In this thesis, intelligent controllers are designed to **control** attitude for **quadrotor** UAV (Unmanned Aerial Vehicle).Quadrotors have a variety of applications in real time e.g. surveillance, inspection, search, rescue and reducing the human force in undesirable conditions. Quadrotors are generally unstable systems; the kinematics of **quadrotor** resembles the kinematics of inverted pendulum. In order to avoid the possibility of any kind of damages, the mathematical model of **quadrotor** should be developed and after that, the different **control** techniques can be implemented. This thesis presents a detailed simulation model for a **Quadrotor**. For the **control** purpose, three classical and modern **control** strategies are separately implemented which are PID, **Fuzzy**, and **Adaptive** **Fuzzy** PID for four basic motions roll, pitch, yaw, and Z/ Height. For better performance, error reduction and easy tuning, this thesis introduces individual controllers for all basic motion of a **Quadrotor**. The modeling and **control** is done using MATLAB/Simulink. The main objective of this thesis is to get the desired output with respect to the desired the input. At the end, simulation results are compared to check which controller acts the best for the developed **Quadrotor** model

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The results summarised in Table II show that this initial **fuzzy** system was able to significantly improve over the best fixed length hyper-heuristic for two instances. Being an initial, un-tuned **fuzzy** system to illustrate the potential of parameter **control** using **fuzzy** systems in hyper-heuristic’s, the **fuzzy** system also performed insignificantly better, insignif- icantly worse, and significantly worse for three, four, and three instances respectively. Overall, the **fuzzy** controlled late- acceptance hyper-heuristic was able to perform better for five of the twelve instances. As well as being able to make some improvements over LAHH, the objective function values of the best runs in Table III show that it is able to improve over AdapHH, although median results show that while improving for one instance of the competition, it performed worse for two others, albeit for one of these, it managed to obtain a better best solution than AdapHH. In the CHeSC competition, hyper- heuristics were awarded scores based on their median perfor- mances for each problem instance of each problem domain

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Performance of many tracking **control** systems is limited by variation of parameters and disturbances. This specially applies for direct drive robots with highly nonlinear dy- namics and model uncertainties. Payload changes and/or its exact position in the end effector are examples of uncer- tainties. The **control** methodologies that can be used are ranging from classical **adaptive** **control** and robust **control** to the new methods that usually combine good properties of the classical **control** schemes to **fuzzy** [1,2], genetic al- gorithms [3], neuro-**fuzzy** [4,5] and neural network [6] based approaches. Classical **adaptive** **control** of manipula- tors requires a precise mathematical model of the system’s dynamics and the property of linear parameterization of the system’s uncertain physical parameters [7].

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The **fuzzy** system is comprised of 15 rules (Table I). A rule is defined by three variables C, F, N which relate to the **fuzzy** sets CAL, NFD, and NAL respectively. The rules are defined as IF (CAL = C) AND (NFD = F ) THEN (NAL = N ). When defining the rules of the system, the effects of different list lengths for late acceptance were considered along with what should happen if the search beings to stagnate. A higher value of L causes the search to take longer to converge while a smaller value of L will cause the search to stagnate very quickly. It has previously been shown that a longer convergence time will eventually lead to a better quality solution. Setting this parameter to a high value then would appear to be the best solution however there are other problems concerning the execution time of the hyper-heuristic and the total number of iterations. If the parameter is set too high, then the search would degrade into a random walk with a threshold value equal to the initial solution’s objective value. At any given point of the search, the optimal value of this parameter is then uncertain as to what we should assign it and needs to be controlled.

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Although the difficulties in both understanding and design of the type-2 logic systems comparing to other controllers, the first stays still as a preferred research area in the recent years, due to its robustness through the uncertainties and disturbances. In this sense, PID controller, which is highly sensitive to perturbations and uncertainties, has a drawback and it may cause a performance degradation. In the meanwhile, applied on the same class of systems as described previously, the PID and **fuzzy** **control** have higher tracking errors, especially when disturbances arise. In this study, the proposed designed controllers successfully designed several controllers for trajectory tracking **control** of TRMS model on MATLAB/Simulink, among these designed controllers, an interval type-2 **fuzzy** logic system is presented. According to the results, type-2 **fuzzy** logic controller produce better results than the PID and **fuzzy** type-1 controller in terms of tracking precision in the presence of the disturbances.

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The consolidated three-stage crest **control** swell as observed by the shipboard generator(s) at any single recurrence created by the heap should be not as much as the cutoff points characterized by Figure 3. The subsequent permitted stack profile proposed in Figure 3 has been coordinated to the generator and prime mover execution. Run of the mill gensets' reaction times to a critical load change are on the request of 1.0 to 1.5 sec [15], [16]. In the event that the ascent and fall times for **control** changes (incline rate) seen by the generator are controlled to be slower than the genset's reaction times, the generator and prime-mover **control** circles will have the capacity to keep up the voltage and speed direction, transport aggravations will be kept to a base for such a moderate changing force profile, and sub-synchronous resonances won't be energized in light of the fact that the unsettling influences are at bring down frequencies.

