MISSILE GUIDANCE LAW DESIGN USING A FUZZY BRAIN EMOTIONAL LEARNING CONTROLLER

Abstract

This study introduces an innovative guidance law utilizing a Fuzzy Brain Emotional Learning Controller (FBELC) designed for high-performance target tracking and missile homing applications. The FBELC represents a computational intelligence framework simulating the neural emotional learning mechanism, specifically the interaction between the amygdala (which governs emotional reactions) and the orbitofrontal cortex (which handles cognitive regulation). This biologically inspired structure is converted into a control system consisting of two coupled neural networks: an emotional network for rapid assessment and a sensory network for processing context. Their interplay empowers the FBELC to prioritize learning during critical moments, resulting in swifter and more resilient adaptation compared to traditional intelligent controllers. The suggested control framework utilizes the FBELC as the primary controller to emulate an ideal control strategy, while an auxiliary compensation controller is implemented to mitigate approximation errors and external perturbations, thereby guaranteeing system stability and robustness. The efficacy of the proposed FBELC-based guidance system was assessed through extensive numerical simulations and compared against the established Adaptive Fuzzy Cerebellar Model Articulation Controller (AF-CMAC) . Comparative findings indicate that the FBELC performs superiorly across critical metrics. Notably, the suggested approach yields a substantial decrease in miss-distance and exhibits accelerated convergence along with enhanced control efficiency relative to the AF-CMAC guidance law detailed in [1]. These outcomes validate that the FBELC is not only viable but also provides improved capabilities, positioning it as a highly promising candidate for modern missile guidance systems that require exceptional precision and flexibility.