Design and Implementation of a Ball and Beam PID Control System Based on Metaheuristic Techniques

Azar, Ahmad Taher; Ali, Nourhan; Makarem, Sarah; Diab, Mohamed Khaled; Ammar, Hossam Hassan

doi:10.1007/978-3-030-31129-2_29

Cited by 13 publications

(7 citation statements)

References 16 publications

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“…To find the optimal PID controller, a multi-objective optimization problem with constraints has to be solved [4,[12][13][14]. Although there is no clear methodology to define the cost function to optimize [12] (usually, to minimize), most of them rely on the tracking error variation (both on transient and steady-state zones in signal dynamics).…”

Section: Introductionmentioning

confidence: 99%

“…A wide range of metaheuristics has been used in optimal PID design, as reported in the literature [5]. For example, the following metaheuristics were preferred: the Genetic Algorithms [19] in [6,8,16,17,20]; Particle Swarm Optimization [19] in [8,13,21]; Ant Colony Optimization [19] in [6,9]; Artificial Bee Colony [19] in [3,13,21,22]; Differential Evolution [23] in [8]; Firefly Algorithm [19] in [4,15,24]; Cuckoo Search [25] in [2,24]; Bat Algorithm Optimization [19] in [13,22]; Symbiotic Organisms Search [26] in [9]; and Grey-Wolf Optimization [27] in [3]. In some of these references, two or more metaheuristics were combined.…”

Section: Introductionmentioning

confidence: 99%

“…Most contributions deal with continuous time SISO PID controllers [6,9,12,13,15,21,22,24]. To the best of our knowledge, few contributions are concerned with designing optimal PID controllers for the real-world MIMO processes [4,9,20].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Identification and Metaheuristic PID Control of a Two-Tank System

Ștefănoiu

Culita

2021

Electronics

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In the modern optimization context, this paper introduces an optimal PID-based control strategy for a two-tank installation, namely ASTANK2. The process model was identified by using raw and spline smoothed measured data, respectively. Two PID controller configurations, a standard (regular) one (PID-R) and a non-standard one (PID-N), were considered for each type of model, resulting in four regulators. The optimal tuning parameters of each regulator were obtained by a searching approach relying on a combination of two metaheuristics. Firstly, an improved version of the Hill Climbing algorithm was employed to comprehensively explore the searching space, aiming to find fairly accurate tuning parameters. Secondly, an improved version of the Firefly Algorithm was proposed to intensively refine the search around the previously found optimal parameters. A comparative analysis between the four controllers was achieved in terms of performance and robustness. The simulation results showed that all optimal controllers yielded good performance in the presence of exogenous stochastic noise (bounded error tracking, setpoint tracking, reduced overshoot, short settling time). Robustness analysis is extensive and illustrates that the PID-R controllers are more robust to model uncertainties, whilst PID-N controllers are more robust to tracking staircase type references.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Identification and Metaheuristic PID Control of a Two-Tank System

Ștefănoiu

Culita

2021

Electronics

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“…Gerçek sistemlerin analizindeki problemlerden en önemlisi çalışılacak sistemin laboratuvar ortamına getirilememesidir. Uçakların inişte ve türbülanslı hava akışı durumlarında yatay dengeyi sağlayabilmek ve benzer konum kontrolü gerektiren problemlere eşdeğer sonuçlar veren top ve çubuk sistemi; dinamik ve kararsız yapısı ile birçok araştırmacının ilgisini çekmektedir [2][3][4]. İki serbestlik dereceli top ve çubuk sisteminde tanımlanan çubuk üzerinde topun verilen referans konumuna gitmesi ve dengede kalması istenmektedir.…”

Section: Introductionunclassified

“…Top ve çubuk sistemi için kaskad denetleyici parametrelerinin meta-sezgisel algoritmalarla optimizasyonu Optimization of cascade controller parameters for ball and beam system with meta-heuristic algorithms Hasan Hüseyin Bilgiç 1* , Muhammed Selimhan Tutumlu 2 , Çağlar Conker 3…”

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Top ve çubuk sistemi için kaskad denetleyici parametrelerinin meta-sezgisel algoritmalarla optimizasyonu

Bilgiç¹,

Tutumlu²,

Conker³

2021

Deu Muhendislik Fakultesi Fen Ve Muhendislik

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In this study, cascade PD+P controller gains for the ball and beam system are tuned using metaheuristic algorithms. The aim of the ball and beam system, which provides the simulation of many systems in the laboratory environment, is to reach the reference position and balance of the ball, which moves freely on a single axis on the beam. PD+P cascade controller which contained fast and slow dynamics at the same time is designed for the ball and beam system. Cascade controller gains, which are difficult to adjust compared to conventional controllers, are tuned using Artificial Bee Colony and Teaching Learning Based Optimization algorithms. In the optimizations applied, IAE, ITAE, ISE, MSE objective functions, and a new step response based objective function are proposed. In the controller responses obtained using the proposed objective function, the ball reached the desired reference position without overshoot.

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