Robust and Optimal Control

By Mi Ching Tsai, D. Gu. 2014

3,181 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Control and Systems Engineering

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Citations in the corpus

Model-Based Reinforcement Learning via Meta-Policy Optimization (2018, background): “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.”
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles (2016, background): “Robust control is a branch of control theory which formally studies development of robust policies [15, 16].”
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms (2019, background): “These restrictions are relaxed in Iterative LQR [117] and Differential Dynamic Programming [376, 429], in which nonlinear dynamics and nonquadratic costs can be approximated as locally linear and locally quadratic, respectively.”
A comparison of model reduction techniques from structural dynamics, numerical mathematics and systems and control (2013, background): “[5, 46]) that for linear systems as in (37) the controllability and observability functions in (39) and (43) can be written as the quadratic forms”
Controller Synthesis for String Stability of Vehicle Platoons (2014, methodology): “Inspired by [12], the structure of the controller of each vehicle is chosen according to where K fb ( s ) represents the feedback control law; K ﬀ ,j ( s ) , j = 1 , 2 , . . . , k , represents the feedforward controllers, and ) is the output of the controller K ( s ) .”
A Survey of Linear Parameter-Varying Control Applications Validated by Experiments or High-Fidelity Simulations (2015, neutral): “In an LFR, each LPV parameter is assumed to have only one repetition, which is not uncommon, since the minimum number of required repetitions of each parameter for affine dependence is given by rank(S i ) [64].”
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].”
New results on H∞ dynamic output feedback control for Markovian jump systems with time‐varying delay and defective mode information (2014, methodology): “Invoking the results from robust control theory [22], another powerful tool to deal with time-delay is the input–output (IO) approach, which mainly relies on the scaled small-gain (SSG) theorem [23–25].”
Colour of turbulence (2016, background): “(5.6) Here, ‖ · ‖2 is the standard energy norm and the largest amplification over temporal frequencies determines the H∞ norm (Zhou et al. 1996), supω σmax(Tvw(k, ω)).”
A Game Theoretic Framework for Model Based Reinforcement Learning (2020, result): “Algorithms inspired by robust or risk-averse control [48, 49, 50] explicitly improve against the adversarial choice in the ensemble, consistent with the Stackelberg setting.”
AMB Vibration Control for Structural Resonance of Double-Gimbal Control Moment Gyro With High-Speed Magnetically Suspended Rotor (2013, methodology): “ω∈ [0,∞] the structured singular value is to satisfy the condition [37]”
Control: A perspective (2014, methodology): “H∞ control developed into a standard design method with books [171,266,604,702,701,383] and Matlab toolboxes.”
Tracking-Learning-Detection (2012, background): “[57], [58], the state vector ~x converges to zero if both”
Vibration suppression of structures using tuned mass damper technology: A state-of-the-art review (2021, methodology): “The modification of TMD system illustrated in Figure 1(d) was named as the MDOF-TMD by Zuo and Nayfeh (2004), in which the attached TMD system contains both displacement and rotary degrees of freedom.”
Structured H∞‐control of infinite‐dimensional systems (2017, methodology): “In that case hypothesis (iv) has only to be checked for G+ (and K), which reduces to standard finitedimensional tests like the Hautus test [24].”