Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2026In-depth Analysis of the Control Unit for Multiple Voltage Rails to Drive a Variety of ICs
Yanarateş, Cağfer; Altan, Aytaç
Generating a negative output voltage rail from a positive input voltage is possible by reconfiguring an ordinary buck regulator into an inverting buck-boost (IBB) topology. Rather than operating considerations such as voltage and current stresses, the main challenge associated with this transformation arises from differences in the small-signal closed-loop characteristics. The most critical distinction is the presence of a right-half-plane (RHP) zero in the control-to-output transfer function of the IBB, which introduces non-minimum-phase behaviour, reduces the achievable phase margin, and may lead to instability and poor load transient response. For conventional PI controllers, the buck and IBB converters resulted in settling times of 5.0 ms and 6.8 ms, with overshoots of 13.65% and 8.46%, respectively, thus demonstrating the performance limitations of fixed-gain designs. As a solution, an auto-tuned PI controller with adaptive features has been proposed, which operates effectively in both the buck and IBB topologies. The proposed scheme achieves a significantly faster settling time of 2.27 ms with overshoot and undershoot below 2%, thereby ensuring superior robustness and dynamic performance compared to conventional approaches.
- MakaleYayıncıda açık erişim2026Measurement-aware zero-phase iterative learning control for robust regulation of nonideal boost converters in electric vehicle fast charging
Altan, Aytaç; Alzaidi, Mohammed S.; Yanarateş, Cağfer
Integrating battery energy storage with DC-DC boost converters for electric vehicle fast charging exposes the regulator to ageing-induced parameter drift, periodic load pulses, and, critically, the nonidealities in the output-voltage sensing chain. This paper proposes a measurement-aware, zero-phase iterative learning control scheme for robust output-voltage regulation of a nonideal boost converter whose parameters are matched to those of a commercial Texas Instruments TPS6102x battery regulator. The controller combines an inner proportional-integral stabilizing loop with an outer zero-phase learning law that updates a feedforward correction based on the sensor-captured output trajectory; a forward-backward robustness filter suppresses the amplification of measurement noise and quantization across successive trials without introducing phase lag. The averaged small-signal model is validated against a 960 kHz switched simulation, with agreement within 0.2% at the operating point and a transient-waveform correlation of 0.91, while the bandwidth limitations imposed by the lightly damped resonance and the nonminimum-phase dynamics on feedback are quantified from the loop frequency response. Because feedback alone is bandwidth-limited, the learning feedforward recovers the intrinsic converter speed for the repetitive task and reduces the periodic tracking error by more than an order of magnitude relative to a fairly tuned baseline. A bounded-error convergence theorem shows that, under real sensing, the tracking error converges to a neighbourhood whose size is proportional to the measurement-disturbance level; this is confirmed numerically, with the asymptotic error scaling approximately linearly with the noise standard deviation once the noise dominates the filter bias. Systematic sweeps of converter resolution, noise, and sensor delay show that the zero-phase measurement-aware law remains bounded where a naive learning law diverges, and a 500-sample Monte Carlo study confirms robustness to component tolerances, load variation, and parasitic aging. The primary physical task is the rejection of a repeating high-current charging pulse: over successive charging cycles the scheme reduces the periodic output-voltage sag from 35% to below 0.01%, whereas the best feedback comparator leaves a 17% residual, and the sinusoidal reference used elsewhere serves as a spectral diagnostic that isolates the feedback-bandwidth ceiling at a single known frequency. The learning law is further verified in closed loop on the cycle-by-cycle switched large-signal converter, where it converges and stays bounded. A per-unit scale-invariance analysis then establishes the conditions under which the design obtained on the low-power benchmark transfers to fast-charging power levels, as confirmed on a 400 V, 6.1 kW stage that reproduces the benchmark convergence exactly.
