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Depending on ULN model with single neuron controller, the control architectures are introduced and applied to pH neutralization process.
This paper proposes an artificial neuron controller for closed loop speed control of DC drive fed by DC chopper.
The training patterns for the neuron controller are obtained from the conventional PI controller and the effectiveness of the proposed neuron controller is studied using simulation studies.
In this control system, the fuzzy neuron hybrid controller is constructed by the fuzzy PI controller and the neuron controller, the gain of the neuron controller is tuned by using a fuzzy algorithm.
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Both these controllers are trained offline with the data obtained during experimental run of the robot, but the approximation of the activation function generated an error, and therefore, an increasing number of neurons could be worse outcome neuronal controller.
An on-line control of the deposited beads was realized based on a segmented neuron self-learning controller.
Sliding mode control inhibits the parameter perturbations and load disturbance, single neuron PID control realizes the online adjustments of the proportional, integral, differential parameters of traditional PID controller.
To the plant with big uncertainties and grave nonlinearities, the fuzzy neuron hybrid control system is set up and the new model-free controller is designed.
Unlike the previous works on periodic synchronization of neural network, both the neuron activations and controllers in this paper are allowed to be discontinuous.
Two control architectures, model predictive control based on ULN model and single neuron PID (SN-PID) controller based on ULN predictor, are designed to control pH neutralization process, respectively.
The results show that neural controller with separated hidden neurons has a fast response to sensory input.
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CEO of Professional Science Editing for Scientists @ prosciediting.com