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Full-state Constraints-based Neuroadaptive Finite-time Control for Induction Motor Drive Systems with Iron Losses

Chen Song, Jinpeng Yu*, Lin Zhao, and Yumei Ma
International Journal of Control, Automation, and Systems, vol. 20, no. 2, pp.637-647, 2022

Abstract : This paper proposes a dynamic surface control-based neuroadaptive finite-time control method for induction motors (IMs) with full-state constraints and iron losses. Firstly, the barrier Lyapunov function is introduced to constrain the state variables of IMs, which ensures that the rotor angular velocity and stator current of motor are always in the given range. Secondly, the neural networks (NNs) and dynamic surface control technology are applied to approximate the unknown nonlinear functions and solve the “complexity of differentiation” problem, respectively. Then the finite-time control technology is utilized to accelerate the response speed of the system and realize the fast and effective tracking of the desired signal. At last, the effectiveness of the method is illustrated by simulation results.

Keyword : Adaptive neural network control, dynamic surface control, finite-time tracking, full-state constraints, induction motors.

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