Fractional-Order PID Controllers for Temperature Control:A Review

Fractional-order proportional integral derivative (FOPID) controllers are becoming increasingly popular for various industrial applications due to the advantages they can offer. Among these applications, heating and temperature control systems are receiving significant attention, applying FOPID controllers to achieve better performance and robustness, more stability and flexibility, and faster response. Moreover, with several advantages of using FOPID controllers, the improvement in heating systems and temperature control systems is exceptional. Heating systems are characterized by external disturbance, model uncertainty, non-linearity, and control inaccuracy, which directly affect performance. Temperature control systems are used in industry, households, and many types of equipment. In this paper, fractional-order proportional integral derivative controllers are discussed in the context of controlling the temperature in ambulances, induction heating systems, control of bioreactors, and the improvement achieved by temperature control systems. Moreover, a comparison of conventional and FOPID controllers is also highlighted to show the improvement in production, quality, and accuracy that can be achieved by using such controllers. A composite analysis of the use of such controllers, especially for temperature control systems, is presented. In addition, some hidden and unhighlighted points concerning FOPID controllers are investigated thoroughly, including the most relevant publications

Design of Nonlinear PID Controllers and Their Application to a Heat Exchanger System for LNG-fuelled Marine Engines

Excessive use of fossil fuels resources is adding several types of greenhouse gases which make the earth warmer. Emissions from ship's exhausts contribute to global climate change, too. The International Maritime Organization (IMO) has adopted regulations to reduce the emission of air pollutants from international shipping, such as major air pollutants, carbon dioxide (CO2), nitrogen oxides (NOx), and sulphur oxides (SOx) under Annex VI of the 1997 MARPOL protocol. Likewise, as regulations on the emission of major air pollutants have become internationally strict, the development of environmentally friendly vessels and engines is required. One of the globally accepted means of reducing emission gases is the use of more eco-friendly fuel, LNG (Liquefied Natural Gas). LNG as a marine fuel reduces air pollutants as referred compared to traditional heavy fuel oil (HFO). Recently, large engine manufacturers are developing LNG-fuelled marine engines. In order to use this cryogenic LNG as a fuel, it is necessary to change it back to a gaseous state. A heat exchanger is used to regasify LNG. The heat exchange takes place between LNG and glycol on the primary loop, and heat exchange occurs between glycol and steam on the secondary loop. These series of processes are called LNG regasification. To control the temperature of the heat exchanger, it is necessary to model the heat exchanger. However, it is not easy to obtain an accurate mathematical model because the heat exchanger has non-linearity and time-varying characteristics. In addition, a fixed-gain controller is bound to have a limitation in its function if parameters of the heat exchanger are changed. Thus, various techniques have been studied to improve the adaptability and robustness of the controller. Recently, there has been using nonlinear PID (NPID) controller for the controlled system which have highly nonlinear and time-varying characteristics during operation. Therefore, this thesis proposes two types of the nonlinear proportional, integral, derivative (NPID) controllers to control the glycol temperature of the regasification system for LNG-fuelled marine engines. The Fully-Nonlinear PID (F-NPID) controller has a structure that the error between the set-point (or reference input) and output (or the measured output) is scaled nonlinearly, and input into the controller to derive proportional, integral, and derivative controllers. The Partial-Nonlinear PID (P-NPID) controller uses the conventional linear PD controller and only I controller uses the method of F-NPID controller. In this case, the nonlinear functions are implemented by the Fuzzy model of Takagi-Sugeno (T-S) type. In addition, the error is continuously scaled so that outstanding control performance can be maintained even when the operating environment is changed, thereby improving the swiftness and the closeness of