Feedback Systems: An Introduction for Scientists and Engineers

By K. Åström, Richard M. Murray. 2008

353 citations recorded by the source corpus.

Subfields: Computational Mechanics, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Benedetto Piccoli
2. Sylvain Calinon
3. Shumo Cui
4. D. Work
5. Mauro Garavello

In Control and Systems Engineering

1. Benedetto Piccoli
2. Sylvain Calinon
3. D. Work
4. Aude Gemma Billard
5. Sonia Chernova
6. Seyed Reza Ahmadzadeh
7. Jan Peters
8. Mauro Garavello

Citations in the corpus

• Continual Lifelong Learning with Neural Networks: A Review (2018, neutral): “lasticity (see Fig. 1.a). 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 and Murray, 2010). For a critical review of the temporal aspects of Hebbian and homeostatic p”
• A tutorial on task-parameterized movement learning and retrieval (2015, methodology): “The tracking problem can be solved by different techniques, either exploiting tools from physics, dynamic programming or linear algebra [6, 10].”
• Model-free control (2013, neutral): “…especially by numerous fruitful applications, the consequences on the future development and teaching (see, e.g. , the excellent textbook by ˚Astr¨om & Murray (2008)) of control theory might be dramatic: • Questions on the structure and on the parameter identification of linear and…”
• Second-law-like inequalities with information and their interpretations (2014, neutral): “ntegrals of the sensitivity function, such as B, equals zero holds with great generality. In fact, it represents a well-known conservation principle in control theory known as Bode’s integral formula [38]. To determine I_ traj, we rst note that I_ traj = I_ v!y + I_ y!v. Since we already know I_ v!y”
• Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments (2017, background): “ controller is obtained by measuring the CAT Vehicle speed over a large enough time horizon. The controller determines a command velocity vcmd following a standard proportional integral control logic [64], where the deviation from the average speed is treated as the error signal in the PI controller”