On an Optimized Fuzzy Supervized Multiphase Guidance Law

Proportional navigation (PN) is an effective guidance law in chasing a constant speed target. However, intercepting a maneuvering target requires extra provisions to contain target acceleration. This is observed in the next generation of guidance laws, namely augmented proportional navigation (APN) and the optimal guidance law (OGL). The other advanced guidance method is integral sliding mode (ISM), which exhibits superb low miss distance (MD), but unfortunately at the cost of disappointing effective lateral acceleration (C_eff), higher than the modest level that APN and OGL demand. Reducing both MD and C_eff can be achieved using a multiphase algorithm. A setup of APN, OGL and ISM is proposed to integrate the strength of each and overcome their weaknesses. Hybrid algorithm smooth management is conducted by an optimally tuned fuzzy supervisory controller. The results indicate that in facing a constant acceleration target, an APN‐ISM twin yields the best results, while in case of a constant jerk target an APN‐OGL‐ISM triplet renders excellent interception. Miss distance as low as 0.012m can be achieved with significantly lower control effort than required by ISM alone. The simulations results confirm the conclusions and illustrate the capacity of the algorithm in combating maneuvering targets.