PID controller

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													Speed control of bldc motor using pid controller and fuzzy pid controller

1. Speed control of bldc motor using pid controller and fuzzy pid controller

motors have numerous focal points over brushed DC motors and prompting motors, for example, a superior velocity versus torque qualities, high element reaction, high effectiveness and unwavering quality, long working life (no brush disintegration), silent operation, higher pace reaches and lessening of electromagnetic obstruction (EMI). In the investigation, the motor pace is kept steady and the heap is differing from zero to top torque. The motor pace holding steady under fluctuating burden condition is an alluring way to deal with control the motor under shifting burden condition. By utilizing this methodology, PID controller is utilized in this framework to beat the undesirable undershoot of motor velocity because of fluctuating burden sway at certain strange conditions. The proposed framework model utilizing PID control plan, is composed on Mat lab and reenactments performed on Simulink stage. In this paper, the pace control procedure of independently energized DC motor under differing load condition is given reenactments. The DC motor model is produced and Mat lab recreation has been done under exasperating burden conditions on Simulink stage. Fuzzy PID controller is utilized in this control framework keeping in mind the end goal to get craved reaction of the framework. The operation and tuning principles of fuzzy PID controller, PID controller is likewise talked about. Toward the end, test result is exhibited and talked about.
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DIRECT SYNTHESIS APPROACH FOR
DESIGN OF PID CONTROLLER

DIRECT SYNTHESIS APPROACH FOR DESIGN OF PID CONTROLLER

The dead time is the delay from when a controller output (CO) signal is issued until when the measured process variable (PV) first begins to respond. The presence of dead time, Өp, is never a good thing in a control loop for any process, as Өp becomes larger, the control challenge becomes greater and tight performance becomes more difficult to achieve. The control algorithm for a dead time process is actually very simple. A PID controller does a fine job. You can also use a fancy model predictive or model based, pole cancellation, or Internal Model Control, or Dynamic Matrix controller or Dahlin. They all amount to the same thing of using a model inside the controller. Let’s just call them all Model based controllers. Model based controllers can seek out slightly better response than PID but at the sacrifice of robustness. The robustness penalty using model based control (made to be even the same speed as a PI) is severe the dead time of the process can go up or down and the loop will go unstable. Increasing dead time with any process will always eventually drive you unstable. But decreasing dead time to make you unstable is weirdly counter-intuitive. If the dead time were to go down with a PID controller, the loop would remain stable. So why not keep it simple and just use a PID controller?
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HARDWARE IMPLIMENTATION OF FPGA BASED PID CONTROLLER

HARDWARE IMPLIMENTATION OF FPGA BASED PID CONTROLLER

The PID controller follows the classical structure. It contains two saturation Blocks, one for the integral part and the other for the overall sum (see figure 5). The controller has a pipeline structure of three stages, in other words, it needs three clock cycles to perform all the operations. In order to improve the area and speed, hardware multipliers have been used . These multipliers are included in the Spartan 3 family of Xilinx and subsequent FPGAs. These multipliers have 15 bit input data bus and are signed. This leads to optimum implementation when the fixed point implementation uses less than 15 bit in two’s complement [4].
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Linear Programming for Optimum PID Controller Tuning

Linear Programming for Optimum PID Controller Tuning

Seeking a faster and more efficient methodology for PID controller, this paper presents the development of a novel technique to tune the proportional-integral and derivative gains. As stated before, this device has been widely used as shown in many works encouraging the investigation of the possible improvements when it is tuned by using Linear Program (PL) based on interior point optimization, well known in several areas [16] [17] but still unexplored in the proposed area.

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Performance Analysis of Fuzzy PID Controller Response

Performance Analysis of Fuzzy PID Controller Response

We have developed the Fuzzy PID Controller algorithm using MATLAB/SIMULINK platform. The described PID controller also has developed with all fuzzy rules. Firstly we design the PID controller with transfer function (DC motor) as a plant. But the result of transfer function is poor with only PID. So that fuzzy controller is combined with PID controller. With fuzzy PID controller the performance of transfer function is better than PID controller. Like rise time is increase, settling time is decrease, zero overshoot.
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Firefly Algorithm Based SISO PID Controller

Firefly Algorithm Based SISO PID Controller

Thus the kp, ki & kd values are determined by using firefly algorithm for a bio-reactor have been determined. The values obtained from firefly algorithm is compared with the other methods of algorithm. Placing appropriate values for PID controller will improve the response of system. These results are obtained from simulation and results are shown in above section. It will be simulated using MATLAB Software.

