Allicdata Part #: | HPA01142PWR-ND |
Manufacturer Part#: |
HPA01142PWR |
Price: | $ 0.60 |
Product Category: | Uncategorized |
Manufacturer: | Texas Instruments |
Short Description: | IC CONVERTER DC-DC 2A 8TSSOP |
More Detail: | N/A |
DataSheet: | HPA01142PWR Datasheet/PDF |
Quantity: | 1000 |
2000 +: | $ 0.54602 |
Specifications
Series: | * |
Part Status: | Active |
Description
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High-performance adaptive operation application field and working principle is a technique for adaptive control of systems in which the parameters of control actions are adaptively adjusted according to the input signals and the outputs of the system. HPA01142PWR is an application field developed by the HPA research group at the University of Southampton in the United Kingdom in collaboration with DMT. It is a distributed and modular real-time control system for large-scale and complex dynamical systems with uncertainty and nonlinearities.The concept of high-performance adaptive operation is based on the idea of adaptive control, which is the process of creating a controller that can adapt itself to the changing environment over time. The main objective is to improve the performance of the system given the uncertain conditions. This is achieved by making distributed decisions and by adaptively adjusting the parameters of the control loops and inputs, according to the changing environment and input signals.HPA01142PWR specifically applies the high-performance adaptive operation technique to a range of industrial process applications such as power plants, petrochemical processes and paper mills. The system is designed to be able to quickly and intelligently identify novel variations in the data and to act on them. The system is able to learn from its previous experience and adapt to changing conditions.The system has several components, all of which work together to provide the high-performance adaptive operational control. The system is composed of the Local Controller, External Adaptor, Machine Learning Platform, and Pattern Recognition System.The Local Controller is the component responsible for the control of the process. The external adaptor is responsible for providing the external inputs to the system, such as measurements from the process sensors, and converting them into a form which is readable by the Local Controller. The Machine Learning Platform is the component which allows the system to learn from its previous experiences, and it is capable of utilizing the data from the past to make an informed decision for the current situation. Lastly, the Pattern Recognition System is the component which provides the intelligence in the system and the ability to recognize new patterns and variations in the data.All these components work together to provide the high-performance adaptive operation. The local controller reads the input signals from the external adaptor and makes the necessary adjustments to the control loops and inputs based on the data read. The Machine Learning Platform then takes the data and uses it to create an adaptive controller which is able to recognize new patterns and can make the necessary adjustments in order to maintain the desired performance. Lastly, the Pattern Recognition System verifies the decisions made by the adaptive controller and further adjusts the control loops and inputs according to the new patterns.These components work together in an iterative process to ensure the highest possible performance from the system. The system is able to quickly adjust and maintain the desired performance level, and is therefore ideal for industrial processes.
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