In this paper, we present the design of an innovative Ultra-Wideband (UWB) ring patch antenna customized for 28 GHz, 60 GHz and 80 GHz and 5G mm-Wave communication. The antenna features a ring patch with an embedded small rectangular patch and a partial ground plane. To optimize the antenna dimensions for resonance at 28 GHz, 60 GHz, and 80 GHz, a Multilayer Perceptron Neural Network (MLPNN) was employed with a k-fold cross-validation technique. Simulation results demonstrate that the MLPNN-optimized antenna achieves superior performance metrics, including a low reflection coefficient, efficient radiation patterns, and a wide bandwidth of 13.2 GHz at 28 GHz and an extended bandwidth of 53 GHz spanning from 47 GHz to 100 GHz. These characteristics highlight its potential as a robust solution for next-generation 5G communication systems.

This paper proposes a robust control approach for autonomous vehicle path-tracking based on sideslip angle estimation. First, artificial intelligence techniques, namely artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS), are employed for sideslip angle estimation. The optimal model structure is determined based on statistical error metrics, including mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE). Next, the controller is designed using a Takagi–Sugeno fuzzy multi-model combined with a Line Integral Lyapunov Function (LILF) and H∞ performance, leading to a convex optimization problem formulated as linear matrix inequalities (LMI), from which optimal gains are obtained. The approach is validated through MATLAB/Simulink and CarSim co-simulations under various scenarios and compared with Sliding Mode Control (SMC), LQF-based control, and PID methods. Results show that the ANFIS-based method achieves better accuracy and robustness across different conditions.

This paper presents the design of a compact Planar Inverted-F Antenna (PIFA) operating within the 1.6–3 GHz range, making it well-suited for various wireless communication applications, including 3G, LTE, the Industrial, Scientific, and Medical (ISM) band, Wi-Fi, Bluetooth, and WLAN IEEE 802.11b/g standards. Furthermore, its broad operational bandwidth fully encompasses key 5G sub-6 GHz frequency bands such as n7 (2600 MHz), n38 (2600 MHz), n41 (2500 MHz), n65 (2100 MHz), n70 (2000 MHz), n80 (1800 MHz), n84 (2100 MHz), and n90 (2500 MHz), ensuring seamless integration into new-generation 5G networks while enhancing connectivity and communication efficiency. The intricate task of PIFA antenna design, with its multifaceted parameters, is often hindered by the absence of comprehensive mathematical models. To address this challenge, our research presents a pioneering synthesis and analysis model based on the powerful Adaptive-Network-based Fuzzy Inference System (ANFIS). Leveraging a hybrid learning process, this approach establishes precise input-output mappings through fuzzy if-then rules and specific data pairs. The ANFIS methodology adeptly forecasts the antenna’s resonance frequency, bandwidth, and physical characteristics, with the subtractive algorithm emerging as a streamlined parameter-efficient choice. Utilizing HFSS software, we successfully engineer a single-band PIFA antenna achieving a measured bandwidth of 1.2 GHz and a gain of approximately 6.5837 dB at the desired 2.45 GHz frequency. Comparison between ANFIS predictions and HFSS software simulations underscores the method’s accuracy and highlights the pivotal role of ANFIS as a dependable tool for determining geometric dimensions across diverse antenna types, opening new vistas in wireless communication technology. Furthermore, the proposed PIFA antenna was fabricated and measured, showing good agreement between the simulated and measured results.

In this paper, we are interested to design a compact single band PIFA antenna using the artificial neural networks (ANN) based on the multilayer perceptrons (MLP). The designed antenna will operate at the frequency 2.45 GHz for ISM (Industrial, Scientific and Medical) band, the medical field, the mobile phone,the Wi-Fi and the Bluetooth. The absence of mathematical models that takes into account all the parameters that affect the characteristics of these antennas present a difficulty in the design of this type of antennas. In this paper, our main contribution is the development of a synthesis and analysis model for PIFA antenna based on the artificial neural network method. For this reasons, we have developed a model of the neural network based on the multilayer perceptron to predict the resonance frequency and the bandwidth of a single band PIFA antenna. By applying the same method, we managed to find a multilayer perceptron structure that can accurately predict the dimensions of the PIFA single band antenna. Using the HFSS software, we designed the single band PIFA antenna that can operate at the frequency 2.45 GHz and presents a bandwidth at -10dB equal 1.0552 GHz, a good reflection coefficient ( −51 dB), the gain is 6.5867 dB.