First-Order Active Disturbance Rejection-Virtual Reference Feedback Tuning Control of Tower Crane Systems

Roman, Raul‐Cristian; Precup, Radu; Petriu, Emil M.; David, Radu-Codruţ; Hedrea, Elena‐Lorena; Szedlak-Stînean, Alexandra-Iulia

doi:10.1109/icstcc50638.2020.9259632

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…The informational structure including the connection between the ΤC system and the personal computer through an interface is illustrated in Figure 1. (Roman et al, 2019a;Roman et al, 2019b;Roman et al, 2020;Hedrea et al, 2021;Precup et al, 2021b;Roman et al, 2021;Precup et al, 2022a;Precup et al, 2022b;Precup et al, 2022c;Precup et al, 2022d;Roman et al, 2022a) The mathematical model of the ΤCS is fully detailed in (Precup et al, 2021b;Roman et al, 2021;Precup et al, 2022a). If the time argument is omitted in order to simplify things, the state-space model of the ΤC system is derived from Inteco (2006) and expressed as (Precup et al, 2021b;Roman et al, 2021;Precup et al, 2022a)…”

Section: The Tower Crane Systemmentioning

confidence: 99%

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Fictitious Reference Iterative Tuning of Discrete-Time Model-Free Control for Tower Crane Systems

ROMAN¹,

PRECUP²,

PETRIU³

et al. 2023

SIC

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The purpose of this paper is to propose a novel controller that is based on a combination of two data-driven algorithms, namely the Fictitious Reference Iterative Tuning (FRIT) algorithm and the Model-Free Adaptive Control (MFC) algorithm while considering a particular form of MFC, that is the intelligent proportional-integral-derivative (iPID) controller. The main advantage of this combination is that the FRIT algorithm optimally tunes the parameters of the iPID controller by solving an optimization problem based on a metaheuristic African Vultures Optimization Algorithm (AVOA). The novel controller, referred to as the FRIT-iPID controller, is validated experimentally on a three-degree-of-freedom tower crane system laboratory equipment in the context of controlling the cart position, the arm angular position and the payload position for this system.

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Section: The Tower Crane Systemmentioning

confidence: 99%

“…Figure1. The informational structure of ΤCS laboratory equipment(Roman et al, 2019a;Roman et al, 2019b;Roman et al, 2020;Hedrea et al, 2021;Precup et al, 2021b;Roman et al, 2021;Precup et al, 2022a;Precup et al, 2022b;Precup et al, 2022c;Precup et al, 2022d;Roman et al, 2022a) …”

mentioning

confidence: 99%

Fictitious Reference Iterative Tuning of Discrete-Time Model-Free Control for Tower Crane Systems

ROMAN¹,

PRECUP²,

PETRIU³

et al. 2023

SIC

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“…After its initial formulation, the ADRC algorithm was continuously improved and validated on numerous processes with stability guarantee including rotary DC motor by linear ADRC using anti-windup strategy and Hurwitz polynomials [12], ball and beam systems control using Routh's stability criterion [13], noncircular turning processes using vector margin variation rate [14], electronic gearboxes with ADRC parameters optimally tuned via PSO algorithm, control of all axes of CNC machines [15], tower crane systems (TCS) control by fuzzy ADRC with parameters tuned via virtual reference feedback tuning [16], TCS using virtual reference feedback tuning combined with ADRC [17], twin rotor aerodynamic systems by ADRC mixed with virtual reference feedback tuning [18], data-driven control containing an estimator similar to ADRC applied to shape memory alloys processes [19], also subjected to ADRC [20].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Data-Driven Active Disturbance Rejection Sliding Mode Control with Tower Crane Systems Validation

ROMAN,

PRECUP,

PETRIU

2024

ROMJIST

Self Cite

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This paper proposes a combination of a data-driven algorithm represented by the second-order continuous-time Active Disturbance Rejection Control (ADRC) and a Sliding Mode Control (SMC) algorithm. The purpose of this hybrid controller referred to as ADRC-SMC is to improve the overall control-loop system performance while guaranteeing its stability. This will be done through clear, simple, and transparent steps of controller design in a novel real formulation focused on practical implementation. The parameters of the novel second-order continuous-time ADRC-SMC algorithm are optimally tuned using a metaheuristic slime mould algorithm. The purpose of obtaining the parameters of the ADRC-SMC algorithms in this model-based manner is to reduce the heuristics and further ensure a fair performance comparison of the ADRC-SMC algorithm with that of the popular ADRC algorithm. The data-driven second-order continuous-time ADRC and ADRC-SMC algorithms are validated experimentally validated on tower crane laboratory equipment.

