This paper presents a **tuning** approach based on **Continuous** **firefly** **algorithm** (CFA) to obtain the proportional-integral- derivative (**PID**) **controller** parameters in Automatic Voltage Regulator **system** (**AVR**). In the **tuning** processes the CFA is iterated to reach the **optimal** or the near **optimal** of **PID** **controller** parameters when the main goal is to improve the **AVR** step response characteristics. Conducted simulations show the effectiveness and the efficiency of the proposed approach. Furthermore the proposed approach can improve the dynamic of the **AVR** **system**. Compared with particle swarm optimization (PSO), the new CFA **tuning** method has better control **system** performance in terms of time domain specifications and set-point tracking.

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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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In recent literature many evolutionary optimization algorithms are proposed for **tuning** **PID** **controller** in the **AVR** **system** such as Anarchic Society Optimization [16], reinforcement learning automata optimization approach [17], real coded GA with fuzzy logic technique [18], Choatic ant swarm **algorithm** [19], Artificial Bee Colony **algorithm** [20], Hybrid GA-Bacterial Foraging (BF) **algorithm** [21] and local unimodal sampling **algorithm** [22]. GA and Ant Colony Optimization techniques are proposed to tune the parameters of FOPID **controller** in controlling of **AVR** **system**. In some of the research papers novel performance criteria has been proposed for **optimal** **tuning** of **PID** and FOPID **controller** in **AVR** control **system**. A novel performance criterion comprises of overshoot, settling time, steady state error and mean of time weighted integral absolute error has been proposed for **optimal** **tuning** of **PID** **controller** in **AVR** **system** using cuckoo search **algorithm** [23]. A. Sikander et. al, 2018 has proposed a cuckoo search **algorithm** based fractional order **PID** **controller** for **AVR** **system** with performance criterion which was proposed by Gaing et. al in 2004 [24]. In this research work, Cuckoo search (CS) and particle swarm optimization (PSO) algorithms are proposed to find the **optimal** parameters of **PID** **controller** in the control of automatic voltage regulator (**AVR**) **system** with new performance criterion comprises of Integral absolute error, rise time, settling time and peak overshoot. The performance of this new proposed performance criterion is compared with performance of other performance criterion such as ITAE, ITSE, ISE, MSE and IAE. The paper is mainly organized such that section two describes about the Automatic Voltage Regulator (**AVR**) **system**; section three examines the Cuckoo search (CS) **algorithm** and particle swarm optimization (PSO) algorithms; section four and five concentrate on the application of CS-**PID**, PSO-**PID** and conventional **tuning** method (Ziegler-Nichols) in **optimal** **tuning** **PID** **controller** for both servo and regulatory control of **AVR** **system**. Additionally, section six describes conclusions of the study.

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This paper presents a SNR-PSO **PID** **controller** for searching the **optimal** **controller** parameters of **AVR**. In this section, a **PID** **controller** using the SNRPSO **algorithm** was developed to improve the step transient response of an **AVR** **system**. Signal-to-Noise Ratio (SNR) **algorithm** are used in this paper to evaluate existence possibility of **optimal** value in **PID** parameters. This **algorithm** does not require a wide solution space, and the large number of searching and iterations were susceptible to related control parameters. On the other hand, this method has an effective appliance and better result for uncertainties conditions and different operation points. Signal-to-Noise Ratio **algorithm** has a responsible result in the nonlinear systems optimization. Signal-to-Noise Ratio (SNR) is a measure of the variation within a trial when noise factors present. It looks like a response which consolidates repetitions and reflects noise levels into one data point. SNR consolidates several repetitions into one value that reflects the amount of variation present. There SNR are defined depending on the type of characteristic desired, higher is better (HB), lower is better (LB) and nominal is best (NB). The equations for calculating S/N ratios for HB, LB or NB characteristics are given as follows [19]:

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Electrical motor servo systems are indispensable in modern industries. Servo motors are used in a variety of applications in industrial electronics and robotics that includes precision positioning as well as speed control [9]. Servomotors use feedback **controller** to control the speed or the position, or both. The basic **continuous** feedback **controller** is **PID** **controller** which possesses good performance. However is adaptive enough only with flexible **tuning**. Although many advanced control techniques such as self-**tuning** control, model reference adaptive control, sliding mode control and fuzzy control have been proposed to improve **system** performances, the conventional PI/**PID** controllers are still dominant in majority of real-world servo systems [1].

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Particle Swarm Optimization (PSO) technique, proposed by Kennedy and Eberhart is an evolutionary - type global optimization technique developed due to the inspiration of social act ivities in flock of birds and school of fish and is widely applied in various engineering problems due to its high computational efficiency . It has been proved to be an effective optimum tool in **system** identification and **PID** **controller** **tuning** for a class of processes. This techniques is used to minimize the maximum overshoot, minimize the rise time, minimize speed tracking error, minimize the steady state error, and minimize the settling time, optimization solution results are set of near **optimal** trade-off value which are called the Pareto front or optimally surfaces. PSO is a robust stochastic optimization technique based on the movement and intelligence of swarms. The components of PSO are Swarm Size, Velocity, position components and maximum no of iteration. The structure of the **PID** **controller** with PSO **algorithm** is shown in Fig.3.

