A Robust Sequential Quadratic Programming Optimization Technique for Proportional-Integral- Derivative Controller of Boost Converter in Brushless Direct Current Motor Considering Renewable Energy Sources

The development of renewable energy has had a positive impact on reducing the level of fossil fuel emissions. One of the renewable energies that currently has a fairly high level of efficiency is Photovoltaic (PV). Motors supplied by PV have a low starting voltage and cause voltage fluctuations, reducing the stability and performance of the motor. One method that can be used to reduce voltage fluctuations is to control its stability. This can be achieved by adding a boost converter controlled by an optimized Proportional Integral Derivative (PID). Several methods are often used to find the right PID value, including the trial-and-error method and the numerical optimization method. This paper discusses the optimization of PID tuning on boost converters and Brushless Direct Current (BLDC) motors using the Sequential Quadratic Programming (SQP) method. Tuning using SQP provides an overshoot value of 0% and a completion time of 480 s, which is much smaller than the metaheuristic-based ones, such as Geometric Mean Optimizer (GMO) 379 s and Firefly Algorithm (FA) 1108 s. In addition, the simulation time obtained on SQP is 13.25 and 16.41 times faster than GMO and 16.41 times faster than FA. Considering the performance in terms of the integral of time-weighted absolute error (ITAE), SQP has a better performance by 1018555 compared to the metaheuristic method, which has a performance of 1097300. This difference indicates that SQP, which is a metaheuristic method, produces a more optimal ITAE value by 7.18% compared to FA and 9.64% compared to GMO.

BLDC motor; Boost converter; PID controller; Renewable energy; Robust SQP optimization

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