Real-Time Optimization by Extremum-Seeking Control: Ariyur/Extremum Seeking

By Kartik B. Ariyur, Miroslav Krstic. 2003

1,256 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/5f81d45b4085e1f36065784a27e79276750471ee

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In Control and Systems Engineering

Citations in the corpus

MPPT of photovoltaic systems using extremum - seeking control (2006, background): “The foundations of extremum-seeking control can be found in the early 1920s in the work of Leblanc on the search of the resonance peak of an electromechanical system [10].”
Adaptive separation control of a laminar boundary layer using online dynamic mode decomposition (2020, background): “Common model-free closed-loop control approaches include extremum and slopeseeking feedback control (Ariyur & Krstic 2003), employing gradient-descent algorithms that seek local extrema based on the response of the system output to slow input sinusoidal perturbations.”
Lyapunov-based switched extremum seeking for photovoltaic power maximization (2013, methodology): “Before embarking on a detailed discussion of this method, we give an intuitive explanation of how extremum seeking works, which can also be found in Krstic and Wang (2000) and Ariyur and Krstić (2003), but is presented here for completeness.”
Technical communique: On the choice of dither in extremum seeking systems: A case study (2008, neutral): “This has spurred a renewed interest in this research area (Ariyur and Krstić, 2003; Popović et al., 2003; Guay and Zhang, 2003; Guay et al., 2004; Peterson and Stefanopoulou, 2004) that has lead to numerous practical implementations of the scheme.”
Analysis and Comparison of Extremum Seeking Control Techniques (2007, background): “Reference [2] differentiated between ESC methods which require an external perturbation of the input variable (also known as perturb and observe) [3], [4], and ESC methods which do not require an external perturbation [5], [6], [7] and [8].”
Data-driven discovery of Koopman eigenfunctions for control (2017, background): “ork for nonlinear control via our KRONIC architecture. A wide range of data-driven and nonlinear control approaches exist in the literature, including model-free adaptive control[34], extremum-seeking[3], gainscheduling [55], feedback linearization [15], describing functions [68], sliding mode contr”
Nash Equilibrium Seeking in Noncooperative Games (2012, methodology): “When using the extremum seeking algorithm (9)–(10), the firms only need to measure the value of their own payoff functions, and .”
Reinforcement learning for control: Performance, stability, and deep approximators (2018, methodology): “The second type of online learning comprises policy gradient meth-ods, in which policy parameters are directly optimized from observed data, similarly to extremum seeking in control (Ariyur and Krstic, 2003) but more general.”
Mobile Sensor Network Control Using Mutual Information Methods and Particle Filters (2010, methodology): “First, the mobile sensor network model is defined, then the goal of searching for a target is cast using the information-theoretic concept of mutual information as a utility function to optimally search for a target.”