optimization problem is given. Mokhtar Hafay     

Hybrid dynamical systems with controlled discrete transitions

This invited survey focuses on a new class of systems–hybrid dynamical systems with controlled discrete transitions. A type of system behavior referred to as the controlled infinitesimal dynamics is shown to arise in systems with widely divergent dynamic structures and application domains. This type of behavior is demonstrated to give rise to a new dynamic mode in hybrid system evolution–a controlled discrete transition. Conceptual and analytical frameworks for modeling of and controller synthesis for such transitions are detailed for two systems classes: one requiring bumpless switching among controllers with different properties, and the other–exhibiting single controlled impacts and controlled impact sequences under collision with constraints. The machinery developed for the latter systems is also shown to be capable of analysing the behavior of difficult to model systems characterized by accumulation points, or Zeno-type behavior, and unique system motion extensions beyond them in the form of sliding modes along the constraint boundary. The examples considered demonstrate that dynamical systems with controlled discrete transitions constitute a general class of hybrid systems.     

Cyclic subgradient projections

A cyclically controlled method of subgradient projections (CSP) for the convex feasibility problem of solving convex inequalities is presented. The features of this method make it an efficient tool in handling huge and sparse problems. A particular application to an image reconstruction problem of emission computerized tomography is mentioned.Research supported by National Institute of Health Grant HL 28438-01.     

Adaptive control of discrete-time chaotic systems: a fuzzy control approach

This paper discusses adaptive control of a class of discrete-time chaotic systems from a fuzzy control approach. Using the T–S model of discrete-time chaotic systems, an adaptive control algorithm is developed based on some conventional adaptive control techniques. The resulting adaptively controlled chaotic system is shown to be globally stable, and its robustness is discussed. A simulation example of the chaotic Henon map control is finally presented, to illustrate an application and the performance of the proposed control algorithm.     

Robustness of controllability under some unbounded perturbations

In this work, we prove that the exact controllability of linear autonomous systems are conserved with “small” Desch-Schappacher perturbations arising, e.g., from the perturbations of dynamic operator's domain. Our results are illustrated by an application to controlled systems with dynamic and boundary perturbations.     

Evolving systems: Adaptive key component control and inheritance of passivity and dissipativity

We propose a new framework called Evolving Systems to describe the self-assembly, or autonomous assembly, of actively controlled dynamical subsystems into an Evolved System with a higher purpose. Autonomous assembly of large, complex flexible structures in space is a target application for Evolving Systems. A critical requirement for autonomous assembling structures is that they remain stable during and after assembly. The fundamental topic of inheritance of stability, dissipativity, and passivity in Evolving Systems is the primary focus of this research. In this paper, we develop an adaptive key component controller to restore stability in Nonlinear Evolving Systems that would otherwise fail to inherit the stability traits of their components. We provide sufficient conditions for the use of this novel control method and demonstrate its use on an illustrative example.     

Non-randomized policies for constrained Markov decision processes

This paper addresses constrained Markov decision processes, with expected discounted total cost criteria, which are controlled by non-randomized policies. A dynamic programming approach is used to construct optimal policies. The convergence of the series of finite horizon value functions to the infinite horizon value function is also shown. A simple example illustrating an application is presented.     

Solution of an optimal control problem with phase constraints by the double variation method

An optimal control problem with an integral quality index specified in a finite time interval is formulated for a model of economic growth that leads to emission of greenhouse gases. The controlled system is linear with respect to control. The problem contains phase constraints that abandon emission of greenhouse gases above some predefined time-dependent limit. As is known, optimal control problems with phase constraints fall beyond the sphere of efficient application of the Pontryagin maximum principle because, for such problems, this principle is formulated in a complicated form difficult for analytic treatment in particular situations. In this study, the analytic structure of the optimal control and phase trajectories is constructed using the double variation method.     

Simple models for biological control of crop pests and their application

In this work, we construct simple models in terms of differential equations for the dynamics of pest populations and their management using biological pest control. For the first model used, the effect of the biological control is modelled by a function of repeated infinite impulses. And, our second model uses a periodic function proportional to the population to model the effect of biological control. In both cases, we present analytical solutions and derive a discrete version of them. Moreover, convergence conditions are given for periodic solutions. Finally, an application of such models is described for diamondback moth in a plot of broccoli to be controlled by the application of biological pesticides and beneficial parasitoids.     

Automating computations in the virtual Tokamak software system

We describe the concept, functional capabilities, graphic user interface (GUI), and operating technique of a specialized system for distributing the computational burden encountered in solving typical problems of controlled thermonuclear fusion. The system is employed in the Virtual Tokamak simulation modeling complex to automate the distribution of computing on a network of computers, making it possible to dramatically improve the productivity of a researcher??s work. The system is useful in various applications that require massive multivariate calculations using one or more application codes, and for supporting websites that provide computing services using locally stored science-intensive application software.     

A variational formula for controlled backward stochastic partial differential equations and some application

An optimal control problem for a controlled backward stochastic partial differential equation in the abstract evolution form with a Bolza type performance functional is considered. The control domain is not assumed to be convex, and all coefficients of the system are allowed to be random. A variational formula for the functional in a given control process direction is derived, by the Hamiltonian and associated adjoint system. As an application, a global stochastic maximum principle of Pontraygins type for the optimal controls is established.     

Two-dimensional time-dependent simulation of contaminant transport from a landfill

A numerical model to simulate solute transport in flowing groundwater is developed using finite-difference solutions to the groundwater flow equation and the solute transport equation.The use of the model is illustrated by its application to a problem of groundwater contamination in a waste-disposal area in Miami, Florida. The model integrates the effects of several factors that controlled changes in iron concentrations, and successfully reproduced the record of contamination observed during a one-year period.The model was found to be insensitive to chemical parameters, but sensitive to an aquifer dispersion coefficient and the groundwater flow velocity. This implies that there can be sites where the chemical attenuation can be approximated without a significant impact on results.     

Flight Control of Biomimetic Air Vehicles Using Vibrational Control and Averaging

A combination of vibrational inputs and state feedback is applied to control the flight of a biomimetic air vehicle. First, a control strategy is developed for longitudinal flight, using a quasi-steady aerodynamic model and neglecting wing inertial effects. Vertical and forward motion is controlled by modulating the wings’ stroke and feather angles, respectively. Stabilizing control parameter values are determined using the time-averaged dynamic model. Simulations of a system resembling a hawkmoth show that the proposed controller can overcome modeling error associated with the wing inertia and small parameter uncertainties when following a prescribed trajectory. After introducing the approach through an application to longitudinal flight, the control strategy is extended to address flight in three-dimensional space.     

Synthesis of the Optimal Control of Linear Logical-Dynamical Systems Under Instant Multiple Switchings of the Automaton Part

We consider a dynamic system controlled by an automaton with memory. The continuous part of the system is described by linear differential equations and the logical (automaton) part is described by linear recurrence equations. The moments of the state change of the automaton part are not known in advance and they are determined in the process of optimization. Modes with instant multiple switchings of the automaton part are admitted. Based on sufficient optimality conditions, we develop a technique for the synthesis of a feedback control. The application of the technique is demonstrated in an example.