Abstract. The **DC** **motors** are widely used in the mechanisms that require **control** of speed. Different speed can be obtained by changing the field voltage and the armature voltage. The classic **PID** controllers are widely used in industrial process for speed **control**. But they aren’t suitable for high performance cases, because of the low robustness of **PID** **controller**. So many researchers have been studying various new **control** techniques in order to improve the system performance and **tuning** **PID** controllers. This paper presents particle swarm optimization (**PSO**) method for determining the **optimal** **PID** **controller** parameters to find the **optimal** parameters of **DC** M otor speed **control** system. The **DC** M otor system drive is modeled in M ATLAB/SIM ULINK and **PSO** **algorithm** is implemented **using** M ATLAB toolbox. The results obtained through simulation show that the proposed **controller** can perform an efficient search for the **optimal** **PID** **controller**. Simulation results show performance improvement in time domain specifications for a step response (no overshoot, minimal rise time, steady state error = 0).

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In this paper, a simple performance criterion in time domain is proposed for evaluating the performance of a **PSO**-**PID** **controller** that was applied to the complex **control** system. GA is an iterative search **algorithm** based on natural selection and genetic mechanism. However, GA is very fussy; it contains selection, copy, crossover and mutation scenarios and so on. Furthermore, the process of coding and decoding not only impacts precision, but also increases the complexity of the genetic **algorithm**. This project attempts to develop a **PID** **tuning** method **using** GA **algorithm**. For example, ants foraging, birds flocking, fish schooling, bacterial chemo taxis are some of the well-known examples.

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Based on present literature review authors concluded that **PID** **controller** is very effective and powerful **controller** and has better **control** approach in order to sustain speed of the motor. The parameter values of **PID** **controller** are set up by **tuning** methods. ANFIS has faster response than response of other traditional methods. [1] It is better in rise time, settling time and less steady state error. All **tuning** methods are compared to optimize the values of parameters like Mp, Tr, Ess and Ts. Some new methods like JOA, **PSO**, LQR and MPC also give better and smoother response than traditional methods.[5]Still there is much scope in improving the **PID** **controller** design to make it more simple REFERENCES

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Since there is an enormous improvement in the force electron- ic frameworks, yet the direct current machines are the prime hotspot for the era of the electric footing. Presently a days, discovering more helpful applications in auto industry if there should arise an occurrence of electric vehicles. Since, in cruise system, by conforming the terminal voltage we can work it over an extensive variety of paces, consequently making them good with most mechanical loads by excellence of their torque/speed qualities, along these lines conveying superior and simple controllability [1]. Yet, progressively applications, there are sure variables like outer clamor, variable and ques- tionable inputs, obscure parameters, changes in the motion of the heap, and so on.; prompting the flimsiness in their **control**. **PID** controllers reason for their straightforwardness and vigor discovers applications in 90% of the **control** frameworks being used today. In this way, the streamlining of the **PID** **controller** parameters is a standout amongst the most essential fields in execution and outlining of **PID** controllers [2]. The traditional and broadly acknowledged technique for **tuning** the **PID** para- meters is calculation by Ziegler-Nichols system. Then again, registering the additions doesn't generally gives the best pa- rameters in light of the fact that **tuning** measure presumes one-fourth diminishment in the initial two crests. Be that as it may continuously applications, due to the commotion, the tuned parameters does not generally give the best results, so need is there to try and tweak them, so they can undoubtedly adjust with these changing framework elements. For better versatile reaction of the framework, in vicinity of outer glitches, the utilization of different delicate registering procedures like Fuzzy-Logic, Artificial Neural Networks, Genetic Algorithms, Particle Swarm Intelligence, Neuro Fuzzy, Neuro-Genetic, and so on have ceded better results. In this paper, the optimization of the **PID** **controller** additions has been completed utilizing by Genetic Algorithms (GA), Multi- Objective Genetic Algorithms (Mobj-GA) and Stimulated Annealing, while utilizing the Zieg- ler-Nichols parameters for the determination of the lower and upper headed points of confinement for the introduction of **PID** parameter. At that point, the improvement of the **PID** **control**- lers for the estimation of the best **PID** parameters has been finished concerning the goal capacity, expressed as, "Aggre- gate of the fundamental of the squared slip and the squared **controller** yield veered off from its enduring state" As per the outcomes got in this paper, impressively better results have been acquired on account of the genetic **algorithm**, when con-

