It consists of an amplifier **stage**, which increases the magnitude of the input quantity for further manipulation. It is followed by the exciter **stage** which controls the current to the excitation field and directly influences the speed of the generator, which is the next **stage** of the block diagram. The sensor **stage**, which is an **integral** part of the feedback loop monitors the control variable and directs some portion of it to the summing block.

Speed, torque, and phase currents/voltage are important variables in speed control of BLDC machine. In controlling the speed of BLDC motor the system is closed-loop where actual speed is the feedback parameter that is obtained from the speed transducer. A single **stage** PID controller consisting of a speed loop has been used for the closed loop drivesystem. PID is simplest and famous controller in Industry and Automation for motor application. The purpose of the system controller is to minimize the error between the reference speed and the feedback speed. In addition, it improves dynamic response and behavior of the system.The function of the current limiter is to maintain the motor phase currents at their desired constant value for each 120° interval that a particular phase is energized. The current is limited by controlling the switch duty cycle to ensure that device current ratings and the motor current rating are not exceeded, especially during start-up conditions or low speed operation. The amount of current ripple is controlled by the switching frequency of a PWM waveform.The gate driver is a drive circuitry which controls the current flow through the MOSFET switches. Hence the current flow to motor windings is also controlled. This includes the direction and magnitude of the current flow.

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This project proposes a novel dc to dc high step up chopper and good efficiency for photovoltaic renewable energy system in order to simplify the power system and their cost. The proposed power converter consists of a boost fused dc to dc converter added to cascaded H- bridge inverter circuit across the load. A proposed system includes three major units first is an operation type wherein power is delivered to dc chopper from solar photovoltaic (PV) cell, second is a single **stage** type design of dc to dc converter with high voltage gain to regulate the required output voltage and third for a closed loop control mechanism used to produce the good efficiency and control output for the system. Using MATLAB/Simulink the proposed scheme is developed.

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On the off chance that λ=0 and μ=0, at that point it is simply just a **proportional** (P) controller, If λ=0 and μ=1, at that point it turns into a **proportional**-**derivative** (PD) controller, If λ=1 and μ=0, at that point it turns into a **proportional**-**integral** (PI) controller and If λ=1 and μ=1, at that point it moves toward becoming whole number PID. These whole number request controllers are spoken to as focuses in the λ-μ plane as appeared in Fig. 2 (a). Accordingly FOPID controller sums up the PID controller and extends it from point to whole λ-μ plane as appeared in Fig. 2 (b) hence offering the significantly more extensive determination of tuning parameters subsequently greater adaptability in the controller configuration prompting more exact control. The LSA methods are utilized to decide the ideal requirements of PI, PID and FOPID controllers with the target to limit the **Integral** square of area control error, which can be defined in the accompanying way: 𝐽 ∫ (𝛽 ∆𝐹 𝛽 ∆𝐹 ∆𝑃

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At the beginning of the tuning process, all the PID controllers were turned off; and the PD controller for the link 2 is also turned off. Then the **proportional** gain of the link 1 was tuned simultaneously with the **derivative** gain of the link 1. Unlike the Ziegler-Nichols procedure, where the **proportional** gain is tuned until there is overshoot in the system, two link flexible manipulator is a highly unstable system; this method does not work well for the gain tuning for such unstable system. According to Yun et al. [15] for optimum performance of the PID controllers, the gains: KP, KI and KD must be tuned jointly. Also, Eriksson and Wikander [16] observed that manual tuning is still the most favoured method despite different types of tuning methods available. At certain point, the **derivative** of the link 2 PD controller has to be tuned to be able to further tune the link 1PD controller in order to obtain a satisfactory performance. This is because of the coupling effect in the system. The PD gains are shown in Table 2.

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Recently, many modern control methodologies such as nonlinear control, optimal control, variable structure control and adaptive control of shell and tube heat exchanger. However, these approaches are either complex in theoretical bases or difficult to implement. PID control with its three term functionality covering treatment to both transient and steady state response, offers the simplest and yet most efficient solutions to many real world control problems. The design of PID controller requires the value of parameters such as **proportional** gain (K P ), **integral** time constant (K I ) and

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This paper present a particles swarm optimization (PSO) method for determining the optimal **proportional** – **integral** **derivative** (PID) controller parameters, for the control of nonholonomic mobile robot that involves path tracking using two optimized PID controllers one for speed control and the other for azimuth control. The mobile robot is modelled in Simulink and PSO algorithm is implemented using MATLAB. Simulation results show good performance for the proposed control scheme.

