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 tuningprocess 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.
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 controlsystem begins operating to maintain an average power equal to the set point. The pitch controlsystem 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.
This paper presents the development of an intelligent controller for vibration suppression of a horizontal flexible plate structure using hybrid Fuzzy–proportional–integral–derivativecontroller tuned by Ziegler–Nichols tuning rules and intelligent proportional–integral–derivativecontroller 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–derivativecontroller. Two types of controllers were proposed in this paper which are the hybrid Fuzzy–proportional–integral–derivativecontroller and intelligent proportional–integral–derivativecontroller 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–derivativecontroller with 32.04 dB attenuation. The experimental work was then conducted for the best controller to confirm the result achieved in the simulation work.
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 controlsystem is provided by .
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 Derivativecontroller adds a finite zero to the open loop plant Transfer function and improves the transient response. The Integralcontroller 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 controlProportional, Integral and Derivativecontrol.
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 improvedtuning 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 tuningalgorithm. Effect of noise on PID parameters during online tuningprocess 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.
Abstract: In this Paper, a novel meta-heuristics algorithm, namely the Firefly Algorithm (FA) is applied to the ProportionalIntegralDerivative (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 controlsystem performance in terms of time domain specification.
Liquid level controlsystem is commonly used in many processcontrol 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.
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.
Despite the potential of the modern control techniques with different structure, ProportionalIntegralDerivative (PID) type controller is still widely used for AVR sys- tem . 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.
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 . 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 . 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].
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.
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.
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 .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 processcontrol emerged in the 1940s. In processcontrol 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 controlsystem. 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.
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
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 .
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).
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 . A high gain adaptive output feedback control to a magnetic levitation system is discussed in . 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 . Other interesting adaptive gain scheduling control techniques for real practical applications are given in , , , , . 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 . The simulated external disturbance can be applied
GSA is gradually gaining attention from research community . 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) . In , 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 , which GSA was found to outperform the modified PSO.