2.9. Recent Work on **Type**-**2** **Fuzzy** Sets and Systems 75 The first application of a **type**-**2** **fuzzy** **logic** **controller** to an autonomous mobile **robot** was implemented by Hagras [118], who demonstrated that it outperformed a **type**-1 FLC. The architecture of the **controller** was based upon interval **type**-**2** **fuzzy** **logic** controllers which were used to implement the basic navigation behaviours, and also the coordination of them to produce a **type**-**2** hierarchical FLC. Experiments were carried out in a labora- tory environment and also outdoors. The environments were challenging, dynamic and unstructured in nature. Numerous experiments were carried out including night time op- eration. It was shown that the **type**-**2** controllers dealt in real-time with the uncertainties of the environments. The results obtained showed very good real-time control responses, which had outperformed the equivalent **type**-1 FLCs and HFLCs. There was about a 64% reduction in the number of rules for the **type**-**2** FLCs and HFLCs to those used in the equivalent **type**-1 configuration system. The first instance of an industrial DSP embed- ded platform, with a real time **type**-**2** FLC, used to control a marine diesel engine was by Lynch et al [17], [119]. They found that the **type**-**2** FLCs dealt with the uncertainties in real-time and produced a robust control response. This demonstrated that the embedded **type**-**2** FLCs outperformed the PID and **type**-1 FLCs previously used to control the ma- rine engine whilst using smaller rule bases. Coupland has shown that the use of geometric methods can resolve the computational overhead required in general **type**-**2** **fuzzy** **logic**, and so allow it to be applied to time critical control problems [45]. This was demonstrated in [120], where a general **type**-**2** FLC outperformed both an interval **type**-**2** and a **type**-1 FLC, all executing the same tasks. Studies comparing **type**-**2** and **type**-1 FLC perfor- mance have shown that the best results are given by the **type**-**2** controllers [121], [122]. In the realm of **robot** soccer games Figueroa et al. [123] explored how the **type**-**2** **fuzzy** **logic** **controller** overcome the uncertainty in the control loop without increasing the com- putational cost of the application. Hagras recently described a method to develop a **type**-**2** FLC through embedded **type**-1 FLCs demonstrating that the **type**-**2** FLC outperforms the **type**-1 FLCs that it was based on [124].

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minimum of 500 kWh per year for everyone in the world by 2020, but the current per capital consumption of Ethiopia is 25 kWh per person per year [1]. This shows that the access of electricity in the country is very low. About 85% of the population of Ethiopia is living in rural areas, where access to modern electricity is difficult [**2**]. The communities concerned for this study are settled farther away from the national grid and are sparsely populated, which makes extending the national grid is uneconomical because of the high cost of transmission and and distribution as well the very low load factor. When the issue of electrification is raised, the key issue and the problem that needs to be addressed the question of affordability and pollution of the environment. Ethiopia has huge renewable energies such as **micro**-hydropower, solar, geothermal, biomass and wind that have not yet been assessed and accessed for rural electrification. Though Ethiopia is having huge renewable energy potential, the 85% of the population are not yet electrified. This research paper a standalone hybrid solar/**micro**-hydro/Bio- mass power system is proposed for rural areas. This **type** of hybrid system plays great role in protection of the environment for the countries such as Ethiopia where majority of their people are participating in coffee production.

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pushes system state variables towards the sliding line. A chattering measure is introduced. The integral of the sliding measure, and performance indicators, including the rise time, error integral and steady state error, are used to define a fitness function in a step reference scenario. The method is tested on the model of a **2**-DoF DD (Direct Drive) SCARA **type** **robot**, via simulations. The GA-tuned SMC, however, is obtained for a fixed reference signal and fixed payload. Different references and payload values may lead to chattering effects and performance degradation. The second SMC parameter tuning method proposed in the thesis employs a **fuzzy** **logic** system to enlarge the operation range of the **controller**. The chattering measure and the sliding variable are used as the inputs of this system. The **fuzzy** **logic** system tunes the **controller** output smoothing mechanism on-line, which opposes the off-line GA technique. Again, simulations carried out with the Direct-Drive **robot** model are employed to test the control and the tuning method. The variable sliding control gain and the introduction of a “Smoothing Function” tuned by a GA and a **Fuzzy** **Logic** System are **novel** contributions.

