This paper proposes a new control structure for two multilevel three-phase inverter topologies for photovoltaic (PV) systems connected to the grid. This control scheme includes the use of the space vector pulse wide modulation (SVPWM) technique to control the Diode Clamped Inverter (DCI) and cascade inverter topologies, and the integration of the particle swarm optimisation (PSO) technique to operate the PV system at the Maximum Power Point (MPP). A FPGA implementation of PSO based MPPT is proposed to overcome the problem of MPP tracking under partial shading conditions. This MPPT technique is validated under various PV array configurations in order to evaluate the behaviour of each PV configuration under non-uniform irradiance. A SVPWM control strategy is used in order to generate gate control signals for the inverter and implemented for both DCI and cascade inverter topologies. Then, a comparative study of photovoltaic systems with these inverter topologies is carried out under Matlab/Simulink environment and evaluated on the basis of MPPT, harmonic distortion, cost, advantages and disadvantages. In order to test the practical implementation of the proposed control structure, FPGA/Simulink-based Hardware in the Loop approach has been used to bring the obtained results as close as possible to reality and with a minimum of constraints. Based on the analysis of the obtained results, some experimental parameters are summarized and a comparison table is synthesized.
This work proposes a switched time delay control scheme based on neural networks for robots subjected to sensors faults. In this scheme, a multilayer perceptron (MLP) artificial neural network (ANN) is introduced to reproduce the same behavior of a robot in the case of no faults. The reproduction characteristic of the MLPs allows instant detection of any important sensor faults. In order to compensate the effects of these faults on the robot’s behavior, a time delay control (TDC) procedure is presented. The proposed controller is composed of two control laws: The first one contains a small gain applied to the faultless robot, while the second scheme uses a high gain that is applied to the robot subjected to faults. The control method applied to the system is decided based on the ANN detection results which switches from the first control law to the second one in the case where an important fault is detected. Simulations are performed on a SCARA arm manipulator to illustrate the feasibility and effectiveness of the proposed controller. The results demonstrate that the free-model aspect of the proposed controller makes it highly suitable for industrial applications.
Fault detection in robotic manipulators is necessary for their monitoring and represents an effective support to use them as independent systems. This present study investigates an enhanced method for representation of the faultless system behavior in a robot manipulator based on a multi-layer perceptron (MLP) neural network learning model which produces the same behavior as the real dynamic manipulator. The study was based on generation of residue by contrasting the actual output of the manipulator with those of the neural network; Then, a time delay control (TDC) is applied to compensate the fault, in which a typical sliding mode command is used to delete the time delay estimate produced by the belated signal in order to obtain strong performances. The results of the simulations performed on a model of the SCARA arm manipulator, showed a good trajectory tracking and fast convergence speed in the presence of faults on the sensors. In addition, the command is completely model independent, for both TDC and MLP neural network, which represents a major advantage of the proposed command.
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