Dynamically Tuned PIDD2 Controller for Single-Link Flexible Manipulator

Agrawal, Komal; Negi, Richa; Singh, Nitin

doi:10.1007/978-981-32-9775-3_81

“…Its structure is depicted in Figure 3. It merges two controller forms as PIDND 2 N 2 , which allows system stability and amplification to reduce overshoots, minimizing system settling time and rise time [44]. The second controller is the fractional integrator to reduce the rise time and steady-state error in the system, where λ has a range of [0, 2].…”

Section: Controller Structurementioning

confidence: 99%

A New Load Frequency Control Technique for Hybrid Maritime Microgrids: Sophisticated Structure of Fractional-Order PIDA Controller

Almasoudi

¹

,

Magdy

²

,

Bakeer

³

et al. 2023

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This paper proposes an efficient load frequency control (LFC) technique based on a fractional-order proportional–integral–derivative–accelerator with a low-pass filter compensator (FOPIDA-LPF) controller, which can also be accurately referred to as the PIλDND2N2 controller. A trustworthy metaheuristic optimization algorithm, known as the gray wolf optimizer (GWO), is used to fine-tune the suggested PIλDND2N2 controller parameters. Moreover, the proposed PIλDND2N2 controller is designed for the LFC of a self-contained hybrid maritime microgrid system (HMμGS) containing solid oxide fuel cell energy units, a marine biodiesel generator, renewable energy sources (RESs), non-sensitive loads, and sensitive loads. The proposed controller enables the power system to deal with random variations in load and intermittent renewable energy sources. Comparisons with various controllers used in the literature demonstrate the excellence of the proposed PIλDND2N2 controller. Additionally, the proficiency of GWO optimization is checked against other powerful optimization techniques that have been extensively researched: particle swarm optimization and ant lion optimization. Finally, the simulation results performed by the MATLAB software prove the effectiveness and reliability of the suggested PIλDND2N2 controller built on the GWO under several contingencies of different load perturbations and random generation of RESs. The proposed controller can maintain stability within the system, while also greatly decreasing overshooting and minimizing the system’s settling time and rise time.

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“…Moreover, model parameters may need tuning, even if model identification has been made. Different methods for tuning FLM controller gains include the Ziegler-Nichols method (Mohamed et al, 2016;Agrawal et al, 2020), LMI approach (Mohamed et al, 2016), dynamic particle swarm optimization method (Agrawal et al, 2020), self-tuning method using the artificial neural network (Njeri et al, 2019), self-tuning method based on nonlinear autoregressive moving average with exogenous-input (NARMAX) model of the FLM (Pradhan and Subudhi, 2020), soft computing based tuning method (Singh and Ohri, 2018), and selftuning method based on generalized minimum variance . Compared to the standard Ziegler-Nichols tuning method, the recent self-tuning methods have shown superior performance in the control of FLMs (Agrawal et al, 2020).…”

Section: Control Of Flmsmentioning

confidence: 99%

“…They highlighted that the fractional order fuzzy PD controller performed better than other controllers. Agrawal et al (2020) compared a modified optimal PIDD 2 (proportional, integral, derivative, and second-order derivative) controller with the PID controller for controlling the position and trajectory of the single-link flexible manipulator with minimum tip oscillation. They reported the superior performance of the PIDD 2 controller through simulation.…”

Section: Model-free Control Techniquesmentioning

confidence: 99%

“…,Moh. Khairudin (2008),Abe (2009),Celentano and Coppola (2011), Ouyang et al (2017), Ouyang et al (2017), Reddy and Jacob (2017), Meng et al (2018), Sun et al (2018), Badfar and Abdollahi (2019), Lochan et al (2019), Qiu et al (2019), Qiu and Zhang (2019), Saeed et al (2019), Pradhan and Subudhi (2020), and Boucetta et al (2020) used the Lagrange formulation for obtaining the mathematical model of the FLMs with AMM.Other recent studies using the Lagrange method for deriving equations of motion of the FLMs includes, but are not limited to,Singh and Rajendran (2016),Si et al (2017),Dong et al (2019),Mehria and Foruzantabarb (2019),Singla and Singh (2019), andAgrawal et al (2020).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Review on Modeling and Control of Flexible Link Manipulators

Subedi¹,

Tyapin²,

Hovland³

2020

MIC

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This paper presents a review of dynamic modeling techniques and various control schemes to control flexible link manipulators (FLMs) that were studied in recent literature. The advantages and complexities associated with the FLMs are discussed briefly. A survey of the reported studies is carried out based on the method used for modeling link flexibility and obtaining equations of motion of the FLMs. The control techniques are reviewed by classifying them into two main categories: model-based and model-free control schemes. The merits and limitations of different modeling and control methods are highlighted.

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Revolutionizing Vehicle Cruise Control: An Elite Opposition-Based Pattern Search Mechanism Augmented INFO Algorithm for Enhanced Controller Design

Ekinci

¹

,

İzci

²

,

Abualigah

³

et al. 2023

Int J Comput Intell Syst

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This paper presents a groundbreaking approach to enhance the performance of a vehicle cruise control system—a crucial aspect of road safety. The work offers two key contributions. Firstly, a state-of-the-art metaheuristic algorithm is proposed by augmenting the performance of the weighted mean of vectors (INFO) algorithm using pattern search and elite opposition-based learning mechanisms. The resulting boosted INFO (b-INFO) algorithm surpasses the original INFO, marine predators, and gravitational search algorithms in terms of performance on benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions. Secondly, a novel proportional, fractional order integral, derivative plus double derivative with filter ($$P{I}^{\lambda }DN{D}^{2}{N}^{2}$$ P I λ D N D 2 N 2 ) controller is proposed as a more efficient control structure for vehicle cruise control systems. An objective function is utilized to determine the optimal values for the controller parameters, and the proposed method's performance is compared against a range of recent approaches. Results demonstrate that the b-INFO algorithm-based $$P{I}^{\lambda }DN{D}^{2}{N}^{2}$$ P I λ D N D 2 N 2 controller is the most efficient and superior method for controlling a vehicle cruise control system. Moreover, this work represents the first report of a $$P{I}^{\lambda }DN{D}^{2}{N}^{2}$$ P I λ D N D 2 N 2 controller’s implementation for vehicle cruise control systems, underscoring the novelty and significance of this research. The proposed method's exceptional ability is further confirmed by comparisons with the genetic algorithm, ant lion optimizer, atom search optimizer, arithmetic optimization algorithm, slime mold algorithm, Lévy flight distribution algorithm, manta ray foraging optimization, and hunger games search-based proportional–integral–derivative (PID), along with Harris hawks optimization-based PID and fractional order PID controllers. This work marks a remarkable milestone toward safer and more efficient vehicle cruise control systems.

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Dynamically Tuned PIDD2 Controller for Single-Link Flexible Manipulator

Cited by 5 publications

References 16 publications

A New Load Frequency Control Technique for Hybrid Maritime Microgrids: Sophisticated Structure of Fractional-Order PIDA Controller

A New Load Frequency Control Technique for Hybrid Maritime Microgrids: Sophisticated Structure of Fractional-Order PIDA Controller

Review on Modeling and Control of Flexible Link Manipulators

Revolutionizing Vehicle Cruise Control: An Elite Opposition-Based Pattern Search Mechanism Augmented INFO Algorithm for Enhanced Controller Design

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