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G. Xu et al have used **fuzzy** and **fuzzy** neural network impedance **controller** to regulate impedance parameters،. In this studies control method needs to impaired limb parameters for regulate impedance parameters [13, 14]. Also G. Xu et al presented adaptive hierarchical control for upper limb rehabilitation which combines the high-level progressive resistive supervisory **controller** with a low- level adaptive resistive force triggered **controller** [15]. Y. Choi et al have proposed a **novel** robotic adaptive and automatic presentation of tasks. In this study, the high-level adaptive task scheduler regulated task and difficulty of exercise according to physiotherapist, prior practice database and task bank. Then function task model generated desired trajectory for admittance **controller** [16].

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Interest in renewable energy systems have increased due to the global campaign to reduce the use of fossil fuels. This campaign is motivated by the **dual** consequences of the use of fossil fuels namely, unsustainability and drastic climatic impact. Therefore, intense research into developing reliable renewable energy systems is ongoing. Solutions to reliability issues in renewable energy systems due to intermittent nature of the sources include developing hybrid configurations known as hybrid renewable energy systems (HRES) consisting of two or more renewable energy sources and/or energy storage devices [1]. The benefits of renewable energy systems cannot be overemphasized. However, improper energy flow control, poor energy harvesting methods and/or incorrect battery charge/discharge algorithms result in not only low returns on investment, but also rapid system deterioration and possibly damage to the equipment [**2**]. Therefore, the struggle to improve on the efficiency and reliability of these systems has led some researchers to focus on extracting as much power as possible from the renewable sources [3–5]; some others have focused on improving converter efficiencies to minimize the losses incurred during power conversions [6–8]; yet, other researchers have sought to introduce improvements in the area of energy storage [9–11]; also, a number of researchers have proposed various schemes for power flow management in renewable energy systems [12–14]. Furthermore, the inefficiencies of traditional system monitoring and control techniques have led to the evolving of artificial intelligence based optimizations such as adaptive neuro-**fuzzy** inference system [15–17], particle swam optimization [18,19], genetic algorithms [20,21]. **Fuzzy** **logic** controllers have also gained a place in high performance power monitoring and control [22–24]. Among the advantages of **fuzzy** **logic** based controllers is that the developer is saved the stress of tedious mathematical modelling of the system to be developed, thus making the **controller** easier to develop. Validation of a proposed work or control scheme is a vital step for confirming the functionalities of the system so proposed. The validation may include computer simulations or experimental setups. In order to validate the effectiveness and adaptability of real-time control policy for a hybrid electric tracked vehicle, [25] simulated two driving schedules on the Simulink environment. [26], using dSPACE **controller**, confirmed the improved performance of a linear

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Due to the reduced voltage applied on the switches and an increased number of voltage levels, the 3L-NPC topology becomes more efficient while showing a lower current Total Harmonic Distortion (THD) [13] than an equivalent two level inverter. Several works have been carried out on ESS hybridization using multilevel topologies, including the 3 Leg 3L-NPC [13]–[15]. In [13], a PI **controller** is designed to control the power flow of a Vanadium Redox Flow Battery (VRB) whereas a Super Capacitor (SC) provides the fast variation of power with both parallel and 3 Leg 3L-NPC inverters. It is shown that, beyond the limits of the 3L-NPC topology, the efficiency and THD improvement make this topology suitable for ESS hybridization.

