Quality control of binary distillation columns via nonlinear aggregated models
Automatica (Journal of IFAC)
Nonlinear systems analysis (2nd ed.)
Nonlinear systems analysis (2nd ed.)
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Design with Microprocessors for Mechanical Engineers
Design with Microprocessors for Mechanical Engineers
Adaptive control of a nonlinear dc motor drive using recurrent neural networks
Applied Soft Computing
Single-chip fuzzy logic controller design and an application on a permanent magnet dc motor
Engineering Applications of Artificial Intelligence
A neural network model-based observer for idle speed control of ignition in SI engine
Engineering Applications of Artificial Intelligence
Learning PID structures in an introductory course of automaticcontrol
IEEE Transactions on Education
Engineering Applications of Artificial Intelligence
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The purpose of this paper is to present a simple neuro-control law in order to control a geared DC motor. The main advantage of this controller is that it does not require an exact knowledge of the values of the motor parameters. The proposed artificial neural network is characterized by two input synaptic weights, two output synaptic weights and one threshold; these parameters are used to define the performance of the closed loop system. The DC motor parameters, the synaptic weights and the ANN threshold are combined in order to construct an off-line learning condition. Such condition guarantees that the seminorm of the regulation error remains bounded (closed loop performance index) and it is constructed through a Lyapunov-like analysis. The neuro-controller is evaluated through numerical simulations and through small-scale laboratory experiments by implementing the neuro-controller with electronic hardware.