Laboratory implementation of a neural network trajectory controller for a DC motor

Journal Article · Mon Mar 01 00:00:00 EST 1993 · IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers); (United States)

DOI:https://doi.org/10.1109/60.207413· OSTI ID:6528421

Weerasooriya, S; El-Sharkawi, M A ^[1]

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1. Univ. of Washington, Seattle (United States)

The paper describes the laboratory implementation of a neural network controller for high performance dc drives. The objective is to control the rotor speed and/or position to follow an arbitrarily selected trajectory at all time. The control strategy is based on indirect Model Reference Adaptive Control(MRAC). The motor characteristics are explicitly identified through a multi-layer perceptron type neural network. The output of the trained neural network is used to drive the motor in order to achieve a desired time trajectory of the controlled variable. The main feature of the proposed controller is a neural network which captures the unknown inverse dynamics of the motor through a supervised learning algorithm. The noise rejection and knowledge generalization capabilities of the neural network are effectively used in order to achieve a robust controller design applicable in a wide range of operating conditions. Performance of the control algorithm is evaluated through a laboratory implementation. The neural network controller is assembled in a commercially available PC-based real-time control system shell, using software subroutines. An H-bridge, dc/dc voltage converter is interfaced with the computer to generate the specified terminal voltage sequence for driving the motor. All software and hardware components are off the shelf.' The versatility of the motor/controller arrangement is displayed through real-time plots of the controlled states.

• OSTI ID: 6528421
• Journal Information: IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers); (United States), Vol. 8:1; ISSN 0885-8969
• Country of Publication: United States
• Language: English

Related Subjects

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
ELECTRIC MOTORS
CONTROL EQUIPMENT
ALGORITHMS
CONTROL
DESIGN
DIRECT CURRENT
NEURAL NETWORKS
PERFORMANCE
CURRENTS
ELECTRIC CURRENTS
ENGINES
EQUIPMENT
MATHEMATICAL LOGIC
MOTORS
320303* - Energy Conservation
Consumption
& Utilization- Industrial & Agricultural Processes- Equipment & Processes