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In this study, we will represent the **novel** application of the Interval T2 (IT2) FLCs to solve the moon landing problem in the computer game Lunar Lander. To the best of our knowledge, this is the first deployment of the widely used **type**- **2** **fuzzy** control structure to the lunar lander game. We will present a **novel** **type**-**2** **fuzzy** moon landing control system which is composed of two key blocks, namely the error signal generator and the IT2-FLC structure. The error signal generator is the key block which gives the chance to transform the moon landing problem of the spaceship as a multivariable control problem. In this **fuzzy** control system, we will design two input IT2-FLCs to have adequate control and game performance in the presence of the uncertainties, disturbances and nonlinear system dynamics. In this paper, firstly detailed information about the components of the proposed T2 **fuzzy** moon landing system is provided. Then, we will present the employed optimization based design approach of the IT2-FLC structure. We will examine the performance of the proposed T2 **fuzzy** moon landing structure, in comparison with its **type**-1 and conventional counterparts, by providing comparative results performed in the real game environment as well as simulation studies. The results will show that proposed T2 **fuzzy** moon landing system, in comparison with its conventional and **type**-1 counterparts, with a satisfying game and control performance in the presence of nonlinearities and high level of uncertainties. The paper is organized as follows. Section II provides information about the game space of lunar lander. Section III presents the proposed T2 **fuzzy** moon landing system. The comparative experimental results with designed controllers with their design methodology are given in Section IV. Finally, the conclusions and future works are summarized in Chapter V.

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Artificial Intelligent (AI) as one of the computer science branches can improve the performance of mobile robots. It can handle optimization (PSO), data mining (GA), classification (SVM, NN), decision (**fuzzy**, expert system), and so on. In this research, it utilizes a **fuzzy** system in order to handle the navigation of mobile robots in an unknown environment. The trajectory of them is based on the received information from attached sensors [6]. There are a lot works use **fuzzy** **logic** as the intelligent system [7-9]. It's due to **fuzzy** **logic** does not require exact mathematical modelling but rather works on the idea of range between 'zero' and 'one' value. A **fuzzy** **logic** **controller** is a control design where decisions are made by applying a **fuzzy** interference system based on rule or knowledge containing strings of **fuzzy** if-then rules [10]. This heuristic knowledge will develop perception-

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The **fuzzy** **logic** **controller** rules in table 1 results in the **surface** rule view for the **controller** as can be seen in figure 4. From figure 4 it can be seen that the change in rotational speed and output power varies between [-5 5] and [-3 3]. These changes were cho- sen to have a relatively low value when compare to the output of the generator with the advantage of increased accuracy, but the disadvantage of slower response time for a changing wind speed.

A main objective of controlling melt temperature is to develop a thermal control framework based on temperature profile measurements, which manipulates screw speed and individual set temperatures together to reduce undesirable melt temperature variations while maintain the required average temperature levels. Obviously, this **type** of **controller** will have to handle the complex nonlinear behaviors of the process. This study uses a model-based control approach and hence the performance of the **controller** depends on the accuracy of models. In this **type** of work, use of a control technique like interval **type**-**2** **fuzzy** **logic** (IT2FLC) may be advantageous as it does not require fully accurate models. Another major advantage is that interval type2 **fuzzy** **logic** (IT2FLC) **controller** can handle process nonlinearities with a set of linguistic IF-THEN rules which do not require exact numerical boundaries. Due to these and its other advantages, interval type2 **fuzzy** **logic** (IT2FLC) was selected as the control technique for this study.

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Regarding the membership **type** used in **Fuzzy** **Logic**, other choices than the triangular membership are available such as the gaussian membership, trape- zoid membership or sigmoid membership. As explained above, researcher usu- ally applies the triangular membership as it is seems to be easier than other membership. Even if this is the factor, there were also some other ﬁndings that discovered very interesting results as shown by V.O.S Olunloyo et al.[10]. Based on their ﬁndings, the triangular membership can exhibit linguistic error as it may not deﬁnes properly the real system conditions. As a result, the gaussian membership can be the best membership to describe any practical system for most engineering application. Moreover, gaussian membership may surpassed triangular membership function if better tracking performance is being priori- tized[11].

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ABSTRACT: In this paper, proportional–integral–derivative (PID)-based Fly-back converter and **fuzzy**-**type** controllers are compared for 220V A.C input and 440 D.C output flyback converter operating in discontinuous conduction mode (DCM).Design of **fuzzy** controllers is based on heuristic knowledge of converter behavior, and tuning requires some expertise to minimize unproductive trial and error. The design of PID control is based on the frequency response of the converter. For the flyback converter, the performance of the **fuzzy** **controller** was superior in some respects to that of the PID controllers. The **fuzzy** **controller** was able to achieve faster transient response in most tests, had a more stable steady-state response, and was more robust under some operating conditions. MATLAB/Simulink software is used for implementation and simulation results show the performance variations.

