TRIM, LINEARIZATION AND PID CONTROLLER DESIGN FOR A MULTI-PURPOSE FIXED-WING AIRCRAFT

Desalination plants have an important concern regarding controlling the permeate flow rate and pH during operability. This paper proposes the proportional integral derivative (PID) control design using modified Particle Swarm Optimization (PSO) techniques called autonomous groups PSO (AGPSO) in the two-input two-output (TITO) RO desalination plant to control the permeate flow rate and pH. Here, three different versions (AGPSO1, AGPSO2, and AGPSO3) of the AGPSO algorithm are utilized to design PID control for the same TITO plant. In addition, an integral time absolute error (<inline-formula> <tex-math notation="LaTeX">$J_{\mathrm {ITAE}}$ </tex-math></inline-formula>) based objective function is utilized to design a PID controller. The simulation results suggest that the proposed controller designs are flexible, self-tuning, and have stable characteristics, while the AGPSO3-PID control design attained a robust design for optimum tuning compared to existing improved grey wolf optimization PID (IGWO-PID) and other versions of AGPSO based PIDs (AGPSO1-PID, AGPSO2-PID). The design of AGPSO-PID achieved a minimum objective function than existing IGWO-PID and other versions of AGPSO based PIDs &#x007B;AGPSO1-PID, AGPSO2-PID&#x007D;. Finally, the proposed controller designs outperform the existing IGWO-PID design in literature in terms of control performance, demonstrating a precise control for and the improvement of plant performance.

This chapter examines the proportional integral derivative (PID) controller design. It introduces the idea of disturbance estimation that leads to an equivalent PI control system. The mathematical model used in the chapter is a first order model. For systems with higher order transfer functions, approximation is required. To design a PID controller using the estimation based approach, a second order transfer function is considered. The chapter presents the MATLAB tutorial for the implementation of disturbance observer‐based PID controller. This implementation contains the anti‐windup mechanism when the control signal reaches its maximum or minimum values. The embedded PID controller via estimation is used in the simulation studies within the Simulink environment. The chapter also explains resonant controller design and implementation with anti‐windup mechanism using the disturbance estimation approach. It presents an example to illustrate the disturbance observer‐based resonant controller design.

Flight controllers for micro-air UAVs are generally designed using proportional-integral-derivative (PID) methods, where the tuning of gains is difficult and time-consuming, and performance is not guaranteed. In this paper, we develop a rigorous method based on the sliding mode analysis and nonlinear backstepping to design a PID controller with guaranteed performance. This technique provides the structure and gains for the PID controller, such that a robust and fast response of the UAV (unmanned aerial vehicle) for trajectory tracking is achieved. First, the second-order sliding variable errors are used in a rigorous nonlinear backstepping design to obtain guaranteed performance for the nonlinear UAV dynamics. Then, using a small angle approximation and rigorous geometric manipulations, this nonlinear design is converted into a PID controller whose structure is naturally determined through the backstepping procedure. PID gains that guarantee robust UAV performance are finally computed from the sliding mode gains and from stabilizing gains for tracking error dynamics. We prove that the desired Euler angles of the inner attitude controller loop are related to the dynamics of the outer backstepping tracker loop by inverse kinematics, which provides a seamless connection with existing built-in UAV attitude controllers. We implement the proposed method on actual UAV, and experimental flight tests prove the validity of these algorithms. It is seen that our PID design procedure yields tighter UAV performance than an existing popular PID control technique.

HVDC power transmission systems can overcome some limitations inherent with AC power transmission systems such as transmission over long distance and transmission with cable. One aspect of importance is the improvement in stability achieved with AC-DC power transmission. In this paper a methodology for the optimal proportional integral derivative (PID) controller design using the modified genetic algorithm (MGA) is proposed to improve the transient stability of AC-DC power systems after faults. This study consists of the formulation of load-flow calculation, basic controls on HVDC transmission system, mathematical model selection for stability analysis, and supplementary signal control by an optimal PID controller using the MGA. The proposed method is verified using computer simulation. The results show the application of the MGA-PID controller in AC-DC power systems has improved the transient stability. The PID controller design using MGA method has been shown to be advantageous when applied for the AC-DC power systems.