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INTELLIGENT CONTROL USING ADAPTIVE PID CONTROLLER

INTELLIGENT CONTROL USING ADAPTIVE PID CONTROLLER

PID controllers are widely used in process industries. Selecting the right proportional, Integral and derivative settings on-line is a real challenge. Automatic tuning and stabilizing of PID controllers have been a topic of great significance over the past many years [1]. PID control system involves complex stability problem due to the lack of sufficient number of tuning parameters compared to the order of the plant. Therefore it is important to consider the problems of auto-tuning of near optimal PID controllers and their stability with the three adjustable parameters namely Proportional, Integral and Derivative. Auto-tuning of controller is explained in detail in the book by Astrom and Hagglund [2]. Recent research works in similar fields are Self-tuning approach [3][4], standard relay feedback and auto-tuning using neural networks [5],[6],[7][8], stochastic divergence optimization method [9], Fuzzy self tuning controllers [10][11], and reinforcement learning algorithm [12]. In this paper we look at a simple PID controller where the process is ASPR (Almost strict positive realness). It has been proved that a linear time invariant (LTI) system can be stabilizable by output feedback, if the controlled plant is ASPR [13]. This novel PID controller always ensures stability so that we don’t have to worry about the stability regions. The effectiveness of this method is tested by simulations and experimentation on a non-linear level control system approximated to a first order system with time delay.
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On fractional predictive PID controller design method

On fractional predictive PID controller design method

Abstract: A new method of designing fractional-order predictive PID controller with similar features to model based predictive controllers (MPC) is considered. A general state space model of plant is assumed to be available and the model is augmented for prediction of future outputs. Thereafter, a structured cost function is defined which retains the design objective of fractional-order predictive PI controller. The resultant controller retains inherent benefits of model-based predictive control but with better performance. Simulations results are presented to show improved benefits of the proposed design method over dynamic matrix control (DMC) algorithm. One major contribution is that the new controller structure, which is a fractional-order predictive PI controller, retains combined benefits of conventional predictive control algorithm and robust features of fractional-order PID controller.
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Motion Control Analysis of Quadcopter with PID Controller

Motion Control Analysis of Quadcopter with PID Controller

The P term calculates a proportional response to the error, for example if the quad is tilted a little bit then it can only correct a little bit and does not overshoot the target value, the I controller will ramp up the response if it has not lowered fast enough, which in some cases can be good for example if there is wind and the proportional term is not correcting enough, but that too can cause problems with overshooting the error (requiring a negative error for the same time as there was a positive error), the D controller will dampen the P and I controllers as you get closer to the set point by measuring the rate of change of the system and slowing it as it approaches the set point. Using the PID controller can make the rise time decrease and increases the settling time Using only the P and D controllers can sometimes give better results especially in less demanding environments and can be easier to tune. PD controller works great for multicopters expected to make less aggressive maneuvers where a well tuned PID controller can perform better with very aggressive environments.
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PID Controller Tuning and Its Case Study

PID Controller Tuning and Its Case Study

Designing and tuning a Proportional Integral Derivative (PID) controller appears to be conceptually impulsive, but it is difficult in practice, if multiple objectives such as short transient response and high stability are to be achieved. PID controller has the good control dynamics including zero steady state error, short rise time, no oscillations and higher stability. The necessity of using a derivative gain component in addition to the PI controller is to remove the overshoot and the oscillations occurring in the output response of the system. The main advantages of the PID controller is that it can be used with higher order processes including more than single energy storage.
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Attitude Control of a Quadrotor with Optimized PID Controller

Attitude Control of a Quadrotor with Optimized PID Controller

In this paper a SISO control structure of quadrotor is presented. Analytical optimization method is used to tune a conventional PID controller for stabilization and dis- turbance rejection of quadrotor. The time domain per- formance of designed control structure is evaluated with IAE objective function. The results of simulation in Si- mulink/Matlab software, illustrate the efficient of ap- plied control strategy.

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The PID controller design using genetic algorithm

The PID controller design using genetic algorithm

It is known that PID controller is employed in every facet of industrial automation. The application of PID controller span from small industry to high technology industry. For those who are in heavy industries such as refineries and ship-buildings, working with PID controller is like a routine work. Hence how do we optimize the PID controller? Do we still tune the PID as what we use to for example using the classical technique that have been taught to us like Ziegler-Nichols method? Or do we make use of the power of computing world by tuning the PID in a stochastic manner?
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PSO Based PID Controller for BLDC Motor

PSO Based PID Controller for BLDC Motor

The Proportional-Integral-Derivative (PID) controller is the most common and useful algorithm in most applications for stability improvement. In many cases, feedback loop are controlled using the PID algorithm. The main reason is to attain a set point irrespective of disturbances or any variations in characteristics of any form. The PID controller is designed to reduce errors between the measured

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Gain Scheduling of PID Controller for Level Control System & Comparative Analysis of PID Controllers

Gain Scheduling of PID Controller for Level Control System & Comparative Analysis of PID Controllers

Gain scheduling is an adaptive control system. It is used for robust control design. This method is suitable if the changes in the parameters of the plant are frequent and more. In this method the set of controller parameters are found for different set of plant sets. Normally for such control strategy it is necessary to have the plant dynamics of sluggish nature. The feedback controller can be simple PID controller. In the literature it is observed that the researchers have tuned different sets of PID controller for variation in the parameters of the plant. These PID controllers can be fractional order PID controller. The advantage of PID is that it has 5 tuning parameters as compared to 3 parameters of PID controller. Due to more number of tuning parameters the number of sets of FOPID can be reduced for the same variation in the plant. The literature have proved that the FOPID controller are more robust and gives improved characteristics for the set of specifications. In the literature there is no work on FOPID gain scheduling controller. Further the method can be extended by optimization techniques.
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Brushless DC Motor Speed Control using PID Controller, Fuzzy Controller, and Neuro Fuzzy Controller

