Design of Nonlinear Backstepping Control Strategy of PMSG for Hydropower Plant Power Generation

In this paper, renewable hydropower plant generators with permanent magnet synchronous generator are coupled via a diode bridge rectifier - DC/DC boost converter and three-phase inverter to a power grid. This paper studies a new control structure focused a backstepping control of the energy generation system.The proposed methods for adjusting the active and reactive power by adjusting the currents, the DC bus voltage on the main side converter, as well as the voltage at the output of the DC-DC boost converter. The main objective of this control is to obtain purely sinusoidal and symmetrical grid current signals, to suppress oscillations in reactive power and to cancel active power chattering in the event of grid imbalance. In order to optimize the energy flow in the different parts of the production process, an energy control algorithm is developed in order to attenuate the fluctuations in the water flow, the grid system of the hydropower plant considered has been implemented in Matlab/Simulink, the results show the effectiveness of the proposed method. To analyze our approach, a prototype is modeled, simulated and can be performed in an experimental test setup.

Nonlinear Backstepping Control Design using a High Gain Observer for Automatic Gauge Control

The accuracy and quality of the steel strip exit thickness depends on the structure of the automatic gauge control system (HAGCS) of reversible cold rolling mill. This structure is based on the position control of the work rolls. The design and implementation of a new HAGCS by the backstepping approach with high gain observer are discussed in this paper. Backstepping controller of HAGCS and high gain observer (HGO) has been implemented using MATLAB/SIMULINK software. The simulation results show the effectiveness of the proposed control for improving the quality of the output strip

Adaptive nonlinear backstepping control using mended recurrent Romanovski polynomials neural network and mended particle swarm optimization for switched reluctance motor drive system

A switched reluctance motor (SRM) drive system has highly nonlinear uncertainties owing to a convex construction. It is hard for the linear control methods to achieve good performance for the SRM drive system. An adaptive nonlinear backstepping control system using the mended recurrent Romanovski polynomials neural network and mended PSO with an adaptive law and an error estimated law is proposed to estimate the lumped uncertainty and to compensate the estimated error in order to enhance the robustness of the SRM drive system. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the mended recurrent Romanovski polynomials neural network and the error estimated law are established. Furthermore, to help improve convergence and to obtain better learning performance, the mended particle swarm optimization (PSO) algorithm is utilized for adjusting the two varied learning rates of the two parameters in the mended recurrent Romanovski polynomials neural network. Finally, some experimental results and a comparative analysis are verified that the proposed control scheme has better control performances for controlling the SRM drive system.