From the paper titled ‘Model based Controller Design for a Spherical Tank’ they concluded SIMC based PI controller as the best method with lesser values of IAE and ISE, among all other methods like ZN, IMC, Cohen- Coon and Chien. From the paper titled ‘Design of Fuzzy Logic Controller for a Spherical tank system and its Real time implementation’ it is concluded that fuzzy logic controller is the best with lesser values of **Integral** Square Error (ISE) and faster settling point. From the paper titled ‘Design and Implementation of Skogestad PID Controller for Interacting Spherical Tank System’ they concluded Skogested method as the best method of tuning. From the paper titled ‘Design and Implementation Of IMC Based PID Controller’ they found that the standard IMC filter has a better set point and at the end of simulation they concluded that IMC based controller is better compared to other conventional methods as it has minimum settling time.

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effectiveness of the proposed approach, the results of proposed algorithm are compared with NCS, **integral** controller and ZN -PID enforced to the same power system. Fig. 3 illustrates the comparative analysis made between the transient performances of LFC with different approaches. The measuring parameters affiliated to this figure are scheduled in Table II and it clearly reveals that the total error evaluated using 𝐽 1 in the proposed

The advanced model captures cerebral haemodynamic autoregulation by using a Proportional-Integral-Derivative PID controller to modify efferent artery resistances and partial pressures of[r]

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The problem which always appears to the conventional PID equation is when the good response set point is expected, the result is not good to the load changes. On the other hand, when good response to the load is expected, the result of setting point is a very high overshoot. Ideally, the good controller gives good response to the set point or changing load. And to overcome the existing weakness, according to reference [7], weighing factor β is added to the **proportional** side. According to the reference [8] if β is adjusted to the value that is less than one, the overshoot can be overcome and this adjustment does not influence the response caused by the changing load. The value β is modulated by the controller at the time and condition change. Therefore the PID equation above becomes:

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Abstract: This paper is based on a major research project on the development of a novel design of **proportional** **integral** **derivative** (PID) controller for nonlinear systems. The proposed design has superior features, including easy implementation, stable and fast convergence characteristic , and good computational efficiency.

Analytical studies on transient response, stability and reliability gives dynamical performance of conventional **proportional** **integral** **derivative** (PID) controllers in normal operating conditions, that the conventional controllers have large overshoots and long settling times. Also, optimizing time for control parameters, especially PID controllers, is very long and the parameters are not calculating exactly what they are supposed to be meant. Also, it has been known that conventional controllers generally do not work well for non- linear, higher order and time-delayed linear, and particularly complex, vague, for the systems which so many uncertainities and for those do not have precise mathematical models.

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A **proportional**–**integral**–**derivative** controller (PID controller) is the most common form of feedback controllers and widely is used in industrial control systems (see Fig 1.5). A PID controller calculates an “error”valueasthedifference between a desired set-point and a measured process variable. The controller signal ( attempts to minimize the error by adjusting the process inputs as follow:

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PID controllers have widely been used in process control. Because of simple designing of PID controller, they can easily control various large industrial processes. The PID controllers are used to control the dynamic response & eliminate the steady-state error. The **proportional** term reduces the error response to disturbances. The **integral** term shows that a pole is added at the origin which results in increasing the system type & therefore reducing the steady-state error. The **derivative** term shows that a zero is added to the open loop plant transfer function & therefore it improve the transient responses & stability of the system. To obtain the optimum results from this system, the gain of PID controller must be tuned in such a way that the close loop system produces desires result. Thus, PID controller is considered as a simple, easy &robust controller. The transfer function of well-known PID controller is given by:

