Abstract: Abstract. In this paper, we present a reduced-order model-based reinforcement learning method, leveraging the iterative linear quadratic regulator (ILQR) algorithm for the optimal control of nonlinear partial differential ...

Abstract: This paper presents a strategy for stochastic control of small aerial vehicles under uncertainty using graphbased methods. In planning with graphbased methods, such as the probabilistic roadmap method (PRM) in state space ...

Abstract: Abstract. This paper considers the problem of system identification for linear time-varying systems. We propose a new system realization approach that uses an “information-state” as the state vector, where the information-state ...

Abstract: In this paper, we consider the optimal control of material microstructures. Such material microstructures are modeled by the so-called phase-field model. We study the underlying physical structure of the model and propose ...