Robust and optimal control

By J. Doyle. 1995

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Citations in the corpus

Model-Based Reinforcement Learning via Meta-Policy Optimization (2018, methodology): “ophic failures. This problem is known in the literature as model-bias [6]. Previous work has tried to alleviate model-bias by characterizing the uncertainty of the models and learning a robust policy [6,7,8,9,10], often using ensembles to represent the posterior. This paper also uses ensembles, but ”
Performance bounds for linear stochastic control (2009, background): “with variable P. The optimal point is P = P# , and the optimal value of this problem is J# [8,1,20,3,7, 29 ].”
Balanced truncation model reduction of second-order systems (2008, background): “Furthermore, since these matrices are the products of two symmetric, positive semidefinite matrices, they are diagonalizable and have real non-negative eigenvalues 22 , p.”
Nonreflecting Boundary Conditions for the Time-Dependent Wave Equation (2002, background): “order reduction in electrical engineering and control theory [40, 42 ].”
On the phases of a complex matrix (2019, background): “The rank of I + AB plays an important role in the field of systems and control [24].”
Model reduction of large-scale systems by least squares (2006, unknown): “For details, see [5,42].”
Learning omnidirectional path following using dimensionality reduction (2007, background): “For a general overview of robust cont rol, see [ 31 ] and [9].”
Transfer functions of regular linear systems. Part II: The system operator and the lax-phillips semigroup (2002, background): “A point z 2 C where dim Ker S(z) is larger than its minimal value over all s2 C is called an invariant zero of , see for example Zhou et al. [ 48 ].”