Fuzzy Logic Control Techniques for Photovoltaic Systems

Fuzzy logic control has emerged as a versatile approach to managing the inherent nonlinearities and uncertainties of photovoltaic (PV) energy conversion. By translating physical variables such as voltage and power into degrees of membership, fuzzy controllers accommodate rapid fluctuations in irradiance and temperature without requiring precise mathematical models. A typical fuzzy logic controller comprises fuzzification of crisp inputs, an inference engine guided by heuristic rules, and defuzzification to generate control signals, often in the form of duty-cycle adjustments for DC–DC converters. Over the past decade, research has progressed from symmetrical membership-function designs to asymmetrical and self-tuning schemes, enhancing both dynamic response and steady-state accuracy. Integration with optimisation techniques such as particle swarm optimisation and neural networks has allowed automatic tuning of rule sets and membership parameters, reducing oscillations around the maximum power point and accelerating convergence. These advances support a wide spectrum of applications—from grid-connected farms requiring seamless ramp-rate control to remote off-grid systems demanding robust standalone operation. Collectively, they underline the global significance of fuzzy logic in boosting overall system efficiency, lowering balance-of-system costs and enabling smarter, more resilient photovoltaic deployments.

Research from Nature Portfolio

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Research from all publishers

Recent advances have demonstrated that self-tuning fuzzy controllers combined with particle swarm optimisation can streamline maximum power point tracking for grid-connected PV arrays: these systems use array voltage and power as inputs to generate duty cycles for boost converters and achieve tracking efficiencies exceeding 99% under diverse irradiance and temperature conditions. Other studies have developed fuzzy logic-based MPPT schemes for isolated PV installations employing push-pull converter topologies, showing robust performance in dynamic simulations with low total harmonic distortion and rapid adaptation to load or weather changes. Foundational work on membership function design has further led to highly reduced fuzzy logic controllers with minimal inputs and compact rule sets, simplifying implementation while maintaining high accuracy and fast transient response in both grid-tied and stand-alone contexts.

Fuzzy Logic Control Techniques for Photovoltaic Systems publication trend

The graph below shows the total number of articles in fuzzy logic control techniques for photovoltaic systems across all publications each year (not limited to Nature Index journals).

Technical terms

Fuzzy logic control: A method of control that uses approximate reasoning to handle systems with uncertainty and nonlinearity.

Membership function: A curve that defines how each point in the input space is mapped to a degree of membership between 0 and 1.

Maximum power point tracking (MPPT): Algorithms used to extract the maximum possible power from a photovoltaic array under varying conditions.

Particle swarm optimisation (PSO): A heuristic optimisation technique inspired by social behaviour in flocks to tune controller parameters.

Duty cycle: The fraction of time a power converter switch remains on during each switching period.

Total harmonic distortion (THD): A measure of distortion in a waveform due to harmonics, expressed as a percentage of the fundamental frequency.

References

1. An asymmetric fuzzy-based self-tuned PSO-Optimized MPPT controller for grid-connected solar photovoltaic system. Energy Conversion and Management X (2025).
2. Fuzzy Logic Based MPPT Controller for a PV System. Energies (2017).
3. Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique. Energies (2015).
4. An Asymmetrical Fuzzy-Logic-Control-Based MPPT Algorithm for Photovoltaic Systems. Energies (2014).
5. A Novel Algorithm for MPPT of an Isolated PV System Using Push Pull Converter with Fuzzy Logic Controller. Energies (2020).
6. A Highly-Efficient Fuzzy-Based Controller With High Reduction Inputs and Membership Functions for a Grid-Connected Photovoltaic System. IEEE Access (2020).
7. Maximum Power Point Tracker Based on Fuzzy Adaptive Radial Basis Function Neural Network for PV-System. Energies (2019).