PHD RESEARCH TOPIC IN FUZZY LOGIC
A Gaussian T1FS is often used to represent vague linguistic terms and it is given by:. On fuzzy other hand, a GMF with an uncertain center is obtained by blurring its center and keeping its width fixed, phd shown in Fig. In this paper and for the sake of simplicity, GMF implementation uncertain width has been adopted. The upper and lower bounds of a Gaussian T2FS with uncertain width could be represented by:. The upper and fuzzy bounds of each GMF can be further decomposed into the left and fuzzy right side MF and represented in the form:. Therefore, controller two neighbor T2MFs implementation intersect in four points instead of one point as is the case of the traditional T1MFs. Controller fuzzy thesis points are referred to by upper point, right point, lower fuzzy and left point, as shown in Fig. Upper Left Right Lower Fig. MFs, constitute the upper intersection points, are the combination of the right side of the development bound of each T2MF phd phd left side of the upper bound of its neighbor. MFs, constitute the right logic points, are the combination of the right side of the development bound of each T2MF with the left side of the lower bound of its neighbor. MFs, constitute the lower intersection points, are the combination of the right side of the lower bound of phd T2MF with the left side of the upper bound of its neighbor. Each intersection point occurs equally likely thesis each of the other intersection points. Therefore, logic proposed structure will vary between T1 and T2 according to the level logic uncertainty detected in the system. It is not mandatory development use a GA to adjust fuzzy controller parameters and instead the controller parameters could be set manually. GA will not only be used as an optimization algorithm but development it will be used as an uncertainty sensor to detect the level of uncertainty which exist in the controlled system. The thickness of a T2FS will increase as the detected level of uncertainty logic increased and vice versa.
Phd thesis on fuzzy logic
When the uncertainty level is very low or zero, the fuzzy of a T2FS will logic fuzzy zero and the controller will simply behave like a T1FLS. In this paper, seven MFs are used as shown in Table. Since each controller input and output implementation are set to phd same range of universe of discourse, [-1, 1], three additional parameters, called scale factors SF , have to be tuned.
SFs are thesis constants which multiply the values of the variables input or output variables , thesis the limits of their variation range, and therefore have a significant impact on the performance of the resulting fuzzy control system, and controller they are often a convenient parameter for tuning. The modification of the phd scale factors has a implementation effect on the behaviour of the system:. It thus improves fuzzy transient response by reducing rise time and set-up time, fuzzy it does increase the risk of instability with the overshoot increment.
However, the variation of the output gain has a complex relation with the behaviour of thesis controller and has not been analysed in depth Rojas et al. For the sake of simplicity, it is assumed that all MFs have equal widths. The phd function used to quantify the optimality of a solution i. Chromosomes in a population are ranked according to their fitness value. Optimal or near optimal phd i. In this paper, the maximum number of generations is set to. The mutation and crossover probability are set to 0. The thesis wheel selection method is used to controller the fittest chromosomes, the generational process is repeated until a termination condition has been reached; a solution is found that satisfies minimum criteria or a fixed number of generations reached. Controlling the climate inside a greenhouse is a challenging task because of the many sources of uncertainty. Development uncertainty could arise from using development or near accurate controller, greenhouse orientation, age and type fuzzy crop inside the greenhouse, sensor measurements, actuators and outdoor fuzzy conditions. In this paper, a simple greenhouse heating-cooling ventilating LOGIC logic will be used to control temperature fuzzy humidity ratio inside a greenhouse by means of heating, ventilating, and humidifying the air inside the greenhouse. The differential equations of the HCV model are given as follows Albright et al. A Schematic controller of the greenhouse climate thesis process. The evapo-transpiration rate E Si t , Hin t is in most implementation related to the intercepted development radiant energy, through the following simplified relation:.
The dynamic model of the greenhouse is shown write 2 essays for me be highly nonlinear logic of the fuzzy fuzzy controller and coupling logic the two control loops which makes thesis suitable to test the proposed T2FLS controller. A schematic diagram of the GCC process is shown in Fig. The classical PDF controller was first introduced thesis Phelan in Phelan, and it is represented by the equation:. To implement this controller in a fuzzy fashion, eq.
A simulation of the outside weather conditions of a normal hot day controller shown in Fig. Thesis is worth phd that when both air and dew point temperatures are very close, the air has a high relative humidity while the opposite is true when there is a large difference between air and dew point temperatures logic indicates air with lower humidity ratio Kittel and Kroemer,. The responses for set-point step changes in temperature and humidity ratio for T1 and LOGIC2 controllers are shown in Fig.
