The current paper investigates Backstepping controller using Particle Swarm Optimization for Photovoltaic "PV"/Wind hybrid system. The tested system was connected to the grid by three-phase inverter commissioned to address current depending on the grid parameters and still deliver its reactive power to zero. Backstepping control is a recursive methodology that uses Lyapunov function which can ensure the system stability. The best selection of Lyapunov function gains values should give a good result. In most of the literatures, the choice was based on the expertise of the studied system using hurwitzienne method considered as heuristic choice. The aim of this work is to propose an optimization using a powerful method commonly called Particle Swarm Optimization "PSO" able to calculate the gains values depending on the grid parameters by minimizing a selected criterion. The simulation results show that the PSO Backstepping controller gives good results shown in the current injected to grid with a small harmonic distortion despite climate change in the wind speed and the irradiation, which also shows the robustness of the applied control.

This paper presents an application of fractional control scheme named Tilt Integral Derivative (TID) to control a stand-alone hybrid energy system composed of a solar photovoltaic (PV) system and a battery bank (BB). A three-level NPC inverter is inserted in order to increase the efficiency of the energy injected into the AC load. Variation in solar radiation or AC load may cause power imbalance, which leads to variation in DC link voltage. As a solution, a buck-boost converter is connected between the DC link and the battery bank to ensures the transfer of energy in both directions. The parameters of TID controller were tuned using a powerful optimization technique known a Genetic Algorithm (GA) by minimizing the Mean Square Error (MSE) used as a performance index. The effectiveness of the proposed TID controller is demonstrated through a comparison with a conventional Proportional-Integral-Derivative (PID) controller, whose parameters are computed by the pidtool function of the Matlab/Simulink tool where the DC link voltage behavior is previously modeled by a capacitor transfer function. The obtained results show that the proposed TID controller provides a stable DC bus with low chattering, regardless of the rapid irradiation and load changes, when compared to a conventional PID controller.

Dissolved oxygen (DO) concentration is a key variable in the activated sludge wastewater treatment processes. In this paper, an auto control strategy based on Euler method and gradient method with radial basis function (RBF) neural networks (NNs) is proposed to solve the DO concentration control problem in an activated sludge process of wastewater treatment. The control purpose is to maintain the dissolved oxygen concentration in the aerated tank for having the substrate concentration within the standard limits established by legislation of wastewater treatment. For that reason, a new proposed control strategy based on gradient descent method and RBF neural network has been used. Compared with RBF neural network PI control, the obtained results show the effectiveness in terms of both transient and steady performances of proposed control method for dissolved oxygen control in the activated sludge wastewater treatment processes.