"2"^^ . "232419" . . . "Bologna" . "Stochastic maximum principle" . . "21230" . "Milano" . . "2011-08-28+02:00"^^ . "Stochastic maximum principle"@en . . "Stochastic maximum principle" . . "\u0160techa, Jan" . "[589F33CDC22E]" . . . . "Stochastic maximum principle"@en . "Proceedings of the 18th IFAC World Congress, 2011" . . . . . "The Pontrjagin maximum principle solves the problem of optimal control of a continuous deterministic system. The discrete maximum principle solves the problem of optima control of a discrete-time deterministic system. The maximum principle changes the problem of optimal control to a two point boundary value problem which can be completely solved only in special tasks. It was probably the reason that the maximum principle is not in favor this time. Optimal control of stochastic systems or even systems with probabilistic parameters is usually derived using stochastic dynamic programming. In the paper an alternative approach based on a stochastic modification of the maximum principle is presented, both for continuous and discrete-time systems. Cautious and certainty equivalent optimal control strategies are then derived using this method and the results are consistent with those achieved by stochastic dynamic programming." . "IFAC" . . "7"^^ . "Optimal control theory; Stochastic optimal control problems; Maximum principle; LQG control; ARX model; Lyapunov and Riccati equations"@en . "The Pontrjagin maximum principle solves the problem of optimal control of a continuous deterministic system. The discrete maximum principle solves the problem of optima control of a discrete-time deterministic system. The maximum principle changes the problem of optimal control to a two point boundary value problem which can be completely solved only in special tasks. It was probably the reason that the maximum principle is not in favor this time. Optimal control of stochastic systems or even systems with probabilistic parameters is usually derived using stochastic dynamic programming. In the paper an alternative approach based on a stochastic modification of the maximum principle is presented, both for continuous and discrete-time systems. Cautious and certainty equivalent optimal control strategies are then derived using this method and the results are consistent with those achieved by stochastic dynamic programming."@en . "RIV/68407700:21230/11:00182340" . . . . . . "978-3-902661-93-7" . . "2"^^ . . "RIV/68407700:21230/11:00182340!RIV12-MSM-21230___" . "P(GA102/08/0442), P(LA09012), S" . "Rathousk\u00FD, Jan" . . .