Master–Slave Stochastic Optimization for Model-Free Controller Tuning

Ateş, Abdullah; Alagöz, Barış Baykant; Yeroğlu, Celaleddin

doi:10.1007/s40998-017-0029-1

Cited by 8 publications

(4 citation statements)

References 32 publications

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“…Because the performance of the hardware will be limited while performing the algorithms, in other words, the hardware limitations are also important for online tuning applications. The SMDO algorithm was implemented for online controller tuning and modeling studies, and the computational many experimental study indicated that complexity of the SMDO is manageable by the hardware (Alagoz et al, 2013;A. Ates et al, 2017A.…”

Section: Comparisons With Diffrerent Methods Using Different Benchmark Functionsmentioning

confidence: 99%

Performance Analysis of SMDO Method with Benchmark Functions with Matlab Toolbox

Akpamukçu

Ateş

Alagöz

2020

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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SMDO method is a set and trial based optimization algorithm that is developed for online fine-tuning of controller parameters. SMDO method is implemented for several controller tuning applications. It can search parameter space with random backward and forward steps of each parameter. This property reduces risk of testing unstable control system configurations in controller design and thus makes the SMDO method more suitable for online parameter tuning of experimental systems. However, performance of SMDO has not been evaluated previously for benchmark functions in comparison with other well known heuristic optimization methods. This study aims to compare performances of Artificial Bee Colony (ABC), Cuckoo Search Optimization (CK), Particle Swarm Optimization (PSO) and Stochastic Multi-parameters Divergence Optimization (SMDO) methods for benchmark functions. Therefore, a benchmark tests program that is a user-friendly MATLAB GUI is introduced for user. This program can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/75043-smdo-method-with-benchmark-functions

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Section: Comparisons With Diffrerent Methods Using Different Benchmark Functionsmentioning

confidence: 99%

Performance Analysis of SMDO Method with Benchmark Functions with Matlab Toolbox

Akpamukçu

Ateş

Alagöz

2020

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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“…The headcount in the PSO is updated without modification. The hill intelligence is considered the foundation of PSO [85][86][87][88][89][90][91][92][93]. A whirlwind consists of n particles moving in a search engine with a size of d. Speed is assigned randomly to all particles.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Fractional-Order PID Controllers for Temperature Control: A Review

Jamil

Ali

et al. 2022

Energies

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Fractional-order proportional integral derivative (FOPID) controllers are becoming increasingly popular for various industrial applications due to the advantages they can offer. Among these applications, heating and temperature control systems are receiving significant attention, applying FOPID controllers to achieve better performance and robustness, more stability and flexibility, and faster response. Moreover, with several advantages of using FOPID controllers, the improvement in heating systems and temperature control systems is exceptional. Heating systems are characterized by external disturbance, model uncertainty, non-linearity, and control inaccuracy, which directly affect performance. Temperature control systems are used in industry, households, and many types of equipment. In this paper, fractional-order proportional integral derivative controllers are discussed in the context of controlling the temperature in ambulances, induction heating systems, control of bioreactors, and the improvement achieved by temperature control systems. Moreover, a comparison of conventional and FOPID controllers is also highlighted to show the improvement in production, quality, and accuracy that can be achieved by using such controllers. A composite analysis of the use of such controllers, especially for temperature control systems, is presented. In addition, some hidden and unhighlighted points concerning FOPID controllers are investigated thoroughly, including the most relevant publications.

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“…The results showed the FOPID controller outperformed the standard PID counterpart. Ates et al [39] also presented, experimentally and numerically, a model-independent fine-tuning FOPID controller, for a doublerotor helicopter, using master-slave stochastic multiparameters divergence optimization (SMDO) strategy where the results demonstrated the effectiveness of the suggested strategy in adjusting the reference model and FOPID and allowing more fitting without prior knowledge of the model assumptions simultaneously. Moreover, Ijaz et al [40] designed a FOPID controller adjusted using Nelder Mead (NM) optimization method and it turned out to be more effective when compared with FOPID tuned using PSO, and the traditional PID controller as well.…”

Section: Twin-rotor Systemsmentioning

confidence: 99%

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

2020

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This paper presents a review of the various control strategies that have been conducted to address and resolve several challenges for a particular category of unmanned aerial vehicles (UAVs), the emphasis of which is on the rotorcraft or rotary-wing systems. Initially, a brief overview of the important relevant definitions, configurations, components, advantages/disadvantages, and applications of the UAVs is first introduced in general, encompassing a wide spectrum of the flying machines. Subsequently, the focus is more on the two most common and versatile rotorcraft UAVs, namely, the twin-rotor and quadrotor systems. Starting with a brief background on the dual-rotor helicopter and a quadcopter, the full detailed mathematical dynamic model of each system is derived based on the Euler-Lagrange and Newton-Euler methods, considering a number of assumptions and considerations. Then, a state-of-the-art review of the diverse control strategies for controlling the rotorcraft systems with conceivable solutions when the systems are subjected to the different impediments is demonstrated. To counter some of these limitations and adverse operating/loading conditions in the UAVs, several innovative control techniques are particularly highlighted, and their performance are duly analyzed, discussed, and compared. The applied control techniques are deemed to produce a useful contribution to their successful implementation in the wake of varied constraints and demanding environments that result in a degree of robustness and efficacy. Some of the off-the-shelf developments in the rotorcraft systems for research and commercial applications are also presented.

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Master–Slave Stochastic Optimization for Model-Free Controller Tuning

Cited by 8 publications

References 32 publications

Performance Analysis of SMDO Method with Benchmark Functions with Matlab Toolbox

Performance Analysis of SMDO Method with Benchmark Functions with Matlab Toolbox

Fractional-Order PID Controllers for Temperature Control: A Review

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

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