- MakaleAçık Erişim2025Solving the Dynamic Stability Challenge of Automatic Gain-Controlled Phase-Locked Loops: A Robust Alternative via Enhanced SRF-PLL Design and Implementation
Yanarateş, Cağfer; Altan, Aytaç
This study presents a robust alternative to conventional phase-locked loop (PLL) architectures through the design and real-time implementation of an enhanced Synchronous Reference Frame Phase-Locked Loop (SRF-PLL) for single-phase inverter systems. Targeting the inherent dynamic stability limitations of Automatic Gain-Controlled PLLs (AGC-PLLs), the proposed SRF-PLL demonstrates superior performance in transient response and tracking accuracy. Comparative evaluations under diverse dynamic operating conditions—including phase angle jumps, voltage sags/swells, frequency steps, noise, and harmonic distortion—highlight its resilience and adaptability. Experimental results reveal a substantial improvement in dynamic response, achieving 2.5 to 6 times faster settling times depending on the operating condition. Moreover, peak-to-peak oscillations were significantly suppressed, with deviations reduced from 2° to nearly 0° under a 5 Hz frequency step scenario, even in the presence of 3rd order harmonic injection. For a 40° phase angle step, the proposed system achieves a fivefold improvement in attenuation time (0.03 s vs. 0.11 s) compared to the AGC-PLL. Under voltage sag conditions with noise and 3rd order harmonic injection, the enhanced SRF-PLL reduces the attenuation time from 0.2 s to 0.08 s. During voltage swell scenarios, it further improves the response time from 0.025 s to 0.01 s. These findings confirm the practical benefits and efficiency of the enhanced SRF-PLL for applications requiring precise and reliable phase tracking and synchronization.
- MakaleAçık Erişim2025Optimizing control strategies for DC-DC boost converters: Real-time application of an adaptive gain scheduled ISA-PI controller with hybrid state-space and linear parameter-varying modelling
Yanarateş, Cağfer
This paper introduces an innovative sophisticated control scheme for a DC-DC boost converter (DCBC), employing an adaptive gain scheduled ISA-PI controller. Addressing the inherent non-minimum phase behaviour arising from a right-half plane zero and the complexities associated with nonlinear dynamics during continuous conduction mode (CCM), the proposed adaptive gain scheduled ISA-PI controller incorporates the distinct adjustable parameter within the controller structure. This parameter is instrumental in enhancing the adaptability of the controller to varied operating conditions. The adaptive ISA-PI controller seamlessly integrates the real-time duty cycle value, replacing traditional tuning variables with precision. The dynamic adjustment of this sole controllable parameter is facilitated through a carefully designed look-up table, employing the loop-shaping method. Verification of the proposed control system’s effectiveness is conducted using MATLAB/Simulink, incorporating a comprehensive comparative analysis against single proportional integral (PI) controllers. The assessment centres on evaluating the system’s precision in tracking desired signals and regulating plant process variables with optimal efficiency, minimizing delays and overshoot. Experimental validation is further undertaken using MATLAB/Simulink/Stateflow on a dSPACE Real-time-interface (RTI) 1007 processor, DS2004 High-Speed A/D, and CP4002 Timing and Digital I/O boards. The experimental results confirm the superior performance of the proposed adaptive gain schedule ISA-PI controller, which has a unique configurable parameter. This controller demonstrated a twofold improvement in tracking speed and significantly improved disturbance rejection, confirming its effectiveness.
- MakaleAçık Erişim2024Investigating the impact of discretization techniques on real-time digital control of DC-DC boost converters: A comprehensive analysis
Yanarateş, Cağfer; Zhou, Zhongfu; Altan, Aytaç
This paper provides a thorough comparative analysis of discretization techniques commonly utilized in digital control applications for DC-DC boost converters (DBCs). Given the crucial role of DC-DC converters in various industries such as renewable energy systems, electric vehicles, and portable electronic devices, it is imperative to optimize their performance through efficient digital control. This study aims to improve the understanding of the effects of discretization methods on control system behaviour. It investigates their impact on the performance, stability and accuracy of control algorithms in the context of DBC, using dSPACE real-time interface (RTI) and hardware-in-the-loop (HIL) simulation for validation. This approach replicates real-world conditions, allowing performance to be evaluated in a controlled environment. The analysis examines behaviour in the time and frequency domains, providing insight into the strengths and weaknesses of each method. Experimental validation using MATLAB/Simulink/Stateflow on dSPACE RTI 1007 processor, DS2004 high-speed A/D and CP4002 timing and digital I/O boards ensures that the comparative analysis provides practical benefits for DBC digital control applications.
