A Novel Design Approach to X-Band Minkowski Reflectarray Antennas using the Full-Wave EM Simulation-based Complete Neural Model with a Hybrid GA-NM Algorithm

Abstract

In this work, a novel multi-objective design optimization procedure is presented for the Minkowski Reflectarray RAs using a complete 3-D CST Microwave Studio MWS-based Multilayer Perceptron Neural Network MLP NN model including the substrate constant εr with a hybrid Genetic GA and Nelder-Mead NM algorithm. The MLP NN model provides an accurate and fast model and establishes the reflection phase of a unit Minkowski RA element as a continuous function within the input domain including the substrate 1 ≤ εr ≤ 6; 0.5mm ≤ h ≤ 3mm in the frequency between 8GHz ≤ f ≤ 12GHz. This design procedure enables a designer to obtain not only the most optimum Minkowski RA design all throughout the X- band, at the same time the optimum Minkowski RAs on the selected substrates. Moreover a design of a fully optimized X-band 15×15 Minkowski RA antenna is given as a worked example with together the tolerance analysis and its performance is also compared with those of the optimized RAs on the selected traditional substrates. Finally it may be concluded that the presented robust and systematic multi-objective design procedure is conveniently applied to the Microstrip Reflectarray RAs constructed from the advanced patches.