Optimal stochastic control.
Optimal stochastic control of small dynamic systems described by random differential equations, noting selective bibliography on several subjects
Wonham, W. M.↗
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Optimal stochastic control.
Optimal stochastic control, discussing dynamic mathematical models described by differential equations
Wonham, W. M.↗
A new approach to stochastic control.
Optimizing stochastic control problem by addition of estimation-penalty functions to performance index, using invariant imbedding procedure
Melsa, J. L.↗
On the existence of optimal stochastic controls.
Existence of optimal stochastic controls
EXISTENCE THEOREM↗
A stochastic optimal control problem and its applications.
Stochastic optimal control problem solution by dynamic programming and relation to interplanetary guidance
INTERPLANETARY FLIGHT↗
Numerical methods for the solution of the degenerate nonlinear elliptic equations arising in optimal stochastic control theory.
Optimal control problems for Markov chains solved by iterative method, using nonlinear finite difference equations to approximate degenerate elliptic functions
Kleinman, A. J.↗
Function space approach to a class of stochastic optimal control problems
Linear time dependent stochastic optimal control with nonquadratic performance indices using function space approach
Luh, J. Y. S.↗
Numerical methods for the solution of the degenerate non-linear elliptic equations arising in optimal stochastic control theory.
Iterative method for optimal control problems for Markov chains, discussing degenerate nonlinear elliptic equations solution in optimal stochastic control theory
Kleinman, A.↗
On a function space approach to a class of linear stochastic optimal control systems
Function space approach to linear stochastic optimal control systems
Luh, J. Y. S.↗
An optimal stochastic control problem with observation cost
Linear system optimal stochastic control and observation strategies simultaneous determination with quadratic cost by dynamic programming
Cooper, C. A.↗
An optimal stochastic control problem with observation cost
Linear systems simultaneous optimal stochastic control and observation strategies, assuming quadratic observation cost
Cooper, C. A.↗
Digital program for solving the linear stochastic optimal control and estimation problem
A computer program is described which solves the linear stochastic optimal control and estimation (LSOCE) problem by using a time-domain formulation. The LSOCE problem is defined as that of designing controls for a linear time-invariant system which is disturbed by white noise in such a way as to minimize a performance index which is quadratic in state and control variables. The LSOCE problem and solution are outlined; brief descriptions are given of the solution algorithms, and complete descriptions of each subroutine, including usage information and digital listings, are provided. A test case is included, as well as information on the IBM 7090-7094 DCS time and storage requirements.
Geyser, L. C.↗
Low-thrust guidance - An optimal stochastic control problem
Space vehicle low thrust minimum terminal variance guidance problem reduced to stochastic bang-bang optimal control system
Ash, G. R.↗
Optimal stochastic control theory applied to interplanetary guidance
Optimal control of nonlinear dynamic system in presence of noise
NOISE REDUCTION↗
Control by Coin Flips: Mixed Strategy for Finite-Horizon Stochastic Optimal Control
No abstract available
Ono, Masahiro↗
Parallel and vector computation for stochastic optimal control applications
A general method for parallel and vector numerical solutions of stochastic dynamic programming problems is described for optimal control of general nonlinear, continuous time, multibody dynamical systems, perturbed by Poisson as well as Gaussian random white noise. Possible applications include lumped flight dynamics models for uncertain environments, such as large scale and background random atmospheric fluctuations. The numerical formulation is highly suitable for a vector multiprocessor or vectorizing supercomputer, and results exhibit high processor efficiency and numerical stability. Advanced computing techniques, data structures, and hardware help alleviate Bellman's curse of dimensionality in dynamic programming computations.
Hanson, F. B.↗
Supercomputer optimizations for stochastic optimal control applications
Supercomputer optimizations for a computational method of solving stochastic, multibody, dynamic programming problems are presented. The computational method is valid for a general class of optimal control problems that are nonlinear, multibody dynamical systems, perturbed by general Markov noise in continuous time, i.e., nonsmooth Gaussian as well as jump Poisson random white noise. Optimization techniques for vector multiprocessors or vectorizing supercomputers include advanced data structures, loop restructuring, loop collapsing, blocking, and compiler directives. These advanced computing techniques and superconducting hardware help alleviate Bellman's curse of dimensionality in dynamic programming computations, by permitting the solution of large multibody problems. Possible applications include lumped flight dynamics models for uncertain environments, such as large scale and background random aerospace fluctuations.
Chung, Siu-Leung↗
Towards sub-optimal stochastic control of partially observable stochastic systems
The paper deals with a class of multidimensional stochastic control problems with noisy data and bounded controls encountered in aerospace design. The emphasis is on suboptimal design, the optimality being taken in quadratic mean sense. To that effect the problem is viewed as a stochastic version of the Lurie problem known from nonlinear control theory. The main result is a separation theorem (involving a nonlinear Kalman-like filter) suitable for Lurie-type approximations. The theorem allows for discontinuous characteristics. As a byproduct the existence of strong solutions to a class of non-Lipschitzian stochastic differential equations in n dimensions is proved.
Ruzicka, G. J.↗