Model-free control

By Michel Fliess, Cédric Join. International Journal of Control, 2013

1,067 citations recorded by the source corpus.

‘Model-free control’and the corresponding ‘intelligent’ PID controllers (iPIDs), which already had many successful concrete applications, are presented here for the first time in an unified manner, where the new advances are taken into account. The basics of model-free control is now employing some old functional analysis and some elementary differential algebra. The estimation techniques become quite straightforward via a recent online parameter identification approach. The importance of iPIs and especially of iPs is deduced from the presence of friction. The strange industrial ubiquity of classic PIDs and the great difficulty for tuning them in complex situations is deduced, via an elementary sampling, from their connections with iPIDs. Several numerical simulations are presented which include some infinite-dimensional systems. They demonstrate not only the power of our intelligent controllers but also the great simplicity for tuning them.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/2efa356db7a6f866736b40b92dde41e58b6dc91b

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In Control and Systems Engineering

Citations in the corpus

From Noisy Data to Feedback Controllers: Nonconservative Design via a Matrix S-Lemma (2020, background): “f previous work is therefore not exhaustive, but we note that additional references can be found in the survey [4]. We mention contributions to data-driven optimal control [5]–[16], PID control [17], [18], predictive control [19]–[23], and nonlinear control [24]–[28]. Some of these techniques are it”
The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles? (2017, methodology): “4) Model-free Control: At last, controllers based on correcting the tracking error with no modeling of the system such as the popular Proportional-Integral-Derivative (PID) controller [28] or the recently introduced Model-free Control [29] can be used to control the vehicles.”
Data Informativity: A New Perspective on Data-Driven Analysis and Control (2019, neutral): “ that apply reinforcement learning [8]. Additional noteworthy data-driven control problems include predictive control [20]–[22], model reference control [23], [24] and (intelligent) PID control [25], [26]. For more references and classiﬁcations of data-driven control techniques, we refer to the surv”
Stability of active disturbance rejection control for uncertain systems: A Lyapunov perspective (2017, methodology): “in [25, 26] proposed a similar method, named model free control, to control uncertain plants, where the uncertain dynamic is computed by integration of the closed-loop system, instead of using an extended high-gain observer.”
Control: A perspective (2014, background): “The modelfree controller by Fliess [210] is another example which is related to the self-tuning regulator.”
Comparing filtered PI, PID and PIDDcontrol for the FOTD plants (2018, background): “Today, they may be denoted by different trademarks, such as “model-free control” MFC (underpinned by the “flatness” theory (Fliess and Join, 2013)), or “active-disturbance-rejection-control” ADRC (Gao, 2014), or interpreted as “well approved” approaches (Mercader and Banos, 2017).”
Model-Free Predictive Current Control of PMSM Drives Based on Extended State Observer Using Ultralocal Model (2021, neutral): “system is expressed as [24]”