Keywords
electric drive
control systems
transfer function
characteristic polynomial
finite difference method
friction characteristic
friction-induced self-oscillations
neural network
quasi-neurocontroller двомасова електромеханічна система
електропривод
системи управління
передатна функція
характеристичний поліном
метод кінцевих різниць
характеристика тертя
фрикційні автоколивання
нейронна мережа
квазінейрорегулятор
How to Cite
Abstract
This paper proposes a synthesis methodology for an electromechanical system controller that ensures the required control performance. When linearizing individual sections of a nonlinear load, the structure of the controller resembles a neural network, leading to its designation as a quasi-neurocontroller (QNC). Unlike a PERCEPTRON-type neural network, the proposed controller does not include hidden layers, and its weight coefficients, instead of being determined through multiple iterative calculations (105 – 106), are derived analytically. These relationships are universal and valid for any linearized section. The use of generalized dimensionless parameters makes these relationships applicable to a wide range of electric drives used in machines and mechanisms. A modified set of dimensionless generalized parameters has been introduced, allowing for a simple transition to expressions for systems with absolutely rigid couplings. One of the key advantages of the proposed controller is that it eliminates the need to measure hard-to-obtain system coordinates, such as elastic torque and the need of state observers. Instead, it operates using only a single output variable. The essence of quasi-neurocontrol consists in the construction of the feedback transfer function in the form of a polynomial complement, which ensures that the characteristic polynomial of the closed-loop system matches a polynomial with the desired root locations. To compute the required derivatives, the finite difference method is proposed for use within the quasi-neurocontroller. The methodology of quasi-neurocontrol is illustrated by the example of eliminating friction-induced self-oscillations in a two-mass electromechanical system with a quasi-neurocontroller and a nonlinear frictional load. References 23, figures 11.
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