Impact of Steady-State Error Minimization on the Performance of Numerical Optimization Techniques in Linear Automatic Control Systems

Abstract

This paper investigates the impact of steady-state error minimization on the performance of numerical optimization techniques in linear automatic control systems, introducing a novel framework that integrates advanced genetic algorithms and machine learning to enhance controller tuning It highlights the significance of selecting appropriate test signals to generate quality system responses, which directly affects stability and reliability. Various optimization techniques are discussed, including classical methods and modern algorithms such as genetic algorithms and machine learning. Special attention is given to astatic control, which minimizes static errors and enhances controller reliability. Experimental results reveal that optimizing for one signal type can significantly diminish performance for another type. The paper introduces trade-offs that facilitate simultaneous consideration of performance responses to various stimuli. The conclusions underscore the importance of carefully selecting test signals and provide recommendations for automatic control practitioners, ultimately leading to improved reliability and efficiency in systems under dynamic conditions