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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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This paper is continuation for our work presented in [1]. **DC** **motors** are applied in one way or the other in factories, home appliances, computers to robots, airplanes, and cars. They are more widely used than the related machines, the AC **motors**, owing to their diverse favorable characteristics. These characteristics some of which are, linear speed **control** properties and high starting torque. There are more than one types **DC** **motors** and all these types have numerous benefits over AC **motors** which include: less heat production, simpler controllers used, have higher efficiency, can offer precise position **control**, can produce very close to constant torque and they are easily controllable [2-11]. For that reason, the adoption of **DC** **motors** will reduce the amount of energy consumed and improve the efficiency of the machines they are installed. The improvement of **DC** **motors**’ **control** arrangement to enhance their response characteristics is one way of achieving these. Is so doing, they will be able to accomplish their work efficiently without the necessarily increasing the capacities of **motors** alongside their **control** circuits [12-16].

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The process **control** techniques in the industry have made great advances during the past decades. A no of **control** methods such as adaptive **control**, neural **control**, and fuzzy **control** have been studied. Among them, the best known is the proportional-integral- derivative (**PID**) **controller**, which has been widely used in the industry because of its simple structure and robust performance in a wide range of operating conditions. Unfortunately, it has been quite difficult to tune properly the gains of **PID** controllers because many industrial plants are often burdened with problems such as high order, time delays, and nonlinearities. It is hard to determine **optimal** or near **optimal** **PID** parameters with the classic **tuning** method (Ziegler-Nichol’s method for instance). For these reasons, it is highly desirable to increase the capabilities of **PID** controllers by adding new features.

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A. Conventional Approach - Ziegler Nichols Method The **control** system performs poor in characteristics and even it becomes unstable, if improper values of the **controller** **tuning** constants are used. So it becomes necessary to tune the **controller** parameters to achieve good **control** performance with the proper choice of **tuning** constants. **Controller** **tuning** involves the selection of the

A proportional–integral–derivative **controller** (**PID** **controller**) widely used in industrial plants. Because it is simple and robust that is commonly used feedback **controller**. **PID** **control** with its three term functionality covering treatment to both transient and steady-states response, offers the simplest and yet most efficient solution for many real world **control** problems [3]. In this paper, a scheduling **PID** **tuning** parameters **using** particle swarm optimization (**PSO**) strategy for a **DC** motor speed **control** is proposed

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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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Abstract The industry processes involving punching, lifting, and digging usually require high precision, high force and long operating hours that increase the prestige in the usage of the electrohydraulic actuator (EHA) system. These processes with the companion of the EHA system usually possess high dynamic complexities that are hard to be controlled and require well-designed and powerful **control** system. Therefore, this paper will involve the examination of the designed controllers which is applied to the EHA system. Firstly, the conventional proportional-integral-derivative (**PID**) **controller** which is the famous **controller** in the industry is designed. Then, the improved **PID** **controller**, which is known as the fractional order **PID** (FO-**PID**) **controller** is designed. After that, the design of the gradually famous robust **controller** in the education field, which is the sliding mode **controller** (SMC) is performed. Since the controller’s parameters are essentially influencing the performance of the **controller**, the meta-heuristic optimization method, which is the particle swarm optimization (**PSO**) **tuning** method is applied. The variation in the system’s parameter is applied to evaluate the performance of the designed controllers. Referring to the outcome analysis, the increment of 59.3% is obtained in the comparison between **PID** and FOPID, while the increment of 67.13% is obtained in the comparison of the **PID** with the SMC **controller**. As a conclusion, all of the controllers perform differently associated with their own advantages and disadvantages.

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Overview of Particle Swarm Optimization. Many problems have not an exact solution that gives the results in a reasonable time. For overcoming these problems some metaheuristics methods offer an approached solution after much iteration are recently proposed. Among these methods, the **PSO** **algorithm** has a general principle to be applied in many fields of optimization problems. **PSO** is a stochastic optimization **algorithm** developed by Eberhart and Kennedy, inspired by the social behaviour and fish schooling of bird flocking. Each particle in the swarm is a different possible set of the unknown parameters of the objective function to be optimized. The swarm consists of N particles moving around in a D-dimensional search space. Each particle is initialized with a random position and a random velocity [17, 18]. The new velocity can be calculated by the fellow formula.

