Fuzzy controller-driven pattern search optimization for a DC-DC boost converter to enhance photovoltaic MPPT performance – Scientific Reports

A research team has demonstrated a high-efficiency maximum power point tracking (MPPT) strategy for solar photovoltaic systems using a DC-DC boost converter and an intelligent fuzzy controller tuned by pattern search (PS) optimization. Put simply: by pairing a model-free fuzzy logic controller with a derivative-free optimizer, the system tracks the maximum power point with exceptional accuracy, adapting seamlessly to changing irradiance and temperature. In trials, the approach achieved an average MPPT efficiency of 99.7% and maximum power outputs up to 74.48 kW, outperforming the widely used Perturb and Observe (P&O) technique.

Why MPPT matters—and how it’s implemented

MPPT is the brain of a PV power system, continually nudging the operating point of the solar array to extract maximum power as sunlight and temperature vary. In conventional designs, P&O adjusts the reference voltage based on observed changes in power and voltage. The control loop senses PV voltage and current, derives a duty ratio, and generates a PWM signal for the boost converter’s IGBT gate.

While P&O is simple and popular, it tends to oscillate around the true maximum power point and struggles during rapid environmental changes. Other classical methods such as hill climbing (HC) and incremental conductance (IC) face similar challenges—either trading off precision under fast irradiance swings or adding computational burden, with persistent oscillations causing power loss.

From heuristics to intelligent control

Fuzzy logic control (FLC) offers a compelling alternative because it doesn’t require an exact mathematical model of the PV system. In the approach reported here, the controller senses PV voltage and current to compute a reference voltage. The difference between reference and actual voltage becomes the error signal fed to the FLC, whose output is the duty ratio that drives the PWM for the boost converter. This architecture is specifically designed to tame oscillations and speed up convergence to the MPP.

The twist is how the fuzzy controller is tuned. Instead of labor-intensive trial-and-error tuning of membership functions (MFs) and rules, the team optimizes them using pattern search (PS), particle swarm optimization (PSO), and genetic algorithms (GA), with root mean square error (RMSE) as the objective function. All methods were evaluated under the same conditions in MATLAB R2023b.

Pattern search takes the lead

Across 100 iterations, fuzzy-PS delivered the lowest RMSE of 0.6861, beating fuzzy-PSO (0.9454) and fuzzy-GA (1.257). That translated into faster settling, lower steady-state oscillations, and superior energy capture when irradiance and temperature changed abruptly. The team also experimented with a refined variant labeled “PA,” yet the headline metrics attribute the best error performance to the PS-tuned fuzzy controller.

In comparative runs against P&O, the fuzzy-PS controller consistently tracked the true MPP with minimal dithering and handled step changes in operating conditions more gracefully. The result: sustained average MPPT efficiency of 99.7% and peak power draws up to 74.48 kW.

Context: strengths and limits of popular MPPT methods

• P&O and HC: Simple and low-cost but oscillate around MPP; performance degrades with rapid irradiance shifts.
• IC: More precise in theory but can still oscillate and demands higher computation.
• PSO and GA: Powerful metaheuristics that improve tracking, yet can suffer premature convergence and local minima entrapment.
• ANN: Fast convergence but requires extensive, accurate training; risk of local minima.
• FLC: Model-free and well-suited to nonlinearities, but traditional tuning is time-consuming without optimization support.

Pattern search stands out because it’s a derivative-free local optimizer that balances exploration and exploitation without gradient information. When used to tune fuzzy MFs and rules, PS provides robust convergence and resilience under noisy, time-varying PV operating conditions—precisely where classical heuristics and some metaheuristics falter.

How the controller works in the converter loop

The system measures PV voltage and current and computes a reference operating point for the array. The fuzzy controller processes the error and error-rate signals, producing a duty ratio that drives PWM generation. The PWM signal is applied to the IGBT gate in the DC-DC boost converter, shifting the operating point toward the MPP. By constraining reference voltage within bounds and continuously refining the duty ratio, the loop avoids runaway behavior and reduces steady-state ripple.

Performance under real-world variability

Under rapid irradiance and temperature swings, the fuzzy-PS controller demonstrated quick convergence to the MPP and negligible oscillation once settled. Compared with P&O, it captured more energy over time, especially during fast transients. The study notes that the optimization process consistently improved fuzzy MF shapes, yielding robust behavior across test profiles, including partial shading and non-uniform irradiance scenarios commonly responsible for hot spots and mismatch losses.

Why it matters

As PV deployment scales across rooftops, utility farms, and microgrids, MPPT reliability under diverse conditions becomes a major lever for lifetime energy yield. The fuzzy-PS approach offers a practical path to higher performance without demanding detailed models or heavy computational overhead. For developers and system integrators, it represents a compelling middle ground: smarter than heuristics, lighter than deeply trained AI.

Main contributions

• Introduces a fuzzy logic MPPT controller tuned via pattern search to improve convergence speed and reduce oscillations in a DC-DC boost converter.
• Optimizes fuzzy membership functions using PS, GA, and PSO with RMSE as the objective; PS achieves the best RMSE (0.6861) within 100 iterations.
• Demonstrates robust performance under irradiance and temperature variations, achieving up to 74.48 kW and an average MPPT efficiency of 99.7%.
• Provides a head-to-head comparison against P&O, showing superior tracking accuracy and energy capture.
• Positions derivative-free optimization (PS) as a practical, computation-light alternative to heavy metaheuristics and manually tuned fuzzy designs.

Bottom line: By fusing fuzzy logic with pattern search optimization, this MPPT architecture delivers near-ideal tracking efficiency and resilience in the face of real-world PV volatility—raising the bar for next-generation solar power electronics.