Despite the popularity of PID **controller** as the most practical **controller** in **control** engineering, there were still drawbacks reported. Around 30 % of the installed PID controllers in industrial are still operating in manual mode and around 65 % of automatic PID controllers are poorly tuned (Rani, 2012). On the other hand, a study from Van Overschee in 1997 shows that more than 75 % of total PID controllers installed are badly tuned and over than 20 % of the total PID controllers are set under default setting, which means that the controllers are not tuned at all. These situations shows that the **tuning** **process** of PID controllers are the most critical criteria in **tuning** operators in which the existing **tuning** methods are not well compatible for the **tuning** problems in industry.

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Pitch **control** has the potential for producing the highest level of interaction because of the presence of both diesel and wind turbine **control** loops. When wind power rises above the power set point the pitch **control** **system** begins operating to maintain an average power equal to the set point. The pitch **control** **system** consists of a power measurement transducer, a manual power set point **control**, a **proportional** plus **integral** feedback n, and a hydraulic actuator which varies the pitch of the blades. Turbine blade pitch **control** has a significant impact on dynamic behavior of the **system**. This type of **control** only exists in horizontal axis machines. Variable pitch turbines tly over a wider range of wind speeds than fixed pitch machines. However cost and complexity are higher. Generator dynamics model consists of a synchronous generator driven by a diesel engine through a flywheel and enerator driven by a wind turbine. The diesel generator will act as a dummy grid for the wind generator which is connected in parallel. Variations of electrical power due to changes in wind speed should be as small as possible; this is obtained by using duction generator as a wind turbine drive train. Unlike synchronous generators, induction generators are high compliance couplings between the machine and the electrical **system**. This is true for induction generators with slip of at Boenig and Hauer 1985; Mohamed ). The controlled variables are turbine speed and shaft torque. **Control** acts on the turbine blade angle (pitch **control**), since the torque speed nearly linear in the operating region, torque changes are reflected as speed changes. Therefore, it is possible to provide a single speed **controller** to **control** speed as well as torque.

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This paper presents the development of an intelligent **controller** for vibration suppression of a horizontal flexible plate structure using hybrid Fuzzy–**proportional**–**integral**–**derivative** **controller** tuned by Ziegler–Nichols **tuning** rules and intelligent **proportional**–**integral**–**derivative** **controller** tuned by artificial bee colony **algorithm**. Active vibration **control** technique was implemented during the development of the controllers. The vibration data obtained through experi- mental rig was used to model the **system** using **system** identification technique based on auto-regressive with exogenous input model. Next, the developed model was used in the development of an active vibration **control** for vibration suppression of the horizontal flexible plate **system** using **proportional**–**integral**–**derivative** **controller**. Two types of controllers were proposed in this paper which are the hybrid Fuzzy–**proportional**–**integral**–**derivative** **controller** and intelligent **proportional**–**integral**–**derivative** **controller** tuned by artificial bee colony **algorithm**. The performances of the developed controllers were assessed and validated. **Proportional**–**integral**–**derivative**–artificial bee colony **controller** achieved the highest attenuation for first mode of vibration with 47.54 dB attenuation as compared to Fuzzy–propor- tional–**integral**–**derivative** **controller** with 32.04 dB attenuation. The experimental work was then conducted for the best **controller** to confirm the result achieved in the simulation work.

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The **controller** can specifically **control** a **process** to the requirement by **tuning** the 3 components in the PID **algorithm**. The controller’s response can be in the form of the degree of setpoint overshooting by the **controller**, the responsiveness of the **controller** to an error, and the rate of **system** oscillation. It is worthy to note that using the PID **controller** does not ensure an optimal **system** stability. Some systems may need the use of 1 or 2 modes for an efficient **control**. This is achievable through setting the gain of undesired outputs to zero. Without the respective **control** actions, a PID **controller** can be referred to as either a PI, PD, P or I **controller**. The PI controllers are common due to the sensitivity of the **derivative** action to measurement noise; while the **system** may be prevented from attaining the target by the absence of an **integral** value as a result of **control** action. Further details on the PID **control** **system** is provided by [29].

