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2008
There are several optimal control problems that can be naturally cast in a set-theoretic framework and for which set-theoretic techniques provide efficient tools. Although by far non-exhaustive, this chapter considers several cases and discusses several solutions.
Stefano Miani, Franco Blanchini
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There are several optimal control problems that can be naturally cast in a set-theoretic framework and for which set-theoretic techniques provide efficient tools. Although by far non-exhaustive, this chapter considers several cases and discusses several solutions.
Stefano Miani, Franco Blanchini
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1973
Recently, a number of general methods for obtaining necessary conditions for optimality have been derived (see [2],[3],[5]). In [4], the author has given a general method, starting from the following basic problem: BP(S,f): “Given a set S in Rn and a function f: Rn →R1 , determine xeS such that f(x) is maximal”.
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Recently, a number of general methods for obtaining necessary conditions for optimality have been derived (see [2],[3],[5]). In [4], the author has given a general method, starting from the following basic problem: BP(S,f): “Given a set S in Rn and a function f: Rn →R1 , determine xeS such that f(x) is maximal”.
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2007
The most important job of industrial robots is moving between two points rest-to-rest. Minimum time control is what we need to increase industrial robots productivity. The objective of time-optimal control is to transfer the end-effector of a robot from an initial position to a desired destination in minimum time.
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The most important job of industrial robots is moving between two points rest-to-rest. Minimum time control is what we need to increase industrial robots productivity. The objective of time-optimal control is to transfer the end-effector of a robot from an initial position to a desired destination in minimum time.
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1998
Abstract This chapter gives a self‐contained introduction to optimal control of stochastic differential equations. We derive the Hamilton‐Jacobi‐Bellman equation as well as a verification theorem. The general theory is then applied to optimal consumption and investment problems.
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Abstract This chapter gives a self‐contained introduction to optimal control of stochastic differential equations. We derive the Hamilton‐Jacobi‐Bellman equation as well as a verification theorem. The general theory is then applied to optimal consumption and investment problems.
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