# Intelligent Speed Controller For Indirect Computer Science Essay

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This paper presents the development of speed control scheme for indirect field-oriented controlled induction motor drive. The Conventional-PI controller and Fuzzy-PI controller is studied in closed loop speed control. Decoupling of the stator current into torque and flux producing (d-q) currents profile of an IM is involved in the indirect field-oriented control. The current components Iqs and Ids of an IM is developed by an intelligent based Fuzzy PI controller. The speed responses, torque and stator currents were observed under the performance of Fuzzy Logic Controller (FLC) and compared with the PI-Controller. It's provides better dynamic performance. The performance of the controller has been investigated through simulation using MATLAB/Simulink.

Keywords-PI-Controller, Indirect Field-Oriented Control (IFOC), Fuzzy Logic Controller (FLC).

## Introduction

The induction motor is well-known as the workhorse of the industry. The growth of variable speed induction motor drives has a long history of more than four decades. Today's sophisticated industrial drives are the result of the far-reaching research and improvement during the last four decades. The early period of variable speed induction motor drives can be recorded back to the 1960s, with the emerged of the silicon controlled rectifier (SCR). In that period the principle of speed control was based on steady state concerns of the induction machine. The v/f control was one of the techniques and even today it is commonly used for the open-loop speed control of drives with low dynamic requirements. Besides that, another well-known control technique is the slip frequency control method that is well-known to produce better dynamics. This method was taken on in all high performance induction machine drives until field oriented control (FOC) became the industry's standard for AC drives with dynamics close to that of DC motor [1]. Hence so-called vector control or the field-oriented control was one of the most important inventions in AC motor drives which opened the venue for the researchers to embark on R&D program to enhance the control performance. And by other aspects, there are many process control benefits that might be provided by an adjustable speed drive such as, smoother operation, better acceleration control, different operating speeds for each process recipe, compensation of changing process variables, allow slow operation for setup purposes, adjustment to the rate of production, accurate positioning, control torque or tension and energy saving.

In 1965 Fuzzy Logic was presented as a new type of mathematical set approach by Zadah, which consists of the fuzzy set theory that formed the foundation theory of fuzzy logic. The fuzzy control system is based on the fuzzy logic principle that comprises of three main phases: fuzzification, inference engine and defuzzification. The first phase converts the inputs into fuzzy sets. In the second phase, the inference engine defines the fuzzy rules which relate the outputs through specific rules with the inputs sets. The last phase combines the results of the fuzzy rules, and infers the decision, which is then converted from fuzzy sets to a crisp value [2,3].

There are several published control techniques and commercially available tools to controller for VSD to ensure a high degree of reliability and performance. For instance, [4] uses a PLC for controlling an inverter to drive an IM but this method is more for monitoring and protection and there are no control analysis aspects considered. In [5] the system was evaluated when subjected to sudden change in reference and the optimized of PI coefficients were carried out with Ziegler-Nichols method and Genetic-Adaptive Neuro-Fuzzy Inference System (ANFIS) model but with no control analysis. In [6], the work was on the PLC based hybrid-fuzzy control for PWM-driven VSD with constant V/f ratio that depend on s-domain transfer function mathematical model of a real plant. However, the optimizations of the controller's performance against external disturbances were not considered.

In this paper a model of a three-phase induction motor will be derived using mathematical modeling principles. The model will be implemented using MATLAB/Simulink software. Based on indirect field oriented control principles, the controller will be designed and later implemented on the model.

## Indirect vector control

The induction motor dynamics can be modeled in higher order mathematical equations that falls under one of the VSD control classifications.

Steady state models of induction machines are useful for studying the performance of the machine in steady state. This means that all electrical transients are neglected during load changes and stator frequency variations. Such variations arise in applications involving variable speed drives. The variable speed drives are converter fed from finite sources, unlike the utility sources, due to the limitation of the switch ratings and filter sizes. This results in their incapability to supply large transient power. Consequently, there is a need to evaluate the dynamics of converter-fed variable-speed drives to assess the adequacy of the converter switches for a given motor and their interaction to determine the excursions of current and torque in the converter and motor. The related IM parameters mentioned in the Table I.

Parameters

Figure 1 shows the block diagram of the indirect field oriented control model for an induction motor of a proposed method.

Indirect Field Oriented Control model block of a proposed scheme

## mathematical formulas

The dynamic model of the induction motor is derived by using a two-phase motor in direct and quadrature axis. The descriptions of the notation used are given in Table II. The state space model of induction motor in a stationary reference frame can be derived with regards to the voltage and flux linkage equations of induction motor in the arbitrary reference frame [7]. Then finally state space model of induction motor in a stationary reference frame can be written as shown in equations (1)-(6) below:

Nomenclature

d- and q-axis stator current components respectively and expressed in stationary reference frame

d- and q-axis rotor current components respectively and expressed in stationary reference frame

Magnetizing inductance

Self-inductance of the stator and rotor respectively

The resistance of a stator and rotor phase winding respectively

Electromagnetic torque and Load torque reflected on the motor shaft respectively

d- and q-axis stator voltage components respectively and expressed in stationary reference frame

