Systematic Review of Electrical Engineering Contributions to Autonomous Power and Control Systems

Abstract

This study presented a comprehensive quantitative evaluation of control strategies applied in autonomous power and control systems, focusing on key dynamic performance indicators including response time, settling time, overshoot, steady-state error, and control accuracy. A total of 60 experimental simulation runs were conducted using validated control system models to compare conventional proportional-integral-derivative control with advanced approaches such as adaptive control and model predictive control. The findings revealed that advanced control strategies significantly enhanced system performance across all evaluated metrics. Model predictive control achieved the lowest average settling time (M = 1.82 s) and minimal overshoot (M = 3.4%), while adaptive control demonstrated the highest control accuracy (M = 96.7%) and reduced steady-state error (M = 1.30%). In contrast, proportional-integral-derivative control exhibited slower response time (M = 2.95 s), higher overshoot (M = 7.8%), and greater steady-state error (M = 2.8%), indicating lower efficiency in dynamic environments. Inferential statistical analysis using one-way ANOVA confirmed that these differences were statistically significant at p<0.05p < 0.05p<0.05, with large effect sizes observed for settling time (η2=0.41\eta^2 = 0.41η2=0.41) and control accuracy (η2=0.38\eta^2 = 0.38η2=0.38). Regression analysis further indicated that control strategy type explained up to 62% of the variance in system performance outcomes. Secondary analysis demonstrated that adaptive and hybrid control approaches maintained stable performance under high-load variations, with only an 8% increase in response time, whereas conventional control showed up to a 25% increase in overshoot. Overall, the study confirmed that intelligent and hybrid control strategies substantially improve system responsiveness, stability, and reliability. These findings provide strong empirical evidence supporting the adoption of advanced control methodologies in autonomous power systems and contribute to the development of efficient, robust, and data-driven control architectures.