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The invariance property is the motivation of researchers in use of SMC for various applications [4-7] especially is precise systems [8]. The greatest shortcoming of SMC is chattering, the high (but finite) frequency oscillations with small amplitude that produce heat losses in electrical power circuits and wear mechanical parts [3]. Chattering is often due to the excitation of high frequency un-modeled (ignored) dynamics (sensors, actuators and plant) [9]. Excitation of these dynamics is due to two causes: high controller gain and high frequency switching of input **control** signal [9].

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[4]. Han, Bing, Lawu Zhou, Fan Yang, and Zeng Xiang. "Individual Pitch Controller Based on **Fuzzy** Logic **Control** for Wind Turbine Load Mitigation." IET Renewable Power Generation 10.5 (2016): 687-93. Kozak, Peter. "Blade Pitch Optimization Methods for Vertical-axis Wind Turbines." Thesis. ProQuest Dissertations Publishing, n.d. 2016. [5] Jason T Brown. Montana environmental information centre. http://meic.org, 2013.

sampling period, K is the Proportion coefficient, T i is the integral time, and T d is the differential time. Their effects are as follows: (1) The proportion: according to the deviation, the controller adapts the controlled quantity based on the proportion, to reduce the deviation. Proportion coefficient is used to speed up the system response speed, the greater the proportion coefficient, the faster the coefficient of response. If the proportion coefficient is too big, it may cause overshoot, bring oscillation to the system. The smaller the proportion coefficient, the longer time the system takes to be stable, reduce the **control** accuracy, and the static, dynamic characteristics of the system goes bad. (2) The integral: it is used to eliminate static difference, decrease the difference of process value and the setting value. The strength of the integral action depends on the size of the integration time, the smaller the integration time, the greater the integral action, but too much integral action can cause the oscillation of the system. (3) The differential: according to the change trend of deviation value, it regulates the **control** quantity of the system. Before the large changes in the amount of deviation, make the system **control** quantity in advance to change this trend, so as to accelerate the speed of adjustment, and reduce the adjusting time. If differential time is too big, it may cause the system oscillation [3] .

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Abstract—This paper presents a direct **fuzzy** **adaptive** **control** for standalone Wind Energy Conversion Systems (WECS) with Permanent Magnet Synchronous Generators (PMSG). The problem of maximizing power conversion from intermittent wind of time-varying, highly nonlinear WECS is dealt with by an **adaptive** **control** algorithm. The adaptation is designed based on the Lyapunov theory and carried out by the **fuzzy** logic technique. Comparison between the proposed method and the feedback linearization method is shown by numerical simulations verifying the effectiveness of the suggested **adaptive** **control** scheme.

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[21] T.Y. Abdalla, HA Hairik, AM Dakhil , “Minimization of torque ripple in DTC of induction motor using **fuzzy** mode duty cycle controller ”, Energy, Power and **Control** (EPC-IQ), 1st International Conference on, IEEE 2010. [22] Z.T. Allawi , Turki Y. Abdalla, "An Optimal

The progress plot of the late acceptance list length, objective function values of the best and current solution at each stage entry is shown in Fig. 3 during the best run for the instance#2 (for which F-LAHH performs well). From this plot, we can see that the **fuzzy** system controls the list length in each stage to allow an adequate amount of diversification and intensification improving the quality of the solution in hand. A general trend was observed where the list length tended to increase over time, from about 22000 in the initial stages to about 26000 in the latter stages, and the amount by which the list length was changed decreased until the search stagnated, at which point changing the list length would have no effect and therefore the **fuzzy** system makes no change to the list length. On the other hand, it is observed that the worst run on this instance did not allow enough diversification and therefore converged too quickly resulting in solutions whose quality was worse than if more diversification was allowed.

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Fateh [7] improves his previous study [8] and applied to a three-joint articulated ﬂexible-joint robot. This study based on torque **control** strategy. Stability of the system was guaranteed and performance of the **control** system is evaluated. In this paper, a new **fuzzy**-**adaptive** **control** law is developed based on the Lyapunov function, thus stability of the system is guaranteed. First, **adaptive** **control** law is identified then, **control** parameter is defined by **fuzzy** logic controller. After simulation results, it has been seen that **fuzzy** logic controller improve the performance of the **adaptive** **control** law.

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The **Fuzzy** block inputs are presented in Figures 11 and 12. According to the set of rules of the **Fuzzy** con- troller The MPP is achieved when both ΔP and Δ V are zero. Moreover, the dynamic response of the con- troller is shown in Figure 13 (output signal ΔD ), where the steady state is achieved in 2.5ms. Note that, at this time the power supplied by the PV module accomplished the maximum power point (Figure 14) at 13V (Figure 15) and duty cycle equal to 0.3 (Figure 16).

