Search

Distributed solar photovoltaic (PV) generation is growing rapidly around the world. However, unlike conventional synchronous generators, PV systems do not have any rotating masses to deliver inertia to support the grid frequency. The paper presents a detailed modeling of a new converter configuration and control scheme to enable PV systems to adjust the real power output and contribute to the grid frequency regulation. The proposed topology consists of a two-stage converter without an energy storage system. A DC-DC buck converter is used instead of a DC-DC boost converter, and this simplifies the control scheme which aims to keep the PV generator power in the right side of the P-V characteristic and can be varied in the range from near-zero to the maximum power. The proposed control scheme combines robust and nonlinear sliding mode theory with fuzzy logic. The PV system is connected to a low inertia microgrid and its ability to contribute to frequency regulation is assessed for different controls. The proposed converter and its control are validated experimentally on a 3-kW PV system using OPAL-RT real-time simulator and tested under varying temperature, solar irradiance, and partial shading conditions. The results show that with the proposed circuit, the operating point is always on the right side of the P-V characteristic irrespective of the operating mode. Furthermore, the proposed control scheme provides PV generators with a fast and effective inertial response to support the grid and enhance its stability during contingencies. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for the compensation of nonlinear friction in a robot joint structure based on a fuzzy local modeling technique. To enhance the tracking performance of the robot joint, a dynamic model is derived from the local physical properties of friction. The model is the basis of a precompensator taking into account the dynamics of the overall corrected system by means of a minor loop. The proposed structure does not claim to faithfully reproduce complex phenomena driven by friction. However, the linearity of the local models simplifies the design and implementation of the observer, and its estimation capabilities are improved by the nonlinear integral gain. The controller can then be robustly synthesized using linear matrix inequalities to cancel the effects of inexact friction compensation. Experimental tests conducted on a robot joint with a high level of friction demonstrate the effectiveness of the proposed fuzzy observer-based control strategy for tracking system trajectories when operating in zero-velocity regions and during motion reversals. [ABSTRACT FROM AUTHOR]

A stabilizing observer-based control algorithm for an in-wheel-motored vehicle is proposed, which generates direct yaw moment to compensate for the state deviations. The control scheme is based on a fuzzy rule-based body slip angle (β) observer. In the design strategy of the fuzzy observer, the vehicle dynamics is represented by Takagi-Sugeno-like fuzzy models. Initially, local equivalent vehicle models are built using the linear approximations of vehicle dynamics for low and high lateral acceleration operating regimes, respectively. The optimal β observer is then designed for each local model using Kalman filter theory. Finally, local observers are combined to form the overall control system by using fuzzy rules. These fuzzy rules represent the qualitative relationships among the variables associated with the nonlinear and uncertain nature of vehicle dynamics, such as tire force saturation and the influence of road adherence. An adaptation mechanism for the fuzzy membership functions has been incorporated to improve the accuracy and performance of the system. The effectiveness of this design approach has been demonstrated in simulations and in a real-time experimental setting. [ABSTRACT FROM AUTHOR]