responses. Also, the parameters of the two proposed controllers are optimally tuned in terms of minimizing the integral of the absolute error (IAE) objective function based on the genetic algorithm (GA). Meanwhile, it is necessary to examine the stability of overall feedback system that can be caused by introducing nonlinear functions during controller design. For this, the stability of the overall feedback system is analyzed by applying the circle stability theorems, which is often used for stability analysis of nonlinear problems. The proposed controllers are verified their performances which are the set-point tracking, robustness against noise and parameter changes, disturbance rejection performances by comparing with two conventional PID controllers and a conventional NPID controller.Chapter 1. Introduction 1 1.1 Research background and trends 1 1.2 Research content and composition 6 Chapter 2. LNG-fuelled Marine Engines 8 2.1 Changes of LNG-fuelled marine engines 8 2.2 Fuel injection of LNG-fuelled marine engines 10 2.3 Fuel supply system of LNG-fuelled marine engines 13 Chapter 3. Modeling of LNG Regasification System 17 3.1 Heat exchanger 17 3.2 LNG regasification system 18 3.3 Modeling of the secondary loop heat exchanger of LNG regasification system 19 3.3.1 Model of an I/P converter 19 3.3.2 Model of a pneumatic control valve 20 3.3.3 Model of a heat exchanger 23 3.3.4 Model of a disturbance 27 3.3.5 Model of a RTD sensor 28 3.3.6 Model of a time delay 29 3.3.7 Open-loop control system 30 Chapter 4. Surveys of Existing PID Controllers 32 4.1 Linear PID controller 32 4.1.1 Structure of the conventional PID controller 32 4.1.2 Characteristics of control actions 33 4.1.3 Effects of PID controller gains 36 4.2 Gain tuning of the conventional PID controller 37 4.2.1 Ziegler-Nichols tuning method 37 4.2.2 Tyreus-Luyben tuning method 40 4.3 Practical PID controller 41 4.4 Existing nonlinear PID controllers 44 4.4.1 Seraji’s NPID controller 45 4.4.2 Korkmaz’s NPID controller 48 Chapter 5. Suggestion of the Proposed Nonlinear PID Controllers 52 5.1 Fully-nonlinear PID controller 52 5.1.1 Nonlinear P block 53 5.1.2 Nonlinear D block 57 5.1.3 Nonlinear I block 57 5.1.4 Relationship between and 60 5.2 Partially-nonlinear PID controller 62 5.2.1 Linear PD block 63 5.2.2 Nonlinear I block 63 5.3 Feedback control systems 63 5.3.1 Modified F-NPID control system 63 5.3.2 P-NPID control system 66 5.4 Tuning of the controller parameters 68 5.4.1 Genetic algorithm 68 5.4.2 Optimal tuning of the controller parameters 73 Chapter 6. Stability Analysis 75 6.1 System description 75 6.2 Basic definitions and theorems 76 6.3 Stability of the NPID control systems 86 6.3.1 Sector condition of nonlinear block 86 6.3.2 Stability analysis of F-NPID control system 87 6.3.3 Stability analysis of P-NPID control system 88 Chapter 7. Simulation and Discussion of Results 90 7.1 Controller parameter tuning 90 7.2 Reponses to set-point changes 91 7.3 Reponses to noise rejection 94 7.4 Reponses to system parameter changes 95 7.5 Reponses to disturbance changes 97 Chapter 8. Conclusion 99 References 101Docto

Nonlinear and adaptive control

The primary thrust of the research was to conduct fundamental research in the theories and methodologies for designing complex high-performance multivariable feedback control systems; and to conduct feasibiltiy studies in application areas of interest to NASA sponsors that point out advantages and shortcomings of available control system design methodologies

Low-speed longitudinal controllers for mass-produced cars: A comparative study

Four longitudinal control techniques are compared: a classical Proportional-Integral (PI) control; an advanced technique-called the i-PI-that adds an intelligent component to the PI; a fuzzy controller based on human experience; and an adaptive-network-based fuzzy inference system. The controllers were designed to tackle one of the challenging topics as yet unsolved by the automotive sector: managing autonomously a gasoline-propelled vehicle at very low speeds. The dynamics involved are highly nonlinear and constitute an excellent test-bed for newly designed controllers. A Citroën C3 Pluriel car was modified to permit autonomous action on the accelerator and the brake pedals-i.e., longitudinal control. The controllers were tested in two stages. First, the vehicle was modeled to check the controllers' feasibility. Second, the controllers were then implemented in the Citroën, and their behavior under the same conditions on an identical real circuit was compared