- MakaleAçık Erişim2024A robust chaos-inspired artificial intelligence model for dealing with nonlinear dynamics in wind speed forecasting
Barış, Caner; Yanarateş, Cağfer; Altan, Aytaç
The global impacts of climate change have become increasingly pronounced in recent years due to the rise in greenhouse gas emissions from fossil fuels. This trend threatens water resources, ecological balance, and could lead to desertification and drought. To address these challenges, reducing fossil fuel consumption and embracing renewable energy sources is crucial. Among these, wind energy stands out as a clean and renewable source garnering more attention each day. However, the variable and unpredictable nature of wind speed presents a challenge to integrating wind energy into the electricity grid. Accurate wind speed forecasting is essential to overcome these obstacles and optimize wind energy usage. This study focuses on developing a robust wind speed forecasting model capable of handling non-linear dynamics to minimize losses and improve wind energy efficiency. Wind speed data from the Bandırma meteorological station in the Marmara region of Turkey, known for its wind energy potential, was decomposed into intrinsic mode functions (IMFs) using robust empirical mode decomposition (REMD). The extracted IMFs were then fed into a long short-term memory (LSTM) architecture whose parameters were estimated using the African vultures optimization (AVO) algorithm based on tent chaotic mapping. This approach aimed to build a highly accurate wind speed forecasting model. The performance of the proposed optimization algorithm in improving the model parameters was compared with that of the chaotic particle swarm optimization (CPSO) algorithm. Finally, the study highlights the potential of utilizing advanced optimization techniques and deep learning models to improve wind speed forecasting, ultimately contributing to more efficient and sustainable wind energy generation. This robust hybrid model represents a significant step forward in wind energy research and its practical applications.
- MakaleAçık Erişim2023Performance analysis of digitally controlled nonlinear systems considering time delay issues
Yanarateş, Cağfer; Okur, Serkan; Altan, Aytaç
In this paper, a comprehensive investigation into discretization, effective sample time selection considering delays in the system, and time and frequency domain analysis of a DC-DC buck converter, which plays a vital role in photovoltaic (PV) systems, is conducted to enhance the understanding of their dynamic behavior, optimize control algorithms, improve system efficiency, and ensure reliable power conversion in photovoltaic applications. To effectively address the non-linear behavior and enhance digital control of a buck converter by selecting the best sample time, several approaches can be employed. These include accurate modeling and identification of non-linear elements, development of advanced control algorithms that account for non-linearities, implementation of adaptive control techniques, and utilization of feedback mechanisms to compensate for deviations from linearity. By considering and mitigating the non-linear behavior, digital control systems can achieve improved accuracy, stability, and transient behavior in regulating the buck converter's output waveforms (voltage or current). The results of the study demonstrated that the trapezoidal integration method which is also known as bilinear approximation, or Tustin's approach outperformed other commonly used discretization methods, such as first-order hold (FOH), zero-order hold (ZOH), impulse response matching (impulse invariant), and matched pole-zero (MPZ) technique, in dual-domain (both time and frequency) analysis. The key finding highlighting the superiority of the bilinear approximation was its ability to achieve the closest match in the frequency domain bridging the continuous-time and discrete systems. This finding emphasizes the significance of the bilinear approach in preserving the frequency characteristics of the original continuous-time system during discretization. By employing this method, the discrete system closely approximated the behavior of its continuous-time counterpart, ensuring accurate frequency-domain representation.