Sensor Less Control Of Induction Motors By Artificial Neural Networks

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Letters to the Editor________________________________________________________________ Sensorless Control of Induction Motors by Artificial Neural Networks J. R. Heredia, F. Perez Hidalgo, and J. L. Duran Paz

Abstract—In this letter, we propose a voltage-source inverter control working in the open loop of an induction motor measuring the stator current and using an artificial neural network. This technique has the mission to estimate the speed and torque of the rotor without using sensors. With this, a simple and cheap method of control is obtained, with as much precision and robustness as other more complex ones. Index Terms—Artificial neural networks, induction motors, sensorless control.

I. INTRODUCTION It is well known that induction motors are displacing dc motors in many applications where the latter are normally used. This applications are those that require a good speed regulation. Induction motors are machines relatively cheap and robust since, for their construction, neither slip rings nor collectors are needed. These features are very interesting in the applications where a speed control is required. The control of induction motors is possible thanks to advances in the production and design of power electronic devices as well as to the improvements reached in the control of this devices. There are different control strategies as well as different types of drivers used depending on the desired power, robustness, and control degree of the machine. Most of the drivers used in industrial applications are based on scalar (V/Hz) control using voltage-source inverters (VSIs). They are used in applications where great speed precision is not required (pumps, fans, simple elevators, etc.). For other applications of high precision and resolution, scalar control in a closed-loop mode and, more and more, vector control, is used. The scalar control VSI in open loop has the disadvantage of not being precise since it only varies the relationship V=f (voltage/frequency) of the input without having information of the rotor speed and torque. The object of this work is to improve this control type (VSI in open loop) by means of artificial neural networks (ANNs). With this, we try to get an easy and cheap method of control with as much precision and robustness as others working in closed loop [1], [2]. The scheme control proposed consists of estimating the torque and speed of the machine without using tachometers or measurement of torque while the machine is working. The ANN receives the current and voltage/frequency relationship of the stator as inputs and it is trained for a collection of loads. The answer of the net will generate the estimate of the speed and torque. II. ESTIMATION OF THE SPEED BASED ON ANNS The neural network technique is based on a learning process [3]. Neural networks have the advantages of extremely fast parallel comManuscript received January 22, 2000; revised April 1, 2001. Abstract published on the Internet July 31, 2001. J. R. Heredia is with the Departamento de Tecnología Electrónica, Universidad de Málaga, 29013 Málaga, Spain (e-mail: juan @dte.uma.es). F. Perez Hidalgo and J. L. Duran Paz are with the Departamento de Ingeniería Electrica, Universidad de Málaga, 29013 Málaga, Spain (e-mail: [email protected]; [email protected]). Publisher Item Identifier S 0278-0046(01)08788-3.

putation and fault tolerance characteristics due to distributed network intelligence. Many neurons or processing elements are interconnected to form a parallel neurocomputing network. Each element or neuron is a very simple processor that carries out the pondered sum of its inputs and applies them to a function (linear, sigmoidea) to generate an output that is sent to another neuron. The most usual type of ANN is the feedforward multilayer one, where no information is fed back during the recall process. Feedback signals are used only during the training of the neural network. Generally, the backpropagation method is used for adjusting the neural network weights during the training. This process requires a high consumption of time since the algorithm takes a long time to converge to the desired error, but this phase is usually made offline. By using an ANN, it is possible not to depend on aproximate models since the net learns with the complete model [3]. The use of a neural network to estimate the speed consists of an association of some inputs (stator currents, voltage, and frequency) with some outputs (speed and torque). In this case, for each set of inputs there is a set of outputs. To accomplish this operation, the net will have to be trained in a first phase. It is not necessary to carry out this phase in real time and to give the net all the possible inputs–outputs combinations since it has the capacity to generalize results starting from a limited set of inputs–outputs. Once the phase of training has been accomplished, the net is prepared to estimate the speed for any set of inputs. During the training, we have experimented with different architectures and learning methods. The objective is to find a compromise between the convergence speed and the target error. The most significant points to keep in mind when defining the structure and operation of the neural net are mainly the choices for the inputs and the outputs. Some inputs should be chosen that determine completely the state of the asynchronous motor; also, for each input there will be only one output. The inputs in the motor must be easy to measure, so that the necessary hardware to control the machine will be simplified. It is well known that the higher the load of a motor, the more current it consumes, and vice versa. Therefore, a variable that can be appropriate to detect the state of the motor is the stator current I1 . This parameter defines the load of the motor perfectly (resistant torque) and, so, combining the variables (V1 =f1 ) with I1 , it is possible to determine the operation point of the motor (speed and torque) independently of the load. It is necessary to highlight that (V1 =f1 ) and I1 are values that are relatively easily obtained during the operation of the motor. The first value is obtained from the VSI drive and the second by means of a current transducer based on the Hall effect. With all these considerations, the ANN will have the stator current and V=f relationship as inputs and the speed and motor torque as outputs. The final structure of the ANN used is that of a feedforward net with three layers, the first one formed by two neurons (inputs I1 and V1 =f1 ), the second one by ten neurons to reach the objective of the stipulated error (<1003 ), and the third one by two neurons to give the speed outputs and estimated torque. The way of training the ANN consists of taking training data corresponding to the whole range of motor operation. For example, if the motor is loaded with an specific load, we should vary the frequency from 0 Hz until a maximum frequency with small increments. We will then obtain a data vector containing the speed and stator current. We will use this vector to train the ANN. The last operations will be carried out for different loads. To cover the whole range of operation of