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GA is an optimization technique inspired by the mechanisms of natural selection.GA starts with an initial population containing a number of chromosomes where each one represents a solution of the problem in which its performance is evaluated based on a fitness function. Based on the fitness of individual and defined probability, a group of chromosomes is selected to undergo three common stages: selection, crossover and mutation. The application of these three basic operations will allow the creation of new individuals to yield better solutions than the parents, leading to the **optimal** solution. The features of GA illustrated in the work by considering the problem of designing a control **system** for a plant of a first order **system** with time delay and obtaining the possible results. The future scope of this work is aimed at providing a self-**tuning** **PID** **controller** with proposed **algorithm** (Particle Swarm Optimization - PSO) so as to solve the complex issues for real time problems.

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Many **PID** **tuning** methods are introduced. The Ziegler-Nichols method is widely used for **Controller** **Tuning**. One of the disadvantage of this method is prior knowledge regarding plant model. Once tuned the **controller** by Ziegler Nichols method, a good but not optimum **system** response will be reached. The Transient response can be even worse if the plant dynamics change. To assure an environmentally independent good performance, the **controller** must be able to adapt the changes of the plant dynamic characteristics. For these reasons, it is highly desirable to increase the capabilities of **PID** controllers by adding new features. Many random search methods, such as Genetic **Algorithm** (GA) have received much interest for achieving high efficiency and searching global **optimal** solution in the problem space.

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Sahu et al. [32] have outlined around the design and style as well as effectiveness evaluation regarding Differential Evolution (DE) **algorithm** based parallel 2-Degree Freedom of Proportional-Integral-Derivative (2-DOF **PID**) **controller** for Load Frequency Control (LFC) of interconnected power **system** process. The planning issue has been formulated as an optimization issue and DE has been currently employed to look for **optimal** **controller** parameters. Standard as well as improved aim features have been used for the planning goal. Standard aim features currently employed, which were Integral of Time multiplied by Squared Error (ITSE) and Integral of Squared Error (ISE). To be able to additionally raise the effec- tiveness in the **controller**, some sort of improved aim operate is derived making use of Integral Time multiply Absolute Error (ITAE), damping ratio of dominant eigenvalues, settling times of frequency and peak overshoots with appropriate weight coefﬁcients. The particular ﬁneness in the recommended tech- nique has become conﬁrmed by simply contrasting the results with a lately published strategy, i.e. Craziness based Particle Swarm Optimization (CPSO) for the similar interconnected electric power process. Further, level of sensitivity evaluation has been executed by simply varying the machine details as well as managing load conditions off their nominal valuations. It is really observed which the recommended controllers are quite powerful for many the **system** parameters as well as man- aging load conditions off their nominal valuations.

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Abstract: **AVR** (Automatic Voltage Regulator) plays a key role in generating stations. To maintain voltage stability of the generator the terminal voltage should remain constant all the times. In a large interconnected **system** manual regulation is much complicated and therefore automatic generation and voltage regulation is necessary. So, to maintain a constant voltage level, Automatic voltage regulators are used at each generating station. This paper presents the MATLAB simulation based on Ant Colony Optimization (ACO) technique used for the **tuning** of the **PID**(Proportional, Integral, Derivative) controllers which are used for **AVR** **system**. Ant Colony Optimization technique is recognized from the behavior of real ants within the colony to find optimum gain value in a shortest time period. In this paper, an attempt has been made to find out an **optimal** gain values for **tuning** of **PID** **controller** based on ACO **algorithm** to improve the overall transient performance of an **AVR** **system** for the control of terminal voltage following disturbance through MATLAB simulation.

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The **PID** **Controller** is an example of a control loop feedback technique that is used in engineering, industrial and process control systems. The **PID** works optimally in electrical, electronic or electromechanical systems that has precise and distinguishable mathematical model. The **PID** **Controller**, basically, calculates three separate **system** parameters: proportional, integral and derivative coefficients. The proportional component of **PID** computes the value of the very current error. Similarly, the integral component calculates the result of the sum of recent errors while the derivative component is involved with the determination of the system’s reaction based on the rate dynamism of the errors. The weighted sum of the functions of the three **PID** components is then imported into the control **system** to regulate the **AVR**. Some of the different **AVR** models with input **PID** controllers are presented in Figures1-4.

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Initially, the design of PIDA **controller** parameters have been determined using analytical method. In this method two characteristic equations; one formed with desired root loca- tions with specifications based on the design criterion, and another one with the nominal control structure were equated to deduce the parameters of PIDA. It have been considered to design PIDA **controller** for third order systems and extended for the control of an AC motor **system** [2]. The application of PIDA were considered for a servo motor driving a load through a long shaft or transmission **system** in [5]. Sambariya and Paliwal, 2016 have presented the **optimal** **tuning** of PIDA **controller** parameters using harmony search **algorithm** in [6].