Development of a Dynamic Performance Management Framework for Naval Ship Power System using Model-Based Predictive Control

Medium-Voltage Direct-Current (MVDC) power system has been considered as the trending technology for future All-Electric Ships (AES) to produce, convert and distribute electrical power. With the wide employment of highrequency power electronics converters and motor drives in DC system, accurate and fast assessment of system dynamic behaviors , as well as the optimization of system transient performance have become serious concerns for system-level studies, high-level control designs and power management algorithm development. The proposed technique presents a coordinated and automated approach to determine the system adjustment strategy for naval power systems to improve the transient performance and prevent potential instability following a system contingency. In contrast with the conventional design schemes that heavily rely on the human operators and pre-specified rules/set points, we focus on the development of the capability to automatically and efficiently detect and react to system state changes following disturbances and or damages by incooperating different system components to formulate an overall system-level solution. To achieve this objective, we propose a generic model-based predictive management framework that can be applied to a variety of Shipboard Power System (SPS) applications to meet the stringent performance requirements under different operating conditions. The proposed technique is proven to effectively prevent the system from instability caused by known and unknown disturbances with little or none human intervention under a variety of operation conditions. The management framework proposed in this dissertation is designed based on the concept of Model Predictive Control (MPC) techniques. A numerical approximation of the actual system is used to predict future system behaviors based on the current states and the candidate control input sequences. Based on the predictions the optimal control solution is chosen and applied as the current control input. The effectiveness and efficiency of the proposed framework can be evaluated conveniently based on a series of performance criteria such as fitness, robustness and computational overhead. An automatic system modeling, analysis and synthesis software environment is also introduced in this dissertation to facilitate the rapid implementation of the proposed performance management framework according to various testing scenarios

Using learning from demonstration to enable automated flight control comparable with experienced human pilots

Modern autopilots fall under the domain of Control Theory which utilizes Proportional Integral Derivative (PID) controllers that can provide relatively simple autonomous control of an aircraft such as maintaining a certain trajectory. However, PID controllers cannot cope with uncertainties due to their non-adaptive nature. In addition, modern autopilots of airliners contributed to several air catastrophes due to their robustness issues. Therefore, the aviation industry is seeking solutions that would enhance safety. A potential solution to achieve this is to develop intelligent autopilots that can learn how to pilot aircraft in a manner comparable with experienced human pilots. This work proposes the Intelligent Autopilot System (IAS) which provides a comprehensive level of autonomy and intelligent control to the aviation industry. The IAS learns piloting skills by observing experienced teachers while they provide demonstrations in simulation. A robust Learning from Demonstration approach is proposed which uses human pilots to demonstrate the task to be learned in a flight simulator while training datasets are captured. The datasets are then used by Artificial Neural Networks (ANNs) to generate control models automatically. The control models imitate the skills of the experienced pilots when performing the different piloting tasks while handling flight uncertainties such as severe weather conditions and emergency situations. Experiments show that the IAS performs learned skills and tasks with high accuracy even after being presented with limited examples which are suitable for the proposed approach that relies on many single-hidden-layer ANNs instead of one or few large deep ANNs which produce a black-box that cannot be explained to the aviation regulators. The results demonstrate that the IAS is capable of imitating low-level sub-cognitive skills such as rapid and continuous stabilization attempts in stormy weather conditions, and high-level strategic skills such as the sequence of sub-tasks necessary to takeoff, land, and handle emergencies

New Approaches in Automation and Robotics

The book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book