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In this paper, a simple 4-dimensional hyperchaotic system is introduced. The proposed system has no equilibria points, so it admits hidden attractor which is an interesting feature of chaotic systems. Another interesting feature of the proposed system is the coexisting of attractors where it shows periodic and chaotic coexisting attractors. After introducing the system, the system is analyzed dynamically using numerical and theoretical techniques. In this analysis, Lyapunov exponents and bifurcation diagrams have been used to investigate chaotic and hyperchaotic nature, the ranges of system parameters for different behaviors and the route for chaos and coexisting attractors regions. In the next part of our work, a synchronization control system for two identical systems is designed. The design procedure uses a combination of simple synergetic control with adaptive updating laws to identify the unknown parameters derived basing on Lyapunov theorem. Microcontroller (MCU) based hardware implementation system is proposed and tested by using MATLAB as a display side. As an application, the designed synchronization system is used as a secure analog communication system. The designed MCU system with MATLAB Simulation is used to validate the designed synchronization and secure communication systems and excellent results have been obtained.

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Finding accurate mathematical model of electrical equivalent circuit of solar photovoltaic (PV) cell is crucial to achieve and improve maximum power point, simulation design and efficiency computations for solar energy system. Due to the nonlinearity of the characteristic of solar PV cell, optimization methods are the best for estimating the electrical model parameters which lead to accurate estimating I-V curve. In this paper, camel behavior search algorithm is proposed as a new method for estimating the five different parameters for single diode model of PV solar module. This is tested on multicrystalline KC 200GT PV module. A measurement data of the module is used to verify and test the consistency of accurately estimating the set of parameters that govern the characteristics I-V relationship of solar cell. The simulation results show that the current-voltage characteristic and power-voltage curve obtained are matching to the measured experimental data set. For performance evaluation, the proposed method is simple, fast in convergence response and has an acceptable accuracy in obtaining the five estimated parameters.

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This paper proposes an approach to tune an Adaptive Neuro Fuzzy Inference System (ANFIS) inverse controller using Iterative Learning Control (ILC). The control scheme consists of an ANFIS inverse model and learning control law. Direct ANFIS inverse controller may not guarantee satisfactory response due to different uncertainties associated with operating conditions and noisy training data. In this paper, the ILC makes a class of self tuning to the inputs of ANFIS inverse controller to minimize the overall system error so that the performance iteratively gets improved. The proposed scheme is simple, effective and lays out a unique tuning procedure for designing ANFIS inverse controller through ILC process.

The output of photovoltaic cells continues to change with surrounding environments, therefore, the maximum power point of the solar cell relies on the amount of solar irradiation and environment temperature. Maximum Power Point Tracking (MPPT) technology is utilized in photovoltaic systems to take entire advantage of output power for Photovoltaic cells. Power Inverters also is an important side of Photovoltaic power generation. This paper proposed a Perturb and Observe (P & O) MPPT algorithm with space vector pulse width modulation (SVPWM) control method for a three-phase complete stand-alone Photovoltaic generation system. The proposed PV generation system is implemented into Matlab / Simulink. Simulation results show the proposed stand-alone Photovoltaic system can achieve the excellent execution of MPPT and get the output voltage in high quality. The system is tested and verified using a solar cell Kyocera Solar KD215GX-LPU PV module.

Biodiesel reactor is the heart of biodiesel system. These reactors involve a highly complex set of chemical reactions and heat transfers. The high nonlinearity requires an efficient control algorithm to handle the variation of operational process parameters and the effect of process disturbances efficiently. In this paper, Fuzzy logic and Adaptive controllers are compared for advance microwave biodiesel reactor. The process control is complex and nonlinear, the Adaptive control have longer time and unreliability in dealing with the system parameters including temperature, microwave power, liquid flow rate as well as the prediction of chemical reaction. The proposed fuzzy logic control will provide precise temperature control and faster warm-up phase with quicker response to disturbances with minimal overshoot and undershoot where Adaptive control techniques can not meet these extra challenges. A closed loop fuzzy and adaptive controllers are used to automatically and continuously adjust the applied power of microwave reactor under different perturbations. Labview based software tool will be presented and used for measurement and control of the full system, with real time monitoring.