[en] The study of infinite-horizon nonstationary dynamic programs using the operator approach is continued. The point of view here differs slightly from that taken by others, in that Denardo's local income function is not used as a starting point. Infinite-horizon values are defined as limits of finite-horizon values, as the horizons get long. Two important conditions of an earlier paper are weakened, yet the optimality equations, the optimality criterion, and the existence of optimal ''structured'' strategies are still obtained

[en] An application of the linear optimal control theory to the problem of load follow control in pressurized water reactors (PWR) is investigated. In order to perform the steady-state and load follow operation in PWR, a nonlinear model for the reactor and steam generator is derived and linearized at 50% rated power. Simulation tests are performed for 10% demanded load. Comparing the dynamic response of the newly developed optimal load follow controller with those of the integral error feedback controller proposed by Yang, the rise time of dynamic response of the former is about 15 seconds faster than those of the latter, thus the results indicate that the fast response of the optimal load follow controlled is verified. The results of this work are directly applicable to the design of the load control systems for commercially operated PWRs. (Author)

[en] In this Letter, power control problem is firstly studied at an angle of LQG measurement-feedback control problem. A stochastic uplink power control problem is considered for CDMA systems. An effective distributed algorithm is proposed based on stochastic linear quadratic optimal control theory assuming SIR measurements contain white noise. The presented scheme minimizes the sum of the power and the error of SIR. A measurement-feedback power controller is designed by constructing an optimization problem of a stochastic linear quadratic type in Krein space and solving the Kalman filter problem for the systems

[en] We propose nearly optimal control strategies for changing the states of a quantum system. We argue that quantum control optimization can be studied analytically within some protocol families that depend on a small set of parameters for optimization. This optimization strategy can be preferred in practice because it is physically transparent and does not lead to combinatorial complexity in multistate problems. As a demonstration, we design optimized control protocols that achieve switching between orthogonal states of a naturally biased quantum two-level system. (paper)

[en] We analyze a system of N components with dependent failure times. The goal is to obtain the optimal block replacement interval (different for each component) over a finite horizon that minimizes the expected total maintenance cost. In addition, we allow each preventive maintenance action to change the future joint failure time distribution. We illustrate our methodology with an example from South Texas Project Nuclear Operating Company. (authors)

[en] In this paper we are concerned with the optimal control for a class of systems governed by the eigen-equation: Lphi + sigma phi = (1/lambda) K phi where the state (lambda(sigma), phi(sigma)) is the dominate eigenvalue of the operator (L + sigma)^-1 K and its corresponding eigenfunction. Under the variance index, we show the existence and the uniqueness of optimal control and give the corresponding optimality conditions. Finally the obtained general results are applied to the power factor optimization for a nuclear reactor

[en] Quantum optimal control experiments and simulations have successfully manipulated the dynamics of systems ranging from atoms to biomolecules. Surprisingly, these collective works indicate that the effort (i.e., the number of algorithmic iterations) required to find an optimal control field appears to be essentially invariant to the complexity of the system. The present work explores this matter in a series of systematic optimizations of the state-to-state transition probability on model quantum systems with the number of states N ranging from 5 through 100. The optimizations occur over a landscape defined by the transition probability as a function of the control field. Previous theoretical studies on the topology of quantum control landscapes established that they should be free of suboptimal traps under reasonable physical conditions. The simulations in this work include nearly 5000 individual optimization test cases, all of which confirm this prediction by fully achieving optimal population transfer of at least 99.9% on careful attention to numerical procedures to ensure that the controls are free of constraints. Collectively, the simulation results additionally show invariance of required search effort to system dimension N. This behavior is rationalized in terms of the structural features of the underlying control landscape. The very attractive observed scaling with system complexity may be understood by considering the distance traveled on the control landscape during a search and the magnitude of the control landscape slope. Exceptions to this favorable scaling behavior can arise when the initial control field fluence is too large or when the target final state recedes from the initial state as N increases.

[en] These are lecture notes of the introductory course in Optimal Control theory treated from the geometric point of view. Optimal Control Problem is reduced to the study of controls (and corresponding trajectories) leading to the boundary of attainable sets. We discuss Pontryagin Maximum Principle, basic existence results, and apply these tools to concrete simple optimal control problems. Special sections are devoted to the general theory of linear time-optimal problems and linear-quadratic problems. (author)

[en] A method for finding a global optimum to the on-off minimum-time control problem with limited fuel usage is presented. Each control can take on only three possible values: maximum, zero, or minimum. The simplex method for linear systems naturally yields such a solution for the re-formulation presented herein because it always produces an extreme point solution to the linear program. Numerical examples for the benchmark linear flexible system are presented

[en] This paper describes an optimal scheduling for ice slurry systems for energy cost saving. The optimization technique applied in the study is the dynamic programming method, for which the state variable is the storage in the ice storage tank and the control variable is the state of chiller's on-off switching. Though the costs during charge period is included in optimization by taking the average cost of ice per hour for slurry making, the time horizon for the simulation is limited building cooling period because accurate charge rate from the ice maker into the ice storage tank cannot be estimated during the charge period. In the operating simulation after optimizing procedure, energy consumption and operating cost for the optimal control are calculated and compared with them for a conventional control with one case of cooling load profile