Fuzzy logic implementation of proportional navigation guidance

Rajasekhar, V.; Sreenatha, A.G.

doi:10.1016/s0094-5765(99)00189-7

Cited by 31 publications

(20 citation statements)

References 3 publications

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“…For example, Rajasekhar et al [8] proposed a fuzzy logic controller to adjust the navigational constant of the PNG. Deskovski et al [9] developed a fuzzy-rule-based system which can calculate the closing velocity and lead angle in order to compute the navigation constant of the PNG.…”

Section: Submitting the Manuscriptmentioning

confidence: 99%

The Design of a Modified PSO Guidance Law Using Predictor and LOS Rate Evaluation

Lee

Chen

Chen³

et al. 2016

MATEC Web Conf.

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Abstract. This paper proposes a modified particle swarm optimization guidance (MPSOG) for the pursuit-evasion optimization problem. If the line-of-sight (LOS) rate equals zero, the curvature of the missile's trajectory would be smaller and the probability of intercept greater. Thus, we propose using a MPSOG to improve the guidance performance. The MPSOG uses a Kalman filter to predict a target's dynamic. The lateral acceleration commands of the y-and z-axis are optimized by the particle swarm optimization (PSO) algorithm, respectively. Numerical simulation results show that the MPSOG have better guidance performance than proportional navigation guidance and particle swarm optimization guidance in miss distance, time-to-go, and final lateral acceleration commands.

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Section: Submitting the Manuscriptmentioning

confidence: 99%

The Design of a Modified PSO Guidance Law Using Predictor and LOS Rate Evaluation

Lee

Chen

Chen³

et al. 2016

MATEC Web Conf.

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show abstract

“…This is incorporated in every interceptor [1] designed to minimize the vehicle-target distance. This is incorporated in every interceptor [1] designed to minimize the vehicle-target distance.…”

Section: Introductionmentioning

confidence: 99%

On an Optimized Fuzzy Supervized Multiphase Guidance Law

Rezaee

Seyedtabaii

2016

Asian Journal of Control

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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.

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“…Rajasekhar et al [18] use fuzzy logic to change the gain of the proportional navigation guidance (PNG) law. The fuzzy-based PD controller was also used extensively in the design of guidance laws where the line of sight (LOS) angle and change of LOS angle rate can be used as input linguistic variables, and the lateral acceleration command can be used as the output linguistic variable for the fuzzy guidance scheme.…”

Section: Introductionmentioning

confidence: 99%