Essentials of Robust Control

By Xiao Zhou, J. Doyle. 1997

How researchers see this paper

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

1. Tom Everitt
2. Sergey Levine
3. Zhongkui Li
4. Wei Ren
5. Guanghui Wen
6. Z. Duan
7. Pieter Abbeel
8. Jan Leike

Citations in the corpus

• Reinforcement learning in robotics: A survey (2013, background): “This problem may be inevitable due to both uncertainty about true system dynamics, the nonstationarity of system dynamics6 and the inability of any model in our class to be perfectly accurate in its description of those dynamics, which have led to robust control theory (Zhou and Doyle, 1997).”
• AI Safety Gridworlds (2017, methodology): “A first and most direct approach to remedy this situation consists in adapting methods from the feedback and robust control literature (Whittle, 1996; Zhou and Doyle, 1997) to the reinforcement learning case (see e.g. Yun et al., 2014).”
• Controllability and observability covariance matrices for the analysis and order reduction of stable nonlinear systems (2003, background): “[16] Zhou, K.”
• Issues in active flow control: theory, control, simulation, and experiment (2004, methodology): “When the disturbances w are taken to be worst-case disturbances then the approach is referred to as H1 control design and the interested reader should consult [72,82,102] to learn more about the theory ofH1 control.”
• On designing improved controllers for AQM routers supporting TCP flows (2001, methodology): “Instead of optimal control methodologies, such as LQG/LTR, or [ 21 ] methods, we limited our attention to classical control elements.”
• Towards a theory of biological robustness (2007, methodology): “Robust control assumes uncertainties in a model and defines a method of applying stable control over the system such that proper control is guaranteed even if the model deviates from the real system due to modeling errors (Zhou and Doyle, 1997).”
• Consensus and Cooperation in Networked Multi-Agent Systems (2007, methodology): “This formalism lends itself to applications of tools from robust control theory [101].”
• Flow control: new challenges for a new Renaissance (2001, background): “The H1 control solution (Doyle et al. 1989) may be described as follows: a compensator which minimizes J in the presence of that disturbance which simultaneously maximizes J is given by K = ?”
• Secure Estimation and Control for Cyber-Physical Systems Under Adversarial Attacks (2012, background): “roblem formulation the number of failure modes can be very large and one cannot afford to generate and analyze a residual signal for each possible failure mode. In another area, namely robust control [8], one seeks to design control methods that are robust against disturbances in the model. In gener”
• Safe Control under Uncertainty (2015, background): “One of the most effective approaches in robust control under uncertainty is modeling the environment uncertainty as part of the dynamics of the system, and finding the optimal strategy for the worst case disturbance [22, 41, 44].”
• Control: A perspective (2014, methodology): “H∞ control developed into a standard design method with books [171,266,604,702,701,383] and Matlab toolboxes.”
• A high-precision, wide-bandwidth micromachined tunneling accelerometer (2001, background): “ation from the nominal model for the real physical system, can we design a controller so that the closed-loop system meets both robustness and performance specifications? Allowing both real and complex blocks in the uncertainty model leads the development of mixed synthesis [18].”