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The PID **controller**, represented by Fig.3, is well known and widely use to improve the dynamic response as well as to reduce or eliminate the steady state error. The **Derivative** **controller** adds a finite zero to the open loop plant Transfer function and improves the transient response. The **Integral** **controller** adds a pole at the origin, thus increasing **system** type by one and reducing the steady state error due to a step function to zero. PID **controller** consists of three types of **control** **Proportional**, **Integral** and **Derivative** **control**.

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Abstract— ON line auto tune **algorithm** of **Proportional**- **Integral**-**Derivative** (PID) **controller** using successive approximation method is reviewed in this paper. Furthermore, detailed mathematical analysis of PID **control** equation is formulated for understanding of basic PID **controller**. The result of mathematical analysis come up with a numerical based **tuning** method. This new **tuning** method is the updated or **improved** **tuning** method over the existing **tuning** strategies based on numerical method. Using this new **tuning** method, PID **controller** has less overshoot and settling time as compared with its earlier numerical **tuning** **algorithm**. Effect of noise on PID parameters during online **tuning** **process** is analyzed and suggested method to modify PID parameters. In this present work, settling time is calculated as the function of PID parameters and settling time is correlated with new constant R and R’. MATLAB based simulation results are much more agreed with theoretical analysis of PID **controller**.

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Abstract: In this Paper, a novel meta-heuristics **algorithm**, namely the Firefly **Algorithm** (FA) is applied to the **Proportional** **Integral** **Derivative** (PID) **Controller** parameter **tuning** for Automatic Voltage Regulator **System** (AVR). The main goal is to increase the time domain characteristics and reduce the transient response of AVR systems. This paper described in details how to employ Firefly **Algorithm** to determine the optimal PID **controller** parameters of an AVR **system**. The proposed **algorithm** can improve the dynamic performance of AVR **system**. Compared with Ziegler Nichols (Z-N), Particle Swarm Optimization (PSO) methods, it has better **control** **system** performance in terms of time domain specification.

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Liquid level **control** **system** is commonly used in many **process** **control** applications, for example the level of liquid in a tank. Liquid tank **system** plays an important role in industrial application, such as in food processing, filtration and water purification **system**. In **process** industries, the water will be pumped into the tank and having a liquid **process** for example chemicals and mixing treatments. Afterwards, the liquid is transfered to other tanks as per requirement. The requirement in this **system** is to **control** the flow rate of the liquid delivered by the pump so that the liquid within the tank is as per desired. Vital industries where liquid level flow **control** is essential include petrochemical industries, papermaking industries and water treatment industries. In order to achieve this requirement of the **process**, the fluid supplied to the tanks must be controlled. An effective and proper **tuning** of PID **tuning** will be **improved** the performance of coupled tank liquid level **system**. Optimization technique will be used to obtain the **tuning** parameter of the **controller**.

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Measurement of Level, Temperature, Pressure and Flow parameters are very vital in all **process** industries. A combination of a few transducers with a **controller**, that forms a closed loop **system** leads to a stable and effective **process**. This article deals with **control** of in the **process** tank and comparative analysis of various PID **control** techniques and Genetic **Algorithm** (GA) technique. The model for such a Real-time **process** is identified as First Order Plus Dead Time (FOPTD) **process** and validated. The need for **improved** performance of the **process** has led to the development of model based controllers. Well-designed conventional **Proportional**, **Integral** and **Derivative** (PID) controllers are the most widely used **controller** in the chemical **process** industries because of their simplicity, robustness and successful practical applications. Many **tuning** methods have been proposed for PID controllers. Many **tuning** methods have been proposed for obtaining better PID **controller** parameter settings. The comparison of various **tuning** methods for First Order Plus Dead Time (FOPTD) **process** are analysed using simulation software. Our purpose in this study is comparison of these **tuning** methods for single input single output (SISO) systems using computer simulation.Also efficiency of various PID **controller** are investigated for different performance metrics such as **Integral** Square Error (ISE), **Integral** Absolute Error (IAE), **Integral** Time absolute Error (ITAE), and Mean square Error (MSE) is presented and simulation is carried out. Work in this paper explores basic concepts, mathematics, and design aspect of PID **controller**. Comparison between the PID **controller** and Genetic **Algorithm** (GA) will have been carried out to determine the best **controller** for the temperature **system**.