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Model predictive control (MPC) is an advanced method of process used in industries, oil refineries and now-a-days it has also been used in power system balancing models. Model predictive controllers depend on dynamic models and most often linear empirical models obtained by system identification. The most important aspect of MPC is the fact that it allows optimized current timeslot, while considering future timeslots in account. MPC can achieve by optimizing a finite time-horizon, but only implementing the current timeslot. Model predictive control has the ability to predict the future events and can take control actions accordingly. MPC is early universally applied as a digital control, although there is research in achieving faster response with specially designed analog circuitry. The models used in Model predictive control are normally intended to represent the functioning of complex dynamical systems. The additional complexity of the Model predictive control algorithm is not generally needed to provide adequate control of simple systems, which are often controlled well by generic PID controllers. Common dynamic characteristics that are difficult for PID controllers include large time delays and high-order dynamics. MPC will predict change in dependent variables that takes place during the process. In the control process the independent variables are adjusted by the **controller** very frequently either by set of point’s regulatory. PID controllers are the final control elements with independent variables that cannot be tuned by means of the **controller** are used as disturbances. Dependent elements in this procedure are the measurements of control objectives or constraints. MPC use the current plant measurements and dynamic response of the system function, MPC model is used to check the changeable parameter

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Multi-**robot** formation methods can be partitioned into three class approaches such as virtual structure approach, behavioral approach, and leader-follower approach. Each of them has several advantages and weaknesses. The virtual structure approach treats the entire formation as a single virtual rigid structure [5], [11]. The main disad- vantage of the current virtual structure implementation is the centralization, which leads a single point of failure for the whole system. By behavior-based approach, several desired behaviors are prescribed for each **robot**, and the final action of each **robot** is derived by weighting the relative importance of each behavior [15], [16]. The limitation of such approach, it is difficult to analyze mathematically, therefore it is hard to guarantee a precise formation control. In the leader-follower approach [17], [18], [19], one of the robots is designated as the leader, with the rest being followers. Unfortunately, it centralized control, if the leader fails then it can be influenced by the all system performance [20], [22]. Due to, the limitation has explained above hence, the selected method to enhance the formation control performance, how to improve it by overcoming the drawbacks is desirable. In a certain sense, the formation control problem can be seen as a natural extension of the traditional trajectory-tracking prob- lem.

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The principle of working and structure of the IT2FLC is similar to the T1FLC, just the **type** there are block named reducer its location between the inference block and defuzzifier block due to the o/p of the inference block is a **type**-**2** output **fuzzy** set and it should that before applying it must to converted by defuzzifier to make it crisp o/p such as converted to a **type**-l **fuzzy** set. For IT2FLC the block diagrams shown in Fig.1 and for i/p and o/p crisp of the IT2FLS the mapping between them it is represented as

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Figure 5 shows the functional block diagram of the FMRLPSS. It is made up of four main parts; the plant, the **fuzzy** **type** **2** **controller** to be tuned, the reference model, and the learning mechanism (an adaptation mechanism) [8]. The FMRLPSS uses discrete time signals (r(kT), and y(kT) with T as the sampling period. It also uses the learning mechanism to observe numerical data from a **fuzzy** **type** **2** control system. With this numerical data, it characterizes the **fuzzy** **type** **2** control system’s current performance and automatically synthesizes or adjusts the **fuzzy** **type** **2** **controller** so that some given performance objectives are met.

