Optimal Control and Estimation

By P. Hippe, J. Deutscher. 2009

1,236 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Numerical Analysis, Mathematical Optimization, Robotics & Control

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Citations in the corpus

Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images (2015, methodology): “, st can be inferred from zt alone, and f lat is differentiable, the cost-minimizing controls can be computed from J(z1:T ,u1:T ) via SOC algorithms [12].”
The Mirage of Action-Dependent Baselines in Reinforcement Learning (2018, background): “Linear-quadratic-Gaussian (LQG) systems (Stengel, 1986) are a family of widely studied continuous control problems with closed-form expressions for optimal controls, quadratic value functions, and Gaussian state marginals.”
Reward-estimation variance elimination in sequential decision processes (2018, methodology): “In this section we demonstrate performance of VE on a controlled diffusion model [6] – a simple linear quadratic Gaussian (LQG) model [15].”
Optimal control as a graphical model inference problem (2009, positive): “The best known example is the linear quadratic control problem, which is mathematically equivalent to an inference problem and can be solved as a Kalman smoothing problem (Stengel 1994).”
Approximate Inference and Stochastic Optimal Control (2010, background): “However we note that attempts to relate stochastic optimal control to inference have along history, in part motivated by the exact duality for the linear-quadratic-gaussian (LQG) case discovered by Kalmann [27].”
SE-Sync: A certifiably correct algorithm for synchronization over the special Euclidean group (2016, background): “) have long been a feature of algorithms for planning (Russel and Norvig, 2010) and control (Åström and Murray, 2008; Stengel, 1994), to date it appears that the development of practical algorithms with clearly delineated performance guarantees for robotic perception has to a great extent remained a”
Control of quantum phenomena: past, present and future (2009, background): “d increasing their dimensions [191]. 4.2. Optimality of control solutions Satisfaction of the condition (21) for a critical point is a necessary but not suﬃcient condition for optimality of a control [166,223]. For Mayer-type cost functionals, a suﬃcient condition for optimality is negative semideﬁn”
Applied Stochastic Differential Equations (2019, neutral): “Finally, one possible extension is to consider stochastic control problems (e.g., Maybeck, 1982b; Stengel, 1994; Øksendal, 2003; Øksendal and Sulem, 2007) related to SDEs.”
Graphical Model Inference in Optimal Control of Stochastic Multi-Agent Systems (2008, background): “(10) This result is well known (Stengel, 1993).”
Policy gradients in linearly-solvable MDPs (2010, background): “ (xu) =  () + 12u  (x)u Such problems have been studied extensively [13].”
Bayesian Filtering and Smoothing (2013, methodology): “Kalman, has also made several contributions (Kalman, 1960a) to the theory of linear quadratic Gaussian (LQG) regulators, which are fundamental tools of stochastic optimal control (Stengel, 1994; Maybeck, 1982a).”
A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition (2014, methodology): “ would be completely determined by the spectrum of the evolution operator. Furthermore, this could enable the simple yet eective algorithms designed for linear systems, for example controller design [7,8] or stability analysis [1,9], to be applied to nonlinear systems. Mathematically, the evolution ”
Information-Theoretic Bounded Rationality (2015, methodology): “Due to this, in practice we either make severe domain-speciﬁc simpliﬁ-cations , as in linear-quadratic-Gaussian control problems [Stengel, 1994]; or we approximate the “gold standard” prescribed by SEU theory, exempliﬁed by the reinforcement learning algorithms based on stochastic approximations…”
Decentralized Environmental Modeling by Mobile Sensor Networks (2008, background): “The optimal estimator for this system is a KF [4], [41].”