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Fig. 1. Block diagram of the control using the speed and torque estimated by ANN.

Fig. 2. Response of the motor. (a) Response for a constant load and for a speed target of 1000 r/min. (b) Response for different loads and speed (1000 and 800 r/min). (c) Response of the motor to a Foucault brake and the speed error.

the motor, we will choose a significant group of loads that allows the ANN, once it has carried out the training, to generalize for all the possible loads and speeds.

III. RESULTS AND CONCLUSIONS A block diagram of the control using the information estimated by the ANN is shown in Fig. 1. The control model proposed has been

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carried out on an induction machine of 220 V and a power of 600 W. The control strategy has been implemented using the tools of Matlab (toolbox for neural nets) and using a data acquisition card as interface between the PC and the system formed by a power inverter and the motor. The network has been trained with 20 different loads and a range of speed of 0–1500 r/min. A later implementation of the system using a microcontroller (H83644 by Hitachi) to implement the ANN, and an analogical circuitry to carry out the proportional integral derivative (PID) control, has allowed the extraction of information about the calculation times that are around 200 ms to carry out the estimate of the speed and torque. A big improvement would be to carry out an analog implementation of the whole system, using operational amplifiers as summers; the resistances would be the weights of the net; in this case the calculation times will be around some microseconds. Concerning the sampling times, they will be limited by the conversion speed of the analog–digital converters and, in the case of a completely analog solution, for the response time of the system. Results are very satisfactory as we can see in Fig. 2(a)–(c). They all show the rotor and estimated speed by the ANN when the speed reference changes. Fig. 2(a) represents the motor response for a speed of 1000 r/min and a constant load, belonging to those that were used in the training phase. Fig. 2(b) shows the same as Fig. 2(a), but for speed changes and loads without previous training; overdamping shown in Fig. 2(b) is due to the motor inertia and

PDI control. Finally, in Fig. 2(c) is shown the control response for speed variations, using a Foucault brake as a load. The most interesting conclusion of all the tests carried out is that the motor response and the one estimated by the net are quite similar, and there is nearly no error in the steady state. That shows the capacity that the model has to generalize and to adapt itself to situations not contemplated in the training phase. The main advantages of controlling an induction motor with ANNs are the following: 1) more accurate models without having to use approximations; 2) the neural network learns the real motor behavior, more accurately than the approximate one; and 3) once the learning is accomplished, in the operation phase it is only neccesary to make sums and multiplications to estimate the speed and torque, and they can be made in real time. REFERENCES [1] M. T. Wishart and R. G. Harley, “Identification and control of an induction machine using artificial neural network,” in Conf. Rec. IEEE-IAS Annu. Meeting, Toronto, ON, Canada, Oct. 1993, pp. 703–709. [2] B. Burton et al., “Implementation of a neural network to adaptively identify and control VSI-fed induction motor stator currents,” IEEE Trans. Ind. Applicat., vol. 34, pp. 580–588, May/June 1998. [3] P. Vas, W. Drury, and A. F. Stronach, “A recent developments in artificial intellegence based drives—A review,” in Proc. PCIM’96, 1996, pp. 580–588.