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Despite the potential of the modern **control** techniques with different structure, **Proportional** **Integral** **Derivative** (PID) type **controller** is still widely used for AVR sys- tem [2]. Industrial implementations of PID controllers in AVR systems show that the appropriate selection of PID **controller** parameters results in satisfactory performance during **system** upsets. Thus, the optimal **tuning** of a PID gains is required to get the desired level of robust perfor- mance. Since optimal setting of PID **controller** gains is a multimodal optimization problem and more complex due to nonlinearity and time-variability of real world power **system** operation. Therefore, the traditional techniques are not completely systemic and most of them occasion- ally yield poor performance in practice, so they are not suitable for such a problem.

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Computational techniques such as GA and fuzzy logic have been used for analytic solution [11, 16-18] which resulted the **control** field for implementing the real time manipulation based on the neural network. Furthermore, it has been established that Radial-Basis Function Neural Network (RBF-NN) has the ability to approximate any continuous function with any arbitrary accuracy [19, 20]. A **tuning** fuzzy logic approach to determine the optimal PID **controller** parameters in the AVR **system** by developing a fuzzy **system** can give the PID parameters on-line for different operating conditions [21]. A Linear-Quadratic Regulator (LQR) method has been implemented to improve the PID **controller** for a universal second-order **system** which required a good selection of weighting functions for acceptable performance [22]. An RGA and a PSO **algorithm** with new fitness function methods have been proposed to design a PID **controller** for the AVR **system** [23, 24].

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epsilon constraint method is considered because of ability to overcome the convexity problem of weight sum techniques which is normally used in multi objective optimization method and multi parameters that must be optimized to **control** the parameters of PID **controller**. Regarding to new optimization of PID **controller** based on EC-RBF neural network the performance of the **system** is increased. In addition, based on EC-RBF neural network the energy cost function, robustness as well as stability of the **system** for both humidity and temperature are **improved**. Improving the energy cost function and transient response of **system** it makes to increase the energy efficiency and improve the human comfort, respectively.

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The most popular automatic **controller** used for this purpose is the **Proportional**-**Integral**-**Derivative** (PID) **controller**. A PID **controller** is a **control** loop feedback mechanism that is widely used in industrial **control**. They are easy to construct and simple to manipulate based on the problem statement. The main function of the PID **controller** is to improve the performance indices of the AVR **system**. The performance indices are a set of parameters that determine the general characteristics of any **system**. An AVR **system** without a PID **controller** has undesirable characteristics like high peak overshoot, more settling time etc. that might cause damage to the **system** and degrade its efficiency. On the other hand, an AVR **system** with PID **controller** reduces the peak overshoot, settling time and so on.

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In practice, the design of the BLDC motor servo **system** usually requires time consuming complex **process** such as model, devise of **control** Scheme, simulation and parameters **tuning**. Hence in this paper a simple PID **controller** based speed **control** has been proposed for BLDC motor. The PID **controller** is highly suitable for the linear motor **control** [8].The PID **controller** is the most common form of feedback. It was an essential element of early governors and it became the standard tool when **process** **control** emerged in the 1940s. In **process** **control** today, more than 95% of the **control** loops are of PID type, most. PID controllers are today found in all areas where **control** is used. PID **control** is an important ingredient of a distributed **control** **system**. The controllers are also embedded in many special purpose **control** systems. PID **control** is often combined with logic, sequential functions, selectors, and simple function blocks to build the complicated automation systems used for energy production, transportation, and manufacturing.

