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This paper presents a neural network implementation of the delay compensator to reduce the variable sampling to actuation delay effects in networked control systems. The compensator action is based on the knowledge of the sampling to actuation delay affecting the system and the control signal. It can be easily added to an existing control system that does not account for the sampling to actuation...
This paper presents a neuro-fuzzy implementation of the delay compensator to reduce the variable sampling to actuation delay effects in distributed control systems. The delay compensator approach proposed the addition of a compensator to an existing distributed system in order to overcome the control performance degradation that results from the variable sampling to actuation delay that characterizes...
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