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In some papers the **quadrotor** helicopter has also been controlled using a linear controllers based on lineariza- tion models. In [12] two **control** techniques were com- pared, a PID and a Linear Quadratic Regulator (LQR), where a linearization model was considered to design the PID controller. The development of the LQR was based on a time variant model. The time-optimal **control** prob- lem of a hovering **quadrotor** helicopter is addressed in [13]. Instead of utilizing the Pontryagin’s Minimum Prin- ciple (PMP), in which one needs to solve a set of highly nonlinear differential equations, a nonlinear program- ming (NLP) method is proposed. In this novel method, the count of **control** steps is fixed initially and the sam- pling period is treated as a variable in the optimization process. Nonlinear **control** problems for hovering qua- drotor helicopters such as feedback linearization **control** and back-stepping **control** laws were studied in [14]. Back-stepping based techniques are utilized to design a nonlinear **adaptive** controller which can compensate for the mass uncertainty of the vehicle. Lyaponve based sta- bility analysis shows that the proposed **control** design yields asymptotic tracking for the UAV’s motion in x, y, z direction and the yaw rotation, while keep the stability of the closed loop dynamics of the **quadrotor** UAV [11]. In [15] the rotor dynamics were considered in the model. The model was split up into two subsystems: the angular rotations and the linear translations and then back-step- ping and sliding mode techniques were used to **control** the helicopter. In [7] a **control** law based on a standard back-stepping approach for translational movements and a nonlinear

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Neuro-**fuzzy** network system combines the advantages of neu- ral network and **fuzzy** logic system. Neural network provides connectionist structure and learning abilities to the **fuzzy** logic systems, and the **fuzzy** logic systems provide neural networks with a structural framework with high-level **fuzzy** IF-THEN rule of thinking and reasoning. Neural network-based **fuzzy** systems, NF have the learning ability of neural networks to realize the **fuzzy** logic inference system, are gained popularity in the **control** of nonlinear systems [13]. The **adaptive** NF inference system (ANFIS) is one of the proposed methods to combine **Fuzzy** logic and artiﬁcial neural networks. Fig. 5 shows the **adaptive** NF inference system structure. It is com- posed of ﬁve functional blocks (rule base, database, a decision making unit, a fuzzyﬁcation interface and a defuzzyﬁcation interface) which are generated using ﬁve network layers:

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methodology employed to stabilize and guide the vehicle is PVA (Proportional- Velocity-Acceleration), derived and implemented by using Simulink. As it will be shown, it stabilises and provides satisfactory **quadrotor** trajectory tracking. Since the **control** methodology feeds back the acceleration of the vehicle, and this acceleration has an oscillating nature, an **adaptive** process has been designed and introduced into the vehicle’s model in order to avoid the oscillations’ transmission to the **control** system, showing how it reduces the amplitude of the **control** actions oscillations.

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such a case the multivariable process can be controlled using independent loop controllers. Decouplers are derived from a mathematical model of a plant and the model itself should be not complicated [3, 6, 7]. But the coupling and non-minimum-phase behavior, the nonlinearities from valves, high amount of noise present in sensor readings (caused by turbulent water flow) complicate mathematical modeling of the sys- tem. It was experimentally shown in [4] that the derived mathematical model of the analyzed level- pressure system is adequate only at the designed ope- rating regime and requires tuning of its parameters each time the set points changes. Considered that, and the fact, that the model-based **control** approach will not, in this case, significantly improve the level-pres- sure **control** results in practice, comparing to PI cont- rol (noise forbids derivative action, and non-minimum phase limits bandwidth) [3], **fuzzy** logic was intro- duced to **control** level-pressure system. To overcome the problem with the loop interactions, a **fuzzy** com- pensator was used instead of the pressure loop de- coupler. The **control** system is shown in Figure 4.

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Abstract: At present, the attitude **control** method of plant protection UAV is the classical PID **control**, but there are some imperfections in the PID **control**, such as the contradiction between speediness and overshoot, the weak anti-jamming ability and adaptability. The physical parameters of plant protection UAV are time-varying, and the airflow also interferes with it. The **control** ability of classical PID is limited, and its **control** parameters are fixed, and its anti-jamming ability and adaptability are not strong. Therefore, a **fuzzy** **adaptive** PID controller is proposed in this paper. **Fuzzy** logic **control** is used to optimize the **control** parameters of PID in order to improve the dynamic and static performance and adaptability of attitude **control** of plant protection UAV. In the process of research, the mathematical model of UAV is established firstly, then the **fuzzy** **adaptive** PID is designed, and then the simulation is carried out in Simulink. The simulation results show that the **fuzzy** **adaptive** PID controller has better dynamic and static **control** performance and adaptability than the traditional PID controller. Therefore, the proposed **control** method has excellent application value in the attitude of plant protection UAV.

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FL is used in weather forecasting system since global climate is unpredictably changing and airports need to be informed of the changes every instant of time. FL is also used in biological processes such as production of drugs. Many techniques have been used for controlling and automating biological processes; however, they were unsuccessful because of lack of information in some of the biological reactions, complexity of mathematical modelling of the systems, and unavailability of sensors. FL is used in some home appliances such as washing machines. For washing machines, sensors continually monitor conditions inside the machine and accordingly adjust the setting for the best wash result. FL is used in transportation system in Japan. Sendai trains in Japan include FL **control** for smart transmission, breaking system, traffic planning, predicting number of customers, and energy consumption. FL led to tremendous improvement in autonomous robotics **control** systems. In 1990s, Motorola produced a FL based microcontroller that was well suited for designing autonomous robots.

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