This paper presents a Proportional Integral Derivative (PID) controller design with the presence of an uncertain internal gain and additional time delay in the forward path of a 300 kW Solid Oxide Fuel Cell-Gas Turbine (SOFC-GT). The outputs of the system are turbine speed and the fuel cell mass flow rate. A fixed set of proportional controller coefficients are determined to graphically develop an area of selection for the integral and derivative coefficients of the PID controller. The inputs to the power plant are the electric load and cold air valve. The decentralized controllers are applied to four sub-systems as a Single Input Single Output (SISO). The PID controller coefficients are selected from a singular matrix solution that stabilizes the system and satisfies the internal gain and time delay uncertainties. Two sub-systems are the transfer functions of the turbine speed over the electric load and the cold air valve. The other two sub-systems are the transfer functions of the fuel cell mass flow rate over the electric load and the cold air bypass valve. Multiple options for selecting PID controller coefficients are beneficial to the SOFC-GT plant due to the wide range of operations and internal uncertainty interactions. The specific internal time delay and gain margins increase the reliability and robustness of the SOFC-GT with multiple uncertain parameters.

Now a day's Unmanned Aerial Vehicle (UAV) systems are frequently used in many commercial applications. This makes UAV application as one of the hottest topic among the researchers. The most critical task for the researchers working on UAV is its proper and stable flight controlling under uncertainty and perturbed condition. In fixed wing UAV system altitude and attitude angles (Yaw, Pitch, Roll) play a significant role for flight stabilization. This paper presents a control algorithm for fixed-wing UAV longitude and latitude angles (Yaw, Pitch and Roll) controlling. This control algorithm is based on Proportional Integral Derivate (PID) conjunction with Extended Kalman Filter (EKF). It is also describes that how an unstable system become stable after applying proposed control technique. The proposed control method is simulated on fixed-wing UAV mathematical model under perturbed and unperturbed conditions using MATLAB platform. The Simulation results shows that the proposed controller performed well and maintains the stability of an airplane and holds desired angle values under uncertainties and perturbed conditions.

&lt;div&gt;In this dissertation, methods for real-time trajectory generation and autonomous obstacle avoidance for fixed-wing and quad-rotor unmanned aerial vehicles (UAV) are studied. A key challenge for such trajectory generation is the high computation time required to plan a new path to safely maneuver around obstacles instantaneously. Therefore, methods for rapid generation of obstacle avoidance trajectory are explored. The high computation time is a result of the computationally intensive algorithms used to generate trajectories for real-time object avoidance. Recent studies have shown that custom solvers have been developed that are able to solve the problem with a lower computation time however these designs are limited to small sized problems or are proprietary. Additionally, for a swarm problem, which is an area of high interest, as the number of agents increases the problem size increases and in turn creates further computational challenges. A solution to these challenges will allow for UAVs to be used in autonomous missions robust to environmental uncertainties.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;In this study, a trajectory generation problem posed as an optimal control problem is solved using a sequential convex programming approach; a nonlinear programming algorithm, for which custom solver is used. First, a method for feasible trajectory generation for fast-paced obstacle-rich environments is presented for the case of fixed-wing UAVs. Next, a problem of trajectory generation for fixed-wing and quad-rotor UAVs is defined such that starting from an initial state a UAV moves forward along the direction of flight while avoiding obstacles and remaining close to a reference path. The problem is solved within the framework of finite-horizon model predictive control. Finally, the problem of trajectory generation is extended to a swarm of quad-rotors where each UAV in a swarm has a reference path to fly along. Utilizing a centralized approach, a swarm scenario with moving targets is studied in two different cases in an attempt to lower the solution time; the first, solve the entire swarm problem at once, and the second, solve iteratively for a UAV in the swarm while considering trajectories of other UAVs as fixed.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;Results show that a feasible trajectory for a fixed-wing UAV can be obtained within tens of milliseconds. Moreover, the obtained feasible trajectories can be used as initial guesses to the optimal solvers to speed up the solution of optimal trajectories. The methods explored demonstrated the ability for rapid feasible trajectory generation allowing for safe obstacle avoidance, which may be used in the case an optimal trajectory solution is not available. A comparative study between a dynamic and a kinematic model shows that the dynamic model provides better trajectories including aggressive trajectories around obstacles compared to the kinematic counterpart for fixed-wing UAVs, despite having approximately the same computational demands. Whereas, for the case of quad-rotor UAVs, the kinematic model takes almost half the solution time than with a reduced dynamic model, despite having approximately the similar range of values for the cost function. When extended to a swarm, solving the problem for each UAV is four to seven times computationally cheaper than solving the swarm as a whole. With the improved computation time for trajectory generation for a swarm of quad-rotors using centralized approach, the problem is now reasonably scalable, which opens up the possibility to increase the number of agents in a swarm using high-end computing machines for real-time applications. Overall, a custom solver jointly with a sequential convex programming approach solves an optimization problem in a low computation time.&lt;/div&gt;