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In the design of **PID** controllers, Ziegler-Nichols settings give an oscillatory response of the **system**; hence the parameters found by this method cannot be implemented in the designs as computed. However, it can be used with the genetic **algorithm** to form possible interval for the **controller** parameter set. Since determining precise intervals for these gains have not been developed yet, it is wise to use these gains as the first step towards **tuning** the **controller**. For example, the lower and upper bounds for the design parameters can be formed around the values found from Ziegler-Nichols method, such as one-third for lower bound and three-fold for the upper bound

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There are several methods in the literature for **tuning** the **PID** parameters, which include some modern techniques. Ang et al presented a modern overview of functionalities and **tuning** methods, software packages and commercial hardware modules [6]. Pyung et al presented asystematic method to select gains of a discrete **PID** **controller** [7]. Coelho et al used a chaotic optimization approach based on Lozi map for **tuning** the **PID** parameters [8]. He et al presented a new **optimal** PI/**PID** **controller** **tuning** algorithms via LQR approach [9].Awouda et al demonstrated an efficient method of **tuning** the **PID** **controller** parameters using the optimization rule for ITAE performance criteria [10].Bagis presented an efficient and fast **tuning** method based on a modified generic **algorithm** structure to find the **optimal** parameters of the **PID** **controller** [11]. And Stephen et al formulate multi-input multi- output proportional integral derivative **controller** design as an optimization problem [12].

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We extended the benefits of P ⋋ **controller** for **AVR** **system**. In Particular, a masked P ⋋ **controller** is developed in matlab by programming to optimize the parameters based on the recently developed optimization techniques instead of using toolbox which was restrained for using advanced optimization techniques. Genetic **Algorithm** (GA) technique has already been used to determine **optimal** solution to several power engineering problems and we employed these algorithms to design an FOPID **controller** for Automatic voltage regulator (**AVR**) problem. The proposed **controller** is simulated within various scenarios and its performance is compared with those of an optimally-designed **PID** **controller**. Transient response and performance robustness characteristics of both controllers are studied and superiority of the proposed **controller** in all two respects is illustrated.

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In this paper, a Simulink model of a DC motor has been designed and for controlling the speed, a **PID** **controller** based **system** has been designed; followed by the estimation of **PID** parameters of Kp Ki and Kd using Ziegler-Nichols method. Since the ZN-**PID** controllers when implemented for the systems, presents an oscillatory response, so the optimization of the **PID** gains is carried out by various soft- computing techniques like Genetic Algorithms, Multi- Objective Genetic **Algorithm** and Stimulated Annealing. The parameters obtained by Ziegler-Nichols have been used as boundary limits for the **PID** **controller** optimization populations to provide, faster convergence.

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solving **continuous** nonlinear optimization problems. The PSO technique can generate a high-quality solution within shorter calculation time and stable convergence characteristic than other stochastic methods [4, 6]. Bacterial Foraging Optimization (BFO) is a population-based numerical optimization **algorithm**. Until date, BFO has been applied successfully to some engineering problems, such as **optimal** control, harmonic estimation, transmission loss reduction and machine learning [7,8]. However, experimentation with complex optimization problems reveals that the original BFO **algorithm** possesses a poor convergence behaviour compared to other nature-inspired algorithms and its performance also heavily decrease with the growth of the search space dimensionality[8]. BF-PSO **algorithm** combines both BFO and PSO. The aim is to make PSO ability to exchange social information and BF ability in finding new solution by elimination and dispersal, a unit length direction of tumble behaviour is randomly generated.

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feedback **controller** analyzed for temperature control of **continuous** stirred tank reactors (CSTRs) which have strong non linearities has been done in [2].The control objective in his simulation-based work is to maintain the CSTR at steady state operating point. Methodologies to learn and optimize fuzzy logic **controller** parameters based on neural network and genetic **algorithm** has been developed in [3].Designing of **PID** **controller** has been done in [4]. Ziegler et al [6] has proposed a **system** of units for measuring the control effects, which are now in common use. Wang-Xiao Kan et al [7] introduces a design method of fuzzy self-**tuning** **PID** **controller** and make use of MATLAB fuzzy toolbox to design fuzzy **controller**, organically combine fuzzy **PID** **controller** with Simulink. Development of a Linear Quadratic Regulator (LQR) for a set of time-varying hyperbolic PDEs coupled with a set of time-varying ODEs through the boundary has been discussed in [8]. Qingsi Zhanget al [9] uses Fuzzy logic **controller** with self-**tuning** **PID** parameter. CSTR **system** has been discussed in [10].

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Noise corrected improved **algorithm** settle down in very short duration of time and overshoot approximately equal to less than 1% (Except 3-phase Induction Motor). In the noisy environment improved **algorithm** with a noise, the correction method is suggested. Thus, the new **tuning** **algorithm** tuned **PID** **controller** for faster response and better noise rejection property so that this **algorithm** is robust in the noisy environment. The roots of r1 and r2 should be real only and left the side of root locus for better stability and performance of the **algorithm**. The three-phase induction motor control by using SAM-**PID** **controller** is more stable and efficient. The auto tune **algorithm** is robust and demonstrates the performance in noisy environment. Thus, it can be used in various ways. To improve the **system** performance SAM-**PID** **controller** also has the feature of adaptive and **optimal** controlling.

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