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“…In retrospect, a contemporary hybrid approach known as MFAC-PDTSFC was introduced by Roman et al 28 in the year 2019 for the handling of a MIMO nonlinear tower crane system through the incorporation of model-free adaptive control (MFAC) to the proportional-derivative Takagi–Sugeno fuzzy controller (PDTSFC). They then proceeded with the development of first-order active disturbance rejection-virtual reference feedback tuning (ADRV-VRFT) in the ensuing year 29 ; as well as the hybrid active disturbance rejection control-fuzzy controller in the year 2021, 30 targeting effectual operation of three-degree-of-freedom tower crane systems by implementing fine-tuning of the grey wolf optimizer (GWO). Whilst a MIMO crane system was applied towards these studies, the investigated model has been especially designed in parallel as a single-input–single-output (SISO) system comprising three specified domains of cart, arm angular, and payload.…”

Section: Introductionmentioning

confidence: 99%

An improved marine predators algorithm tuned data-driven multiple-node hormone regulation neuroendocrine-PID controller for multi-input–multi-output gantry crane system

Tumari

Ahmad

Suid

et al. 2023

Journal of Low Frequency Noise, Vibration and Active Control

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Conventionally, researchers have favored the model-based control scheme for controlling gantry crane systems. However, this method necessitates a substantial investment of time and resources in order to develop an accurate mathematical model of the complex crane system. Recognizing this challenge, the current paper introduces a novel data-driven control scheme that relies exclusively on input and output data. Undertaking a couple of modifications to the conventional marine predators algorithm (MPA), random average marine predators algorithm (RAMPA) with tunable adaptive coefficient to control the step size ( CF) has been proposed in this paper as an enhanced alternative towards fine-tuning data-driven multiple-node hormone regulation neuroendocrine-PID (MnHR-NEPID) controller parameters for the multi-input–multi-output (MIMO) gantry crane system. First modification involved a random average location calculation within the algorithm’s updating mechanism to solve the local optima issue. The second modification then introduced tunable CF that enhanced search capacity by enabling users’ resilience towards attaining an offsetting level of exploration and exploitation phases. Effectiveness of the proposed method is evaluated based on the convergence curve and statistical analysis of the fitness function, the total norms of error and input, Wilcoxon’s rank test, time response analysis, and robustness analysis under the influence of external disturbance. Comparative findings alongside other existing metaheuristic-based algorithms confirmed excellence of the proposed method through its superior performance against the conventional MPA, particle swarm optimization (PSO), grey wolf optimizer (GWO), moth-flame optimization (MFO), multi-verse optimizer (MVO), sine-cosine algorithm (SCA), salp-swarm algorithm (SSA), slime mould algorithm (SMA), flow direction algorithm (FDA), and the formally published adaptive safe experimentation dynamics (ASED)-based methods.

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First-Order Active Disturbance Rejection-Virtual Reference Feedback Tuning Control of Tower Crane Systems

Cited by 6 publications

References 24 publications

Fictitious Reference Iterative Tuning of Discrete-Time Model-Free Control for Tower Crane Systems

Fictitious Reference Iterative Tuning of Discrete-Time Model-Free Control for Tower Crane Systems

Hybrid Data-Driven Active Disturbance Rejection Sliding Mode Control with Tower Crane Systems Validation

An improved marine predators algorithm tuned data-driven multiple-node hormone regulation neuroendocrine-PID controller for multi-input–multi-output gantry crane system

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