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Optimal control, reinforcement learning and other optimization-based methods have been widelyapplied to control synthesis for real systems in physics, biology and robotics. However, in many complicate...
This paper describes how to efficiently solve a robust optimal control problem using recently developed primal-dual interior-point methods. Among potential applications are model predictive control. T...
Robust linear programming and optimal control     Linear programming  Convex optimization  Model-predictive control       font style='font-size:12px;'> 2015/8/11
We describe an efficient method for solving an optimal control problem that arises in robust model-predictive control. The problem is to design the input sequence that minimizes the peak tracking erro...
We consider the design of optimal controllers for a networked system (or a spatio-temporal system), where the dynamics of each subsystem may affect those of other subsystems with some propagation dela...
In this paper, we present an explicit state-space solution to the two-player decentralized optimal control problem. In this problem, there are two interconnected linear systems that seek to optimize a...
Contributions to the theory of optimal control     Contributions  the theory  optimal control       font style='font-size:12px;'> 2014/8/13
It is most flattering and gratifying for a nonpharmacologist, nonbiologist,nonchemist, and nonstatistician, who is at the same time a non-know-how kind of theoretician, to have his paper become a‘Cit...
The stochastic optimal control is an important research subject in structural engineering. Recently, a stochastic optimal nonlinear control method has been proposed based on the stochastic dynamical ...

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