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The main objective of this work is to improve the performance of **PID** **controller** for process variables. The **PID** **controller** is used to **control** the process variables. The parameters of **PID** **controller** has been tuned by **using** **PSO**/GA **algorithm** because manual **tuning** of the **PID** **controller** is a tedious process and it takes very long time because it is based on hit and trial method. So to make the **PID** **controller** speed faster, **PSO**/GA **algorithm** is used. **PSO**/GA algorithms tune the **PID** **controller** parameters by reducing the fitness function which is error function. In each iteration of **PSO** **algorithm** and in each generation of GA **algorithm**, the value of error function is reduced and gets the steady state value. The performance parameters (rise time, settling time, steady state error and overshoot) are improved by **using** **PSO**/GA **algorithm**. The codes for **PSO** **algorithm** and GA **algorithm** were written in Matlab. The SIMULINK models for different process variables was developed and simulated through MATLAB m files containing **PSO**/GA code.

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For the **tuning** of the parameters of the membership functions of a fuzzy **controller** a novel **PSO** **algorithm** has been developed. The **algorithm** for the fuzzy **controller** has been encoded in MATLAB but a block diagram strategy is enabled to explain the **algorithm**. A SIMULINK model has been used.The Plant used is an armature controlled **DC** Motor. Conventional controllers like PI and **PID** controllers fail in case of non linearities and may generate steady state error[1]. In such a case a fuzzy **controller** is used which is basically a non-linear element whose parameters are tuned **using** Particle Swarm Optimization Technique (**PSO**) subject to the condition that steady state error is to be minimized. The quantity to be controlled is the speed of the **DC** Motor. Therefore error in speed is to be minimized. **PSO** technique is a very uncertain **algorithm** that may or may not converge to the optimized values. Nevertheless we got optimistic simulation results. As such it could overcome the limitations of conventional controllers[1].

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Particle swarm optimization (**PSO**) is a metaheuristic **algorithm** based on swarm behaviour observed in nature such as in bird flocking or fish schooling. It attempts to mimic the natural process of group communication of individual knowledge, to achieve some optimum property. **PSO** searches the space of an objective function by adjusting the trajectories of individual agents, called particles. Each particle traces a piecewise path which can be modelled as a time-dependent position vector.

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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Generally, the speed closed-loop is used to **control** the BLDC **motors** and the speed **controller** based on proportional-integral-derivative (**PID**) is widely adopted in practical application. **PID** **control** is one of the most popular **control** strategies and has been com- monly used in industrial **control** systems because of its simplicity, clear functionality, robustness and effective- ness [7,8]. However, BLDC motor is a multivariable nonlinear system, the conventional **PID** **controller** **using** in this system always exist some de ﬁ ciencies. It is so sensitivity to the system uncertainties that the **control** performance can be seriously degraded under parameter variations. Moreover, the conventional **PID** **controller** is also difﬁcult to tune the **control** parameters to adjust the high precision and rapid speed of system dynamic performance and static

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A proportional–integral–derivative **controller** (**PID** **controller**) is a generic **control** loop feedback mechanism (**controller**) commonly used in industrial **control** systems– a **PID** is the most frequently used feedback **controller**. A **PID** **controller** calculates an "error" value as the difference between a measured plant variable and a preferred set point. The **controller** attempts to reduce the error by **tuning** the plant **control** inputs. The proportional, integral, and derivative terms are adding to calculate the output of the **PID** **controller**. Defining u(t) as the **controller** output, the **PID** **algorithm** final form is:

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The **DC** **motors** are in general much more adaptable speed drives than AC **motors** which are associated with a constant speed rotating field. It is observed that most of the industry is operating under stress condition further load parameter and **control** variable exhibit uncertainness in real practice and in fact these are random variables. Calculated values of load variable normally contain various inaccuracies. It has been observed that error may vary in the range of 5-10%. A few percentage error may be required tolerable in the area of the load speed controlling where these inaccuracies in the entire **controller**. In such situation minor inaccuracy in speed **control** are of little concern. Further the speed **controller** can always be designed to have sufficiently low effect on the non linearity of **DC** motor; so as to worst effect of parameter uncertainty can be accounted. In real time operation, the situation is different; design **controller** may encounter situation never imagined by designer before it took its present shape. Hence, in real time operation condition, risk of affecting nonlinearity of motor is always present. Here it is designed a **controller** which not affects the nonlinearity in **DC** motor.

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Abstract—The position **control** study of **DC** servo **motors** is very important since they are extensively deployed in various servomechanisms. Normally **PID** controllers are used to improve the transient response of **DC** servo **motors**. At present, most **tuning** methods are designed to provide workable initial values, which are then further manually optimized for a specific requirement. This paper presents a flexible and fast **tuning** method based on genetic **algorithm** (GA) to determine the **optimal** parameters of the **PID** **controller** for the desired system specifications. Simulation results show that a wide range of requirements are satisfied with the proposed **tuning** method.

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