One of the key drivers in gaining the cost-effective, reliable and low-downtime Operation system for major Oil Fields is implementation of Water Injection System (WIS) with capability of online-automated regulation and control. This Paper outlines an application of Proportional, Integral, Derivative (PID) control loop algorithm for Decentralized WIS (with direct injection from Water Source wells to Injection wells by means of high capacity ESP's) used in West Qurna-2 oilfield located in Iraq, operated by LUKOIL. PID control loop algorithm has being used in the industry for the years, but not commonly applied as integral component of Water Injection System allowing to maintain optimal equipment modes. Such limited PID deployment is explained by complexity of operational parameters synchronizing for every Water Injection System component. The decentralized Water Injection System at the West Qurna 2 project is common and united pipeline system for every WellPad that provides water transfer from Water source wells (3-5 wells per WellPad) to injection wells (3-6 wells per WellPad) with line pressure of 70-130 Barg by means of high-capacity ESP equipped with VSD. Water injection process is regulated by controlling both the conventional ESP parameters (frequency, flow rate, motor current and load, Intake and discharge pressures), line pressure, required injection rate by means of flow control valves, and implemented PID control loop algorithm. This paper discusses the techniques of PID control loop algorithm application as the tool for: –Pipeline protection against high pressure without full shutdown of water injection process and–ESP automated regulation to ensure operation inside Recommended Operated Range (ROR) and smoothening of ESP mode when any changes in the system happen (i.e. shutdown of other water source well ESP's at the same WellPad or shutdown of injection well) with immediate feedback to Operator Control Room. Implemented approach allows to decrease number of ESP shutdowns and avoid high jumping of ESP loads that consequently leads to maximizing of ESP run life. Comparative examples and case studies of submersible equipment operation results before and after the application of PID regulation are specified. Mainly by implementing PID regulation and control stable and smooth operation of equipment and ESP's used for WIS (direct injection from Water Source Well to Injection Well) has been achieved. New approach for PID application as a tool for pipeline overpressure protection has been formed, which allowed to reduce Capital Expenses for the WIS construction works. Specified case can be used as example for further development and projects in the oil and gas industry.

Electric load simulator (ELS) plays a vital role in the ground test and simulation of the electromechanical actuator (EMA) of an unmanned aerial vehicle (UAV), which is preferred by researchers for its dynamic response and high precision. However, clearance is an inevitable problem of the ELS system which can introduce backlash influencing the stability of the system. The backlash nonlinear can induce mechanical impact and impose limitations on the quality of loading torque control. In order to increase the accuracy of loading torque, a switching control method is presented to compensate for the backlash nonlinear. A time-optimal sliding mode control (TOSMC) method is used when the ELS system entering backlash, and a nonlinear proportional integral derivative (PID) controller is presented for the different system dynamic. A switching controller is also introduced to acquire suitable moment switching from TOSMC to nonlinear PID controller, ensuring the stability of the system. Experimental tests show that compared with the traditional PID control, this method can effectively reduce the torque overshoot when ELS crossing backlash and guarantee torque tracking speed and precision of ELS.

The most widely used control strategy in industry is proportional integral derivative (PID) controller. The popularity of PID controllers can be attributed partly to their robust performance in a wide range of operating conditions and partly to their functional simplicity. One of the application is to control arm robot manipulator model by using DC servo motor as actuator. This paper presents design of PID controller of DC servo motor using automated PID tuning by sisotool for higher order system and implement to the Arduino Mega 2560 via potentiometer by using Simulink Support Package for Arduino Hardware in MATLAB/Simulink. A better design of controller using fuzzy logic controller (FLC) is proposed. Simulation results are demonstrated. Performance analysis shows the effectiveness of the proposed Fuzzy logic controller as compared to the PID controller.

This paper presents the effect of Variable Damping Ratio on design of Proportional Integral Derivative (PID) Controller. This novel method is very useful because of their successful practical application, robustness and simplicity. The most popular and widely used controllers in industries are PID controllers. To find appropriate parameters, many design methods have been proposed for PID controller with having its own merits and demerits. However, it is still not easy to find appropriate parameters for the PID controllers. In this paper, PID having variable Damping ratio is developed to improve the performance of the controller. By using this method there is a 60% saving in settling time as compared to the method proposed by Ziegler Nicholas and 84.6% saving settling time as compared to technique proposed by Salem. Our method is independent of maximum overshoot which is distinguishable advantage which increases the performance of system. Based on the mathematical model of the system, controller is designed and is estimated by the MATLAB version 2013. Data for model estimation is taken from an experimental works, the performance has improved significantly of the PID controller having variable damping ratio compare to conventional PID controller.

