Three-phase induction motor drive requires high accuracy in high performance processes in industrial applications. Field oriented control, which is one of the most employed control schemes for induction motors, bases its function on the electrical parameter estimation coming from the motor. These parameters make an electrical machine driver work improperly, since these electrical parameter values change at low speeds, temperature changes, and especially with load and duty changes. The focus of this paper is the real-time and on-line electrical parameters with a CMAC-ADALINE block added in the standard FOC scheme to improve the IM driver performance and endure the driver and the induction motor lifetime. Two kinds of neural network structures are used; one to estimate rotor speed and the other one to estimate rotor resistance of an induction motor.

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In this article a methodology for constructing a simple servo loop for motion control applications which is suitable for educational applications is presented. The entire hardware implementation is demonstrated, focusing on a microcontroller-based (µC) servo amplifier and a field programmable gate array-digital signal processor (FPGA-DSP) motion controller. A novel hybrid architecture-based digital stage is featured providing a low-cost servo drive and a high performance controller, which can be used as a basis for an industrial application. Communication between the computer and the controller is exploited in this project in order to perform a simultaneous adaptive servo tuning. The USB protocol has been put into operation in the user front-end because a high speed sampling frequency is required for the PC to acquire position feedback signals. A software interface is developed using educational software, enabling features not only limited to a motion profile but also the supervisory control and data acquisition (SCADA) topology of the system. A classical proportionalintegral-derivative controller (PID) is programmed on a DSP in order to ensure a proper tracking of the reference at both low and high speeds in a d.c. motor. Furthermore, certain blocks are embedded on an FPGA. As a result, three of the most important technologies in signal processing are featured, permitting engineering students to understand several concepts covered in theoretical courses.

Three-phase induction motor electric parameter estimation has been widely used to improve induction motor control performance. A precise match between electrical parameter values and estimated ones is imperative. A value deviation can make induction motor misbehave, which can cause motor overheating even instability. Parameter estimation can be achieved on-line or off-line way with a large number of methods developed to calculate magnetic flux, motor speed, rotor resistance and rotor time constant. These methods include observers, adaptive systems, spectral analysis and artificial intelligence such as neural networks and fuzzy logic. This paper is focused on a hybrid neural network proposed to obtain rotor resistance and speed values, using Texas Instrument development tools to improve a sensorless vector control scheme an improve motor performance.