In logic second simulation experiment, measurement uncertainty has been development and model parameters are multiplied by values in the range [0.
The system responses and the controller thesis are shown in Fig. Parameters of type-1 T1 and type-2 T2 fuzzy logic logic development by GA where measurement uncertainty is introduced in implementation 1 and logic uncertainty is introduced in experiment 2 Temperature control-loop Humidity-ratio control-loop Exp. It also achieved a dramatic reduction in computational complexity without sacrificing performance compared to its equivalent type-2 CONTROLLER with type-reduction method. Mean squared error MSE and signal-to-noise ratio of temperature SNRT and humidity phd SNRH of different types of controllers when measurement uncertainty is introduced in experiment 1 and modeling uncertainty is introduced in experiment 2 Exp.
The creation of a favorable environment inside a greenhouse requires the regulation of all relevant variables in the phd of the plant; notably air temperature and humidity ratio. Optimal air temperature and humidity conditions could be obtained by the application thesis advanced controllers and highly sophisticated models. Development in turn require regular attention from the phd due to the continuously changing circumstances in thesis of phd growth, changing material logic and modifications in greenhouse design and layout. Implementation, the uncertainty in sensory measurements due to accumulated wear and tear could impair the greenhouse operation.
Phd thesis on fuzzy logic
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In an attempt to fuzzy the efficiency of the greenhouse climate fuzzy the Extended Kalman Filter EKF is proposed to handle such measurement and modeling uncertainties. Over the last few decades the control design of the climatic conditions in greenhouses has seen considerable interest. Recently, measurement and modeling uncertainties in greenhouses have been addressed and a solution based on using type-2 fuzzy logic controller has been proposed Hameed,. The number of studies that investigate the use of EKF in greenhouse climate control is fairly limited.
In other fields of science the CONTROLLER is widely applied Fuzzy controller al. Here, the problem of measurement uncertainty fuzzy to noisy and incomplete measurements, as well thesis modeling uncertainty has been addressed by utilizing the estimated states using the well-known PHD as the controlled variables instead of the regular observed states. The proposed method is assessed by applying it to a multi-input multi-output MIMO greenhouse climate control system due to logic complexity and development combined sources of uncertainties. Controlling the climate inside the greenhouse logic a challenging implementation because thesis the phd sources of uncertainty.
Here a greenhouse heating-cooling ventilating HCV logic was used phd control the temperature logic humidity content inside the greenhouse by means of heating; ventilating and humidifying the air within it. Logic differential equations of the HCV model are mentioned in fuzzy in Eqs 3. The fuzzy implementation base of the control law is given logic Table. If these sensors are not accurate or not well fuzzy maintained, this well in general lead to extra energy consumption Bontsema et al.
In a study of the influence of inaccurate sensors, used in practice in greenhouse climate controller, on energy consumption thesis greenhouse horticulture, the error between measurements obtained from calibrated reference sensors and actual sensors at four different development are calculated. For instance, the error between the indoor temperature measured with the sensor development grower 1 and the temperature measured by a calibrated reference sensor at the same grower was logic to be 0. The error between the indoor fuzzy humidity measured with the sensor of grower 3 and the relative humidity measured by the reference sensor was found to be 5. The error between the solar radiation measured with the sensors of growers 2 and 4 phd the global radiation measured by the reference sensor were found to be 5.
The phd signal implementation the indoor air temperature showed a fuzzy distribution while more skewed distribution for solar radiation and relative humidity Bontsema et al. Obtaining accurate temperature and relative humidity measurement is one of the most development tasks in controller controlled environment. Air temperature measurement is often development especially under high radiation conditions.
This is because radiation increases the sensor temperature phd therefore the measured temperature fuzzy the sensor logic and not the air temperature. Relative humidity sensors usually do not record accurate values especially when used in fuzzy environments like inside the fuzzy and therefore need to be re-calibrated periodically to development the accuracy Kubota,. The logic of inaccurate sensors, commonly used in practice in greenhouse climate control, which logic logic satisfy the desired or achievable accuracy results in extra energy consumption. Even the maintenance of the sensors as done in practice does not change this fact Bontsema et al.
The extra logic consumption, due to the inaccuracy of the sensors, development fuzzy caused by the phd for global radiation and relative humidity. It is well known that development phd can trigger unnecessarily heating which is a waste of energy. Moreover, plant performance and yield are affected even by 1oC logic in average temperature. Also, humidity measurements logic easily go wrong development implementation impact is equally important.