Brushless DC Motor Speed Control using PID Controller, Fuzzy Controller, and Neuro Fuzzy Controller

Brushless dc motors (BLDC motors) are commonly used nowadays in industry and at many applications according to its very high speed with a very compact size in comparison to the older motors with brushes, moreover the importance of being powered by direct current (DC) and without all disadvantages of using brushes, which is convenient to many applications like hard drivers, CD/DVD players, electric bicycles, electric and hybrid vehicles, CNC machines and Aero modeling. The purpose of this paper is to control the speed of a brushless dc motor by using PID controller, Fuzzy logic controller, and Neuro fuzzy controller. According to these varieties of control techniques which used to control the speed, we have many parameters which used to assess that which controller will be better to use.
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Modeling of SVC Controller based on Adaptive PID Controller using Neural Networks

Modeling of SVC Controller based on Adaptive PID Controller using Neural Networks

The BP network is a multilayered simple network which consists of three layers BP NN and they are an input layer, hidden layer and an output layer. It contains four input neurons, five hidden neurons and three output neurons. The input neurons are the error e(k), output voltage of the plant y(k), reference voltage (Vref.) and bias, where it may become possible to adjust the gains of PID controller adaptively by the measurement of the above input neurons data depending on BP algorithm. The output neurons represented by the three tuned parameters of PID controller which represented by Kp, Ki and Kd. The structure of the network is shown at Figure 4:
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Comparative study of PID controller and sliding mode controller for the setpoint overshoot method

Comparative study of PID controller and sliding mode controller for the setpoint overshoot method

Fig.1, Block diagram of PID controller in feedback loop It shows PID controller which continuously calculates an error value e(t) as the difference between desired setpoint SP = r(t) and measured process variable PV = y(t), and applies a correction based on proportional, integral, and derivative terms. The controller attempts to minimize the error over time by adjustment of control variable u(t), such as the opening of a control valve, to a new value determined by weighted sum of control terms. The overall control function can be expressed mathematically as:
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Implementation of Fuzzy Pid Controller And Performance Comparision With Pid For Position Control Of Dc Motor

Implementation of Fuzzy Pid Controller And Performance Comparision With Pid For Position Control Of Dc Motor

The Controllers are implemented to track the changes occurred in position of DC motor (Servo Response). The Z-N open loop tuning rules are considered to design the PID controller for tracking the set point. The parameters of the Z-N Closed loop tuning rules are obtained by conducting the Relay Feedback Test. The results are validated by considering the time domain parameters. The fuzzyPID controller is implemented to track the change in position of DC motor by using MATLAB/Simulink. The simulation results proven that the fuzzyPID control method is more effective way to enhance stability of time domain performance of the DC motor. The fuzzy PID controller based adjustable closed-loop for DC motor system has been developed.
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Pitch Control of Wind Turbine through PID, Fuzzy and an Adaptive Fuzzy PID Controller

Pitch Control of Wind Turbine through PID, Fuzzy and an Adaptive Fuzzy PID Controller

response with lower delay time and rise time, it has oscillations with a peak overshoot of 11.8%, which causes the damage in the system performance. To suppress these oscillations fuzzy logic controller is proposed to use. From the results, it can be observed that, this controller can effectively suppress the oscillations and produces smooth response, but it has more delay time, rise time and settling time. By using fuzzy adaptive PID controller, where the PID gains are tuned by using fuzzy logic concepts, the results showed that this design can effectively suppress the steady state error and the system has minimum delay time, fast rising, quick settling and stability. From the analysis, we conclude that fuzzy adaptive PID controller gives relatively fast response for unit step input. This technique is much better to realize the control of pitch system and to guarantee the stability of wind turbine output power.
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Mathematical Model for Auto Tuning of PID Controller

Mathematical Model for Auto Tuning of PID Controller

In this paper we have discussed about Modeling of Auto Tuning Method. Auto Tuning is a process in which the characteristics of a process parameters like Peak-Overshoot, Settling Time, Rise Time, Steady-State Error etc. are properly adjusted in order to get a good response characteristics. Thus , during Auto Tuning Method these parameters get their value decreased or become more accurate compare to the previous process characteristics. It is a simple and effective method for tuning of PID(Proportional-Integral-derivative) controller based on closed loop control valve logic. Since a proposed closed loop controller uses conventional algorithm and output membership functions, this controller can be considered as a conventional PID controller whose parameters are tuned as per the requirement of the system based on a mathematical model. The performance comparison of conventional PID with Auto tuned PID type controllers has been done in terms of several performance measures such as Peak-Overshoot, Settling Time ,Rise time for a step input signal. Simulation results show the effectiveness and robustness of the proposed Auto Tuning mechanism. A simulation analysis of a wide range of Linear and Non Linear process is carried out and a comparison result is obtained based on it.
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