Ternary nanocomposites based on multi-walled carbon nanotubes and polyaniline (PANI) wrapped by sulfonated graphene nanopowders (s-GNs) were prepared by an in-situ sequence solution blending followed by ultrasonic dispersion. The role of functionalization s-GNs' surface by methanol and sulfuric acid on the structural characteristics of CNTs 20% @PANI and CNTs 20% /s-GNs 20% @PANI were conducted by X-Ray diffraction, scanning electron microscopy, and Fourier transforms infrared spectroscopy. The results showed less agglomeration and a high interlayer distance between the nanoparticles for the functional groups intercalation which led to immobilize the conductive PANI and prohibit the volume expansion during the charge/discharge cycling of the supercapacitor's electrodes. The sulfonated graphene also improved the interfaces between the electrolyte and the capacitor's electrode and thereby better pseudo-capacitive behavior obtained. Compared to the PANI and CNTs@PANI, the ternary s-GN/CNTs@PANI nanocomposites exhibited the highest conductivity (r) and the total resistivity (q) properties for the increased surface area, hydrophilic oxide surface SO -3 groups of the s-GNs and hence better mobility of the electronic charge carriers and higher capacitance.

Controlling a system is a complicated job, especially when we talk about the nonlinearity of the system introduced by the external changes. This paper presents the procedure of designing, analysis, and verification of nonlinear autoregressive moving average controller (NARMA L2) as an artificial intelligence technique to track the output voltage of a Buck dc/dc converter in comparison with PID controller, digitalized sliding mode controller so as to reduce the ripples in output voltage and to suppress the transient overshoots, or in other words, enhance the transient response diversity of the plant in the case of load and line changes. In this technique, a back-propagation learning algorithm is derived to increase the effectiveness of the proposed controller. Finally, the proposed method of control using a neural network controller is designed using MATLAB/SIMULINK and the results of the converter for the Neuro controller are compared with different techniques of control.

This paper applies an artificial intelligence or fuzzy logic strategy to improve the performance of photovoltaic systems with regard to variations of I-V and P-V characteristics resulting from irradiance and temperature variations that in turn create maximum power point (MPPT) variation, resulting in fluctuation of the DC voltage at the input of the boost step converter. A fuzzy logic controller is used to regulate this output voltage that can cover a wide operating range and does not require exact mathematical modelling, thus being cheap to develop. The performance of the DC converter based on the application of the fuzzy logic strategy was tested using MATLAB/Simulink for different values of input DC voltage within the PV system. The results show a good stabilising ability during irradiation changes and smooth signal output from the converter.

This paper studies the control of the non-linear, time-varying Buck/Buck converter that operates in continuous mode by using the digital sliding mode technology. The main goal is to meet the final objective of achieving acceptable performance and to overcome the system nonlinearity, or in other words, to track the output voltage during load and source diversity. The simulation procedure is based on a small signal state-space model of the Buck/Boost converter in canonical form to achieve the desired operation by using a pulse width modulator to make the system into a stable state, i.e. error signal is zero. This simulation model is preformed in MATLA/SIMULINK to verify the dynamic performance under different operation conditions. All results of the simulation show that this proposed technique both enhances the regulation of the voltage and eliminates the steady-state ripple.