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Due to the lack of fossil energizes and natural issues created by routine power era, renewable energy, especially solar based energy, has turned out to be maximumly prevalent. Sun based electric-energy request has become reliably by 20%–25% for each annum in the course of recent years, and the development is for the most part in **system** connected applications. With the exceptional market development in grid connected photovoltaic (PV) systems, there are expanding interests in grid connected PV designs. Five inverter families can be characterized, which are identified with diverse designs of the PV **system**: 1) focal inverters; 2) string inverters; 3) multistring inverters; 4) air conditioning module inverters; what's more, 5) cascaded inverters. The designs of PV systems are appeared in Fig. 1. Cascaded inverters comprise of a few converters connected in arrangement; accordingly, the high power and/or high voltage from the mix of the different modules would support this topology in medium and substantial lattice connected PV systems. There are two sorts of cascaded inverters. Fig. 1(e) demonstrates a cascaded dc/dc converter association of PV modules. Each PV module has its own particular dc/dc converter, and the modules with their related converters are still connected in arrangement to make a high dc voltage, which is given to a

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Today the demands of design and manufacture of miniature devices have been increasing in both research laboratories and industries. The demand and ability to view and manipulate structures/ devices/ systems at a nanoscale level has opened the possibilities of entire new area of scientific endeavour [1,2]. The size of devices continues to decrease in the nanometer scale size. The important factor that limits the manufacturing precision is the manipulation of the object at the nanoscale. Nanotechnology is the understanding and **control** of matter at the nanoscale where unique phenomena enable novel applications. It is the design, characterization, production and application of structures, devices and systems by controlling shapes and size at nanometer scale (atomic, molecular, and macromolecular scale) that produces structures, devices, and systems with at least one novel/superior characteristic or property [3,4]. The ability to image, **control** and measure at nanoscale is fundamental to nanotechnology Research and Development (R & D). Therefore, further progress in research in all area of nanotechnology request for the high precision positioning device which would ensure the nanometric accuracy of the positioning with high bandwidth [5].

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Consequently, there are has been an upsurge in quadrotor UAV **control** in recent times.Authors,Bora and Altuğ (2007) employed a quadrotor Euler-Newton techniqueto model vision-based stabilization and as well as for output tracking **control**. Similarly, the work of Suter,Hamel, and Mahony, (2002)established the application of quadrotor for image centered visual servo **control**. The research report by Dunfied, Tarbouchi, and Labonte, (2004) demonstrated the application of quadrotor for a neural network **controller**. Also, the quadrotor attitude was investigated with the aid of the Kalman filter by authors (Earl and Andrea, 2004). The work of Mohammad, Abbas, and Youmin (2012) revealed the usage of fault-tolerant adaptive PID-**controller** for a quadrotor helicopter **system** having actuator faults in presence, in their work, should a fault occur, the **system** response was **improved** with the aid of fuzzy scheduler for both tracking and change in tracking errors. The results obtained showed the proposed technique is very efficient and very adaptable for cases of uncertainties and external disturbances. Small UAV was proposed for attitude **control** with switching actuators in Hardware-In-the-loop (HIL) by BittarFiguereido, Guimaraes and Mendes (2014).

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Gain-scheduling **control** solutions are popular nowadays, and they are briefly analyzed as follows: fuzzy-based gain scheduling of exact feed-forward linearization **control** and sliding mode controllers for magnetic ball levitation **system** are proposed in [28]. A high gain adaptive output feedback **control** to a magnetic levitation **system** is discussed in [29]. A **Proportional**-**Integral**- **Derivative** (PID) gain-scheduling **controller** for second order linear parameter varying, which exclude time varying delay using a Smith predictor is given in [44]. Other interesting adaptive gain scheduling **control** techniques for real practical applications are given in [6], [54], [7], [17], [53]. The paper is dealing with the position **control** of a ferromagnetic sphere in a Magnetic Lev- itation **System** with Two Electromagnets (MLS2EM) laboratory equipment. A state feedback **control** structure (SFCS) is first designed in order to stabilize the **system** by applying the **control** signal only to the top electromagnet [8]. The simulated external disturbance can be applied

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GSA is gradually gaining attention from research community [2]. It has been found superior to some well-established optimization algorithms, such as Central Force Optimization (CFO), Genetic **Algorithm** (GA) and Particle Swarm Optimization (PSO) [1]. In [3], GSA was compared with GA in solving the cell placement problem of VLSI circuits design **process**, the results show that GSA has a better performance than GA. GSA and a modified PSO **algorithm** were applied for synthesis of scanned thinned array in [4], which GSA was found to outperform the modified PSO.

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