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Sliding Mode Based P&O and Neural Network MPPT in MATLAB

Sliding Mode Based P&O and Neural Network MPPT in MATLAB

We'll delve into the design and simulation of a power electronics (PE) system integrated with a solar panel, employing advanced control techniques for efficient power generation. Specifically, we'll explore the implementation of a sliding mode controller and a neural network-based Maximum Power Point Tracking (MPPT) algorithm to maximize the performance of the solar panel.

System Configuration

The PE system utilizes a 250-watt solar panel, and its operation is monitored through measurements of current, voltage, and power. A Boost converter is employed to regulate the voltage output, and the system is connected to a load to utilize the generated power effectively.

Sliding Mode Controller

The sliding mode controller plays a crucial role in regulating the system's operation. By comparing the measured photovoltaic (PV) voltage and current with reference values, the controller generates control signals to optimize the power extraction process. This ensures that the system operates at its maximum power point under varying environmental conditions.

Neural Network-Based MPPT

In addition to the sliding mode controller, the system incorporates a neural network-based MPPT algorithm. This advanced technique utilizes machine learning principles to predict the maximum power point based on inputs such as solar radiation and temperature. By training the neural network with historical data, it can accurately estimate the optimal operating point, enhancing the system's efficiency and performance.

Simulation and Results

Through simulation, we analyze the system's performance under different environmental conditions. We observe variations in PV voltage, current, and power output, highlighting the effectiveness of the control algorithms in maximizing power generation. Additionally, we compare the results obtained with the sliding mode controller and the neural network-based MPPT, noting any differences in performance and adaptability.


The integration of sliding mode control and neural network-based MPPT algorithms offers a promising approach to optimize solar power generation in PE systems. By leveraging advanced control techniques and machine learning, it's possible to enhance efficiency, reliability, and adaptability, ultimately contributing to the widespread adoption of renewable energy sources.

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