An Introduction for Scientists and Engineers SECOND EDITION

By Karl Johan, Richard M. Murray. 2021

Subfields: Numerical Methods & Scientific Computing

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Citations in the corpus

Continual Lifelong Learning with Neural Networks: A Review (2018, background): “The feedback controller directly affects synaptic strength on the basis of the observed neural activity and must be fast in relation to the timescale of the unstable system (Aström & Murray, 2010).”
The temporal paradox of Hebbian learning and homeostatic plasticity (2017, neutral): “ble system exhibits run-away activity [66].”
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control (2018, background): “For continuous MDPs, computing the optimal value function exactly is not tractable except in a few well known cases like the LQR [7].”
Bistable vibration energy harvesters: A review (2013, background): “A critique should be made because, as the system is nonlinear, the principle of superposition—an underlying assumption while taking the integral of the response— might not be respected (Åström and Murray, 2008).”
Global connectivity of prefrontal cortex predicts cognitive control and intelligence (2012, background): “…reflect a particular architecture for a flexible neural system underlying human intelligence: Engineers have characterized flexible, adaptive control in terms of the ability for a system to access (both monitor and influence) multiple sources of task-relevant information (Åström and Murray, 2008).”
Self-oscillation (2011, background): “For a more thorough mathematical treatment, see [203].”
A unified approach to local trajectory planning and control for autonomous driving along a reference path (2014, methodology): “For the trajectory tracking control, we employ a typical two degree of freedom control framework in a receding horizon manner, as referred in [12].”
Bayesian Optimal Control of Smoothly Parameterized Systems (2015, methodology): “The problem is taken from Section 7.8.1 of the book by Hellerstein et al. (2004) (this example is also used in Section 3.4 of the book by Astr¨om and Murray (2008)).”
Robustness of Control Barrier Functions for Safety Critical Control (2016, background): “The lead and following vehicles are modeled as point-masses moving on a straight road with uncertain slope or grade (Ioannou and Chien (1993), Aström and Murray (2010)).”
Stabilization of projection-based reduced order models for linear time-invariant systems via optimization-based eigenvalue reassignment (2014, methodology): “R such that the corresponding solution satisfies xð0Þ ¼ x 0 and xðTÞ ¼ x f [6] [7].”
Predictive-State Decoders: Encoding the Future into Recurrent Networks (2017, unknown): “In the case of linear systems, this k is similar to the rank of the observability matrix [6].”
Self-Driving Cars: A Survey (2019, methodology): “es is the Proportional Integral Derivative (PID). It involves specifying a desired hardware input and an error measure that accounts for how far the hardware output is from the desired hardware input [AST08]. In possession of this information, a PID control method computes the hardware input, which ”