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capability to understand the systems behavior. Besides, this control technique is based on qualitative control rules. This kind of approach depends on the basic physical properties of the systems, and it is potentially able to extend control capability even to those operating conditions where linear control techniques fail. As a consequence, the application of nonlinear control laws to face the nonlinear nature of balancing **robot** is easy since **fuzzy** control is based on heuristic rules. In fact, the FLC approach is general in the sense that almost the same control rules can be applied to a non-linear balancing **robot** system. It is possible to give two inputs to the FLC as shown in Figure 3. The proposed defuzzification methods for the FLC are sugeno or mamdani. This is because both of these techniques are commonly used in designing the FLC. In order to implement 6 inputs to the controllers, the FLC were divided into three. As illustrated in Figure 3, the ‘FLC 1’controls the linear position on x-axis, ‘FLC **2**’ controls the angular position y-axis and ‘FLC 3’ controls rotational angle on z- axis of the balancing **robot**. The ‘FLC 1’ received the difference (error signal) between position of cart and set point position, x and the rate at which the error of position changes, Δx as the inputs while the ‘FLC **2**’ received the angle error and rate of error of pendulum pole as the inputs while ‘FLC 3’ received the error and rate of error of rotational angle about z-axis. The control variables of all FLCs were summed together before converted into voltage signal. This signal is then supplied to the dc motors on both left and right sides of the balancing **robot**.

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Abstract – Universal Stretch and Bending Machine (USBM) is a combination of Stretch Machine and Bending Machine which are used in car door sash production. The main purpose of combining these two machines is to reduce the number of machines, space utilization and increase productivity. This paper basically focuses on the design and modeling for revolute control of USBM simplified model. Basically, comparative evaluation of intelligent control (**Fuzzy** **Logic** **Controller**) systems that suites the USBM simplified model are evaluated. The evaluation is done by comparing the PD–**type** FLC performance of different **fuzzy** rules and membership functions in terms of time response and integral square error. Prior to that, mathematical model of the system is first derived and verified by SIMULINK (MATLAB). Based on the simulation result, PD-**type** FLC with 5 membership function is better than PD-**type** FLC with 7 membership function in terms of time response specifications and integral square error.

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The use of convectional automatic voltage Regulator (CAVR) in synchronous generators to control the terminal voltage and reactive power has been the common phenomena in power systems control. Synchronous generators are nonlinear systems which are continuously subjected to load variations and the AVR design must cope with both normal load and fault condition of operation. Evidently, these conditions of operation result to considerable changes in the system dynamics. When the CAVR with fixed gain are used, the performance worsens and in some cases, introduces negative damping and degraded system stability. So far, a lot of work has been done in synchronous machine excitation stabilization using CAVR and controllers, all geared toward overcoming the problems enumerated above. The short comings here is that the parameters of the controllers are fixed and so if the system dynamics changes as a result of faults, the **controller** will be tuned manually to adjust. Modern control techniques are used extensively to achieve self-tuning (ST) control in synchronous generators. These include minimum variance (MV), generalized minimum variance (GMV), optimal predictor and pole placement (PP). In all these ST-AVR work, additional signals are used to improve robustness and are generally nonlinear. The MV generally gives very lively control and can be highly sensitive to non minimum phase plant. GMV, which is more robust and generalized, is vulnerable to unknown or varying plant dead time and can have difficulty with d.c offsets. PP aims to locate the closed-loop poles of the system at pre-specified locations leading to smooth controllers, but the algorithm shows numerical sensitivity when the plant model is over parameterized. Of recent, a lot of research is going on in areas of application of soft computing (**fuzzy** and neural approach) in synchronous generator controls. This work is based on interval **type**- **2** **fuzzy** **logic** **controller** (IT2FLC). (IT2FLC) in synchronous generator (SG) terminal voltage and reactive power control is designed so that it has the ability to improve the performance of interval **type**-**2** **fuzzy** **logic** **controller**. The interval **type**-**2** **fuzzy** **logic** **controller** is superior to conventional AVR controllers which continue to tune the **controller** parameters because it will tune and to some extent remember the values that it had tuned in the past.

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The design of the **fuzzy** **logic** **controller** considered in this work is shown in figure 1. The two inputs, load and operating temperature of the computer, are fed into the system. Fuzzification is carried out and made to function in line with the rule set. Inferences are drawn and defuzzificaton is carried out to obtain the required output. The result, speed is fed back for necessary comparison.

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