Yazar "Rubanenko, Olena" seçeneğine göre listele

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    A novel hyperbolic tangent-based PID controller tuned by the artificial lemming algorithm for nonlinear steam condenser pressure control
    (Nature Portfolio, 2026) Ekinci, Serdar; Izci, Davut; Jabari, Mostafa; Celik, Emre; Bajaj, Mohit; Vishnuram, Pradeep; Rubanenko, Olena
    Precise pressure regulation in nonlinear shell-and-tube steam condensers is essential for maintaining thermal efficiency and operational safety in power generation plants; however, conventional proportional-integral (PI) and proportional-integral-derivative (PID) controllers struggle with nonlinear dynamics, leading to overshoot, slower settling, and reduced robustness. In this regard, a novel hyperbolic tangent-based PID (tanh-PID) controller is developed in this study to introduce smooth nonlinear gain modulation, enabling enhanced damping behavior and improved transient shaping. The recently introduced artificial lemming algorithm (ALA) is employed to optimally tune the proposed controller for integral of time-weighted absolute error minimization. Extensive simulation studies are performed using a comprehensive nonlinear condenser model incorporating steam-air interactions and hot-well dynamics. The proposed strategy is benchmarked against four competitive optimization algorithms (coati optimization algorithm, dandelion optimizer, success-history based adaptive differential evolution with linear population size reduction, and adaptive artificial electric field algorithm) and compared with state-of-the-art PI and fractional-order PID (FOPID) controllers reported in the literature. The ALA-tuned tanh-PID achieves the lowest integral of time-weighted absolute error (2.1189), fastest rise time (0.5960 s), minimal settling time (12.4799 s) and overshoot (5.8056%), along with near-zero steady-state error (4.0776 x 10(-)4%), outperforming all compared methods in both transient response and steady-state accuracy. Robustness analyses further confirm superior disturbance rejection and reliable reference tracking under dynamic uncertainties. These results demonstrate that the proposed methodology offers an efficient, low-complexity, and high-performance control solution suitable for real-time deployment in industrial steam condenser systems.
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    Design of Novel Exponential PDN Controller via Quadratic Interpolation Optimiser for Nonlinear and Unstable Ball and Beam System
    (Wiley, 2026) Izci, Davut; Ekinci, Serdar; Celik, Emre; Uyar, Murat; Bajaj, Mohit; Blazek, Vojtech; Rubanenko, Olena
    This study presents a novel exponential proportional-derivative controller with filter (exp-PDN) for stabilising the nonlinear and underactuated ball and beam system. Unlike conventional PID-based approaches, the proposed controller removes the integral term, resulting in faster transient responses and improved robustness. It incorporates nonlinear exponential shaping of both the error and its derivative, along with a filtered derivative path for enhanced noise handling. A custom multi-objective cost function, comprising the squared error, settling time, and percent overshoot, is proposed to evaluate control performance. The quadratic interpolation optimiser (QIO), a recently developed metaheuristic based on analytical interpolation, is employed to optimise the controller parameters. To validate its effectiveness, the exp-PDN controller is compared against five state-of-the-art metaheuristic algorithms: QIO, spider wasp optimiser, komodo mlipir algorithm, golden eagle optimiser, and slime mould algorithm. The QIO-optimised exp-PDN achieves the best performance, with the lowest cost value (0.3211), minimal overshoot (5.52%), fast rise time (0.97 s), and smallest steady-state error (4.1643 x 10- 4). Further comparisons with QIO-optimised phase-lead and PID-with-filter controllers demonstrate the superiority of the proposed method in both transient and steady-state behaviour. In summary, this work advances the control of nonlinear unstable systems by delivering a structurally simple yet highly responsive control architecture. The combination of dual-channel exponential shaping and efficient metaheuristic optimisation results in state-of-the-art closed-loop performance, highlighting the practical value of the proposed exp-PDN framework for real-world control applications.
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    Designing a cascaded exponential PID controller via starfish optimizer for DC motor and liquid level systems
    (Nature Portfolio, 2025) Izci, Davut; Jabari, Mostafa; Celik, Emre; Ekinci, Serdar; Bajaj, Mohit; Rubanenko, Olena; Prokop, Lukas
    In this study, a novel cascaded exponential proportional-integral-derivative (exp-PID) controller tuned by the starfish optimization algorithm (SFOA) is proposed for enhancing the transient and steady-state performance of nonlinear dynamic systems. The design objective is to achieve improved adaptability, robustness, and precision under varying operating conditions and external disturbances. The exponential PID structure introduces nonlinear modulation in the proportional and derivative components, enabling smoother control action and superior damping characteristics compared to conventional PID and fractional-order PID designs. The proposed SFOA-based exp-PID controller is validated on two benchmark systems: a DC motor speed control system and a three-tank liquid-level process. Across multiple independent trials, the controller achieved outstanding results, with the DC motor system attaining a rise time of 0.0039 s, settling time of 0.0083 s, and zero overshoot, while the three-tank system reached a rise time of 1.72 s, settling time of 2.47 s, overshoot of 1.5%, and steady-state error of 9.22 x 10(-)(5)%. Comparative analyses with recently developed algorithms (including the flood algorithm, greater cane rat algorithm, mantis search algorithm, and dandelion optimizer) as well as previously reported methods demonstrate the superior convergence behavior, stability, and accuracy of the proposed controller. Statistical evaluations further confirm the method's robustness and consistent performance across repeated runs.