Model-Free iPD Control Design for a Complex Nonlinear Automotive System

Baciu, Andrei; Lazăr, Corneliu

doi:10.1109/icstcc50638.2020.9259724

Cited by 10 publications

(3 citation statements)

References 9 publications

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“…1, we can interpret −𝛼 − 𝐹 ̂(𝑡) , 𝛼 − 𝑝𝑦 (𝑡), and 𝐶(𝜀(𝑡)) as corresponding to the disturbance observer, feedforward control, and feedback control, respectively. That is, we can consider that MFC is an extended form of the classical PID controller [20].…”

Section: ) Mfc Control Based On An Ultra-local Modelmentioning

confidence: 99%

“…There are many studies [4], including virtual reference feedback tuning (VRFT) [5][6][7], fictitious reference iterative tuning (FRIT) [8,9], model-free adaptive control based on dynamiclinearization techniques (MFAC-DLT) [10], model-free control based on an ultra-local model (MFC-ULM) [11,12], active disturbance rejection control (ADRC) [13], and adaptive fuzzy control (AFC) [14]. MFAC-DLT, MFC-ULM, and AFC can be applied to nonlinear systems and have been widely studied [10][11][12][13][14][15][16][17][18][19][20]. Additionally, control system design methods that do not use system models to be controlled are being applied to industrial systems, such as process systems and automotive systems [21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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Non-Iterative Data-Driven Tuning of Model-Free Control Based on an Ultra-Local Model

Yahagi

Kajiwara

2022

IEEE Access

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In this paper, we present a data-driven tuning method for model-free control based on an ultra-local model (MFC-ULM), which is also called intelligent proportional-integral-derivative control. In industries, the control design must be easy, and it is important that the control law can be applied to nonlinear systems. The MFC-ULM has most of these features. However, in practice, trial-and-error tuning of MFC-ULM design parameters is necessary. To address this problem, we adopt a data-driven tuning approach. In the proposed method, the MFC-ULM design parameters can be tuned from single-experiment data without requiring system identification, and optimal parameters for the MFC-ULM are obtained using the least-squares method. Additionally, we adopt L2-norm regularization to avoid overlearning. The effectiveness of this method was examined using simulations of two nonlinear systems. The results revealed that the MFC-ULM design parameters can be obtained directly without knowing the characteristics of the controlled object.

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Section: ) Mfc Control Based On An Ultra-local Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non-Iterative Data-Driven Tuning of Model-Free Control Based on an Ultra-Local Model

Yahagi

Kajiwara

2022

IEEE Access

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“…Following [1], we are using here model-free control in the sense of [12,13] and, more precisely, intelligent proportional controllers. This setting, which has been successfully tested in many concrete situations, has already been illustrated via various questions about intelligent transportation systems (see, e.g., [4,7,17,30,31,36,40,41,42]).…”

Section: No Difficult Estimation Technique Implementing Any Control L...mentioning

confidence: 99%

Ramp metering: modeling, simulations and control issues

Join,

Abouaïssa,

Fliess

2021

Preprint

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The aim of ramp metering is to improve the highway traffic conditions by an appropriate regulation of the inflow from the on-ramps to the highway mainstream. Our presentation rests on several improvements: 1) Our simulation techniques do not need contrarily to other approaches any heuristic fundamental law. 2) There is no need of crucial time-varying quantities, like the critical density, which is most difficult to estimate correctly online. 3) Our feedback loop, which is stemming from model-free control, is easy to implement and shows an excellent robustness with respect to model mismatch. Several computer experiments are displayed and discussed.

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