Modified Model-Free Adaptive Control using Compact-Form Dynamic Linearization Technique

Pham, Hoang Anh; Söffker, Dirk

doi:10.1016/j.ifacol.2020.12.2248

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Given the aforementioned assumptions, it is possible to convert the SISO system into a concise form using dynamic linearization, often referred to as the Compact Form Dynamic Linearization (CFDL) model 21 :…”

Section: The Mathematical Form Of Basic-mfacmentioning

confidence: 99%

Enhancing the Tracking Performance of Wind Turbine Blade Pitch Angle Control via Model-Free Adaptive Control Algorithm Utilizing Input-Output Differential

Zhou,

Wang,

Jiang

et al. 2024

Preprint

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In the context of wind energy systems, maintaining optimal power output in wind turbines when wind speeds exceed rated values necessitates precise regulation of blade pitch through the pitch control system. However, challenges in accurately modeling nonlinear control systems and enhancing control responsiveness arise due to factors such as actuator constraints, unmodeled dynamics, and random wind speed fluctuations. This paper proposes an improved model-free control, termed Input-Output Model-Free Adaptive Control (IO-MFAC). It enhances the system's adaptability to stochastic wind and improves tracking capabilities by adaptively adjusting the input difference based on the output difference through the new cost function. Moreover, to demonstrate the stability of IO-MFAC, its monotonic convergence is theoretically confirmed. Simulations conducted on the FAST simulator and hardware-in-the-loop experiments demonstrate that IO-MFAC outperforms basic MFAC under constant wind and turbulent conditions, exhibiting superior dynamic tracking performance, control effectiveness, and practical utility.

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Section: The Mathematical Form Of Basic-mfacmentioning

confidence: 99%

Enhancing the Tracking Performance of Wind Turbine Blade Pitch Angle Control via Model-Free Adaptive Control Algorithm Utilizing Input-Output Differential

Zhou,

Wang,

Jiang

et al. 2024

Preprint

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Parameters setting of a Data-Driven Model-Free Adaptive Controller for a coupled Two-Tank System

Baciu

Lazăr

2022

2022 26th International Conference on System Theory, Control and Computing (ICSTCC)

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Decoupled Model-Free Adaptive Control with Prediction Features Experimentally Applied to a Three-Tank System Following Time-Varying Trajectories

Salighe,

Trivedi,

Bakhshande

et al. 2024

Automation

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In this paper, the performance of three model-free control approaches on a multi-input, multi-output (MIMO) nonlinear system with constant and time-varying references is compared. The first control algorithm is model-free adaptive control (MFAC). The second is a modified version of MFAC (MMFAC) designed to handle delays in the system by incorporating the output error difference (over two sample time steps) in the control input. The third approach, model-free adaptive predictive control (MFAPC) with a one-step-ahead forecast of the system input, is obtained by using predictions of the outputs based on the data-based linear model. The experimental device used is an MIMO three-tank system (3TS) assumed to be an interconnected system with multiple coupled single-input, single-output (SISO) subsystems with unmeasurable couplings. The novelty of this contribution is that each coupled SISO partition is assumed to be controlled independently using a decoupled control algorithm, leading to fewer control parameters compared to a centralized MIMO controller. Additionally, both parameter tuning for each controller and performance evaluation are conducted using an evaluation criterion considering energy consumption and accumulated tracking error. The results demonstrate that almost all the proposed model-free controllers effectively control an MIMO system by controlling its SISO subsystems individually. Moreover, the predictive features in the decoupled MFAPC contribute to more accurate tracking of time-varying references. The utilization of tracking error differences helps in reducing energy consumption.

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Modified Model-Free Adaptive Control using Compact-Form Dynamic Linearization Technique

Cited by 3 publications

References 16 publications

Enhancing the Tracking Performance of Wind Turbine Blade Pitch Angle Control via Model-Free Adaptive Control Algorithm Utilizing Input-Output Differential

Enhancing the Tracking Performance of Wind Turbine Blade Pitch Angle Control via Model-Free Adaptive Control Algorithm Utilizing Input-Output Differential

Parameters setting of a Data-Driven Model-Free Adaptive Controller for a coupled Two-Tank System

Decoupled Model-Free Adaptive Control with Prediction Features Experimentally Applied to a Three-Tank System Following Time-Varying Trajectories

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