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the mobile part of the robot is reduced to the three legs and the mobile platform. Consequently, higher velocities and ac- celerations of the mobile platform can be achieved. Another benefit is that the legs are made of only thin rods, thus, reducing the risk of leg interference. Further, the geomet- rical/physical parameters of the manipulator are also opti- mized for a given constant orientation workspace. The in- verse dynamic model is obtained using the Lagrangian dy- namic formulation method (Abdellatif and Heimann, 2009). The proposed robust task-space trajectory tracking controller is based on a centralized **proportional**-**integral**-**derivative** (PID) control along with a nonlinear disturbance observer. The control schemes for parallel manipulator may be prin- cipally separated into two types, joint-space control estab- lished in joint-space coordinates (Davliakos and Papadopou- los, 2008; Honegger et al., 2000; Kim et al., 2000; Nguyen et al., 1992; Yang et al., 2010), and task-space control designed based on the task-space coordinates (Kim et al., 2005; Ting et al., 2004; Wu and Gu, 2005). The joint-space control ap- proach can be readily employed as an assemblage of several independent single-input single-output (SISO) control sys- tems using the data on each actuator feedback only. A classi- cal PID control in joint-space along with gravity compensa- tion has been employed in industry, but it does not always as- sure a great performance for parallel manipulators. However, the proposed robust task-space control approach improves the overall control performance by rejecting the uncertainty and nonlinear effects in motion equations. The rejections of system or model uncertainty, unknown external disturbance and nonlinear effects in the system motions have been com- pleted in the proposed control scheme with the help of an equivalent control law; a feed-forward control scheme and a nonlinear disturbance observer along with the nonlinear PID control scheme. In the proposed task-space control method, the desired motion of the end effector in task-space is used directly as the reference input of the control scheme. That is, the motion of the end effector can be obtained from the sys- tem sensors and compared with the reference input to form a feedback error in task-space. Therefore, an exact kinemat- ics model is not required in the task-space control, and thus this method is sensitive to joint-space errors or end effector pose errors due to joint clearances and other mechanical in- accuracies. The validity of the proposed control scheme is demonstrated with the help of virtual prototype experiments. The performance of the proposed control scheme including closed-loop stability, precision, sensitivity and robustness is analysed in theory and simulation.

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Table 2: Controller Tuning Formula for IMC The gain values of **proportional** **integral** and **derivative** modes of controller using IMC tuning technique is given below. From these values, the tuned response is obtained using LABVIEW. Kp=0.4498 K i = 0.7197 K d =0.41684

A **Proportional** **Integral**-**Derivative** (PID) is a control loop feedback mechanism used in industrial control system. In industrial process a PI controller attempts to correct the error between a measured process variable and desired set point by and then provides the corrective action that can adjust the process accordingly [2]. The PI controller calculation involves two separate modes the **proportional** mode and the **integral** mode. The **proportional** mode determines the reaction to the current error and the **integral** mode determines the reaction based on the recent error. The weighted sum of the two modes provides a corrective action to the control element. PI controller is widely used in industry due to its ease in design and simple structure. PI controller algorithm can be implemented as

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PID controller is also includes as it is required to keep the blood glucose level under the range of 50-120. PID, **proportional** **integral** **derivative** loop feedback mechanism it is which continuously calculates the blood glucose level and keep it under the range however when the supply meal glucose rises suddenly and abruptly PID shows a slow progress and at the range which is constantly above the 200 mark PID fails partially to maintain the range hence the process is transferred to the S-Reactor block which regulates the body blood glucose level using the insulin and to maintain the system stability, as S-reactor do provides the assistance to PID in maintaining the blood glucose level range but it doesn’t helps the system to maintain its stability for that Fuzzy Logic has been introduced and it helps the system to maintain its stability.

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From the simulation and comparison responses, it is observed that the PSO-PID provides fast response with minimum rise time and minimum settling time i.e PSO-PID provides good control accuracy and faster convergence than other methods. The optimized PID settings, minimized values of the objective function and **integral** performance indices for I and II operating regions are given in Table. 7 and 8 (See Annexure). From the table, it is found that PSO-PID gives better optimized PID settings for the CSTR process under identified operating regions. For the first operating region C 01 =0.0795,, T 01 =443.4566 and q co1 = 97,

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