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Type-2 fuzzy logic systems have recently been utilized in many control processes due to their ability to model uncertainty. This research article proposes the position control of (DC) motor. The proposed algorithm of this article lies in the application of a genetic algorithm interval type-2 fuzzy logic controller (GAIT2FLC) in the design of fuzzy controller for the position control of DC Motor. The entire system has been modeled using MATLAB R11a. The performance of the proposed GAIT2FLC is compared with that of its corresponding conventional genetic algorithm type-1 FLC in terms of several performance measures such as rise time, peak overshoot, settling time, integral absolute error (IAE) and integral of time multiplied absolute error (ITAE) and in each case, the proposed scheme shows improved performance over its conventional counterpart. Extensive simulation studies are conducted to compare the response of the given system with the conventional genetic algorithm type-1 fuzzy controller to the response given with the proposed GAIT2FLC scheme.

Fuzzy logic controllers (FLCs) are usually constructed using type-1 fuzzy sets [

GA was first proposed in 1975 [

Genetic operators such as crossover and mutation are applied to the parents in order to produce a new generation of candidate solutions. As a result of this evolutionary cycle of selection, crossover and mutation, more and more suitable solutions to the optimization problem emerge within the population. Increasingly, GA is used to facilitate FLSs design [

The structure of a type-2 FLS is shown in

An interval type-2 FLS is employed [

of all elements in the FOU (secondary membership grades) area nifty. The inference engine then matches the fuzzy rules in the rule base. To compute unions and intersections of type-2 sets, compositions of type-2 relations are needed. Just as the sup-star composition is the backbone computation for a type-1 FLC, the extended sup-star composition is the backbone for a type-2 FLC [

The output of the inference engine is a type-2 fuzzy et, it must be type-reduced before the defuzzifier can be used to generate a crisp output. This is the main structural difference between type-1 and type-2 FLCs. The most commonly used type-reduction method is the center-of-sets type-reducer, which may be expressed as [

The footprint of uncertainty (FOU) of the membership function (MSF) in the IT2FLS is the area which limited by two MSF, the overhead limitation is the upper membership function UMSF and the down limitation is the lower membership function (LMSF), as shown in

In this study, proposed approach has simulated a DC shunt motor as is shown in

The transfer functions are defined for speed and position control of DC motor, respectively.

where R_{a} is armature resistance, L_{a} is armature inductance i is armature current, V is armature voltage, e_{a} is back emf voltage, K_{e} is back emf constant, K_{m} is torque constant, T_{m} is torque developed by the motor, ω is angular speed of shaft, θ is angular displacement of shaft, J is moment of inertia of motor and load, B is frictional constant of the motor and load. Unlike conventional control, which is based on mathematical model of a plant, a FLC usually embeds the intuition and experience of a human operator and sometimes those of designers and researchers. While controlling a plant, a skilled human operator manipulates the process input (i.e. controller output) based on with a view of minimizing the error within shortest possible time. The controlled variable of fuzzy controller is u(t). Once the fuzzy controller inputs and outputs are chosen, one must think about the membership functions (MSFs) for these input and output variables. In this paper, all membership functions for the conventional fuzzy controller inputs (e and Δe) and the controller output are defined on the common normalized domain [−1, 1]. We use symmetric triangles (except the two MFs at the extreme ends) with equal base and overlap with neighboring MFs. This is the most natural and unbiased choice for MFs. The actual control input voltage for the main fuzzy controller (In the case of PI-type FLC) can be written as

where k is the sampling instant, is the crisp at k sampling instant and is the incremental change in controller output. showed that an IT2 fuzzy-PI (or the corresponding PD) controller is equivalent to a nonlinear PI (or PD) controller with variable gains and control offset [

In this section. The assumed parameters of the electric DC motor represented in the following transfer function.

Simulation experiments under different operation status is carried out based on the fore established model and performance comparison with IT2FLC and conventional type-1 fuzzy controller is made. The two curves in

In this section, DC motor is used in simulation. The genetic algorithm is very useful in optimization technique.

it is apply in this paper of fuzzy type two logic controller in multi position of simulation process, to measured the optimal parameters of fuzzy such as scaling factor input and output data, start-end point of triangular memberships function (bottom triangle) and center of memberships function (top triangle). The number of variable parameters are thirty two parameters.

Initially the motor is operated at the steady state. At the time t = 0.36 sec, an increased step of 25% of initial

set point. As shown in

In this section, we show in

In this paper, The GAFLCT2 has been proposed for position control of DC motor. Performance of the proposed GAFLST2 was also compared with corresponding conventional GAFLCs with respect to several indices such as rise time, settling time, maximum peak overshoot (MP%), integral of absolute error (IAE). and integral time of absolute error (ITAE).

The simulated results show that, using a type-2 FLC in real world applications can be a good option since this type of system is a more suitable system to manage high levels of uncertainty, as we can see in the results shown in Tables 2-5. Simulation results indicate that the performance of the GA FLCT2 will better. That is mean the system will sense for change the value of IAE and ITAE. The results demonstrate that a type-2 FLC can outperform type-1 FLCs that have more robustness design parameters. The main advantage of the type-2 FLC appears to be its ability to eliminate persistent oscillations, especially when unmodelled dynamics were introduced. This ability to handle modeling error is particularly useful when FLCs are tuned offline using GA and a model as the impact of unmodelled dynamics is reduced. The significance of the work is focused to manage the uncertainty of the system. The nonlinear of the systems are big problem therefore the one of successful methods to eliminate or reduce nonlinearity system by using fuzzy type two. It is a good option for real time applications that limited time is needed such as robot system.