Leakage resistance of the stator and rotor respectively

d- and q-axis stator flux components respectively and expressed in stationary reference frame

d- and q-axis rotor flux components respectively and expressed in stationary reference frame

Mechanical and electrical angular rotor speed respectively

Synchronous speed or dominant frequency

Number of pairs of poles

Operator

The inertia of the rotor

The damping constant which represents dissipation due to windage and friction

## Design of controller

## Conventional PI controller

The conventional PI controller is one of the most common approaches for speed control in industrial electrical drives in general, because of its simplicity, and the clear relationship existing between its parameters and the system response specifications. A very common method to determine the Kp and Ki constants of this controller is the method of Ziegler-Nichols. The conventional PI controller block model is given in Fig.2.

Conventional PI controller

## Fuzzy-PI controller

To determine a fuzzy rule from each input-output data pair, the first step is to find the degree of each data value in every membership region of its corresponding fuzzy domain. The variable is then assigned to the region with the maximum degree.

When each new rule is generated from the input output data pairs, a rule degree or truth is assigned to that rule, where this rule degree is defined as the degree of confidence that the rule does in fact correlate to the function relating voltage and current to angle. In the developed method a degree is assigned which is the product of the membership function degree of each variable in its respective region.

Fuzzy PI controller

Every training data set produces a corresponding fuzzy rule that is stored in the fuzzy rule base. Therefore, as each input- output data pair is processed, rules are generated. A fuzzy rule or knowledge base is in the form of two dimensional table, which can be looked up by the fuzzy reasoning mechanism.

Speed error is calculated with comparison between reference speed and speed signal feedback. Speed error and speed error changing are fuzzy controller inputs. Input variables are normalized with a range of membership functions specified and the normalization factors are named as K1, K2 and K3. Suitable normalization has direct influence in algorithm optimality and faster response. Refere to Fig. 3.

## Membership Functions

The Fuzzy Logic Controller initially converts the crisp error and change in error variables into fuzzy variables and then are mapped into linguistic labels. Membership functions are associated with each label as shown in the Fig. 3 which consists of two inputs and one output.

C:\Users\user\Desktop\Muawia's Desktop\Figures for UTP Conf\Fuzzy\Error.bmp

C:\Users\user\Desktop\Muawia's Desktop\Figures for UTP Conf\Fuzzy\CE.bmp

C:\Users\user\Desktop\Muawia's Desktop\Figures for UTP Conf\Fuzzy\output.bmp

Membership functions

The fuzzy sets are divided into seven groups. They fuzzy sets are defined as follows:

Z: Zero

PS: Positive small

PM: Positive Medium

PB: Positive Big

NS: Negative small

NM:Negative Medium

NB: Negative Big

PVS:Pos. Very small

NVS:Neg. Very small

NVB:Neg. Very Big

PVB: Pos. very Big

Each of the inputs and the output contain membership functions with all these eleven linguistics.

## Rule Base

The mapping of the fuzzy inputs into the required output is derived with the help of a rule base as shown in Table II below.

Rule matrix for fuzzy PI controller

e

NB

NM

NS

Z

PS

PM

PB

ce

NB

NVB

NVB

NB

NB

NM

NS

Z

NM

NVB

NB

NB

NM

NS

Z

PS

NS

NB

NB

NM

NS

Z

PS

PM

Z

NB

NM

NS

Z

PS

PM

PB

PS

NM

NS

Z

PS

PM

PB

PB

PM

NS

Z

PS

PM

PB

PB

PVB

PB

Z

PS

PM

PB

PB

PVB

PVB

## Result Analysis

The simulation results of the PI speed controller and FLC for indirect vector control of induction motor were run for 3 seconds. While the motor starts from standstill at t=0 and reaches the rated speed 313rpm the load torque TL=0 as shown in Figure 5. Figure 6 shows the related torque.

fig

figSpeed response

Torque response

The torque response, shown in the figure 6 reflects the ripples are less for the Fuzzy PI controller compared with Conventional PI controller.

Vds and Vqs are generated from Ids and Iqs are seen as DC while Va and VÎ² that calculated from Vds and Vqs are seen as time varying components, that is why controllers have been designed in the d-q synchronously reference frame, as illustrated in Figures 7, 8, 9 and 10.

fig

Vds response

fig

Vqs response

fig

Va response

fig

VÎ² response

From the above results it is illustrated that the model performs as expected when a test was applied with a PI-controller compare to Fuzzy PI controller.

## Conclusions

In this paper the concept of fuzzy logic has been presented and the SVM based indirect vector controlled induction motor drive is simulated using both PI and Fuzzy PI controller. The results of both controllers under the dynamics conditions are compared and analyzed. The simulation result support that the FLC settles quickly and has better performance than when PI controller.

Acknowledgment

The authors acknowledge the support from the Universiti Teknologi PETRONAS for a financial award from Graduate Assistantship Scheme.