The increasing use of battery-powered electronic devices necessitates efficient and fast charging solutions. This research aims to optimize battery charging at a charging station using the Maximum Power Point Tracking (MPPT) method integrated with Proportional Integral Derivative (PID) control. This method is chosen to address overshoot issues and speed up the stabilization process. The study begins with a literature review to understand the basic theory of MPPT optimization and PID control. Subsequently, hardware and software for the battery charging station were designed and developed. An Arduino ATMega328 microcontroller was used as the main controller, with an LCD to display charging information. Tests were conducted on 12V batteries with capacities of 5Ah and 9Ah. The results showed that using PID control in MPPT optimization successfully increased battery charging efficiency. The fastest charging duration was achieved for a 12V 5Ah battery with parameters Kp=6, Ki=9, and Kd=4, where the battery was fully charged in 19 minutes, and for a 12V 9Ah battery with parameters Kp=5, Ki=3, Kd=1, where the battery was fully charged in 27 minutes. Conversely, the longest charging duration occurred for a 12V 5Ah battery with parameters Kp=5, Ki=3, Kd=1, where the battery was fully charged in 34 minutes, and for a 12V 9Ah battery with parameters Kp=2, Ki=5, Kd=1, with a charging time of 38 minutes. The designed system was able to reduce input current and increase input voltage to reach the 14.5V set point, ensuring that there was no excessive voltage and current that could prolong battery life. This research demonstrates that the PID method in MPPT optimization is effective in accelerating and optimizing battery charging. Future research is expected to further develop this method to improve battery charging efficiency and duration. Keywords: MPPT, PID, Optimization, Battery Charger, Optimal, PWM ABSTRAK Penggunaan perangkat elektronik yang bertenaga baterai semakin meningkat memerlukan solusi pengisian yang efisien dan cepat. Penelitian ini bertujuan untuk mengoptimalkan pengisian baterai pada stasiun pengisian dengan menggunakan metode Maximum Power Point Tracking (MPPT) yang diintegrasikan dengan kontrol Proportional Integral Derivative (PID). Metode ini dipilih untuk mengatasi masalah overshoot dan mempercepat waktu menuju keadaan stabil. Penelitian dimulai dengan studi literatur untuk memahami teori dasar optimasi MPPT dan kontrol PID. Selanjutnya dirancang dan dibuat perangkat keras serta perangkat lunak untuk stasiun pengisian baterai. Mikrokontroler Arduino ATMega328 digunakan sebagai pengendali utama dengan LCD untuk menampilkan informasi pengisian. Pengujian dilakukan pada baterai aki 12V dengan kapasitas 5Ah dan 9Ah. Hasil pengujian menunjukkan bahwa penggunaan kontrol PID pada optimasi MPPT berhasil meningkatkan efisiensi pengisian baterai. Durasi pengisian yang paling cepat dicapai baterai 12V 5ah dengan parameter Kp=6 Ki=9 dan Kd=4 dimana baterai terisi penuh dalam waktu 19 menit dan baterai 12V 9ah dengan parameter Kp=5, Ki=3, Kd=1 dimana baterai terisi penuh dalam waktu 27 menit. Sebaliknya durasi pengisian terlama terjadi pada baterai 12V 5ah dengan parameter Kp=5, Ki=3, Kd=1 baterai terisi penuh dalam waktu 34 menit dan baterai 12V 9ah dengan parameter Kp=2 Ki=5, Kd=1 dengan waktu pengisian 38 menit. Sistem yang dirancang mampu mengurangi arus input dan meningkatkan tegangan input untuk mencapai set point 14.5V menjaga agar tidak terjadi tegangan dan arus berlebih yang dapat memperpanjang umur baterai. Penelitian ini menunjukkan bahwa metode PID pada optimasi MPPT efektif dalam mempercepat dan mengoptimalkan pengisian baterai. Penelitian selanjutnya diharapkan dapat mengembangkan lebih lanjut metode ini untuk meningkatkan efisiensi dan durasi pengisian baterai. Kata kunci: MPPT, PID, Optimasi, Charger Baterai, Optimal, PWM

The propulsion subsystem of multi-rotor Unmanned Aerial Vehicles (UAV) is one of the most complex due to the aerodynamic, aero-elastic and electromechanical elements it comprises. Therefore, accurate models of this subsystem can be difficult to work with. Therefore, simplified models are normally used for the design of control and navigation algorithms. Considering this, the effectiveness of these algorithms is heavily dependent on the identification process used for the estimation of the parameters of the simplified propulsion model. On the other hand, the novel method of fuzzy adaptive neurons (FANs) have interesting characteristics that make them attractive for applications in which a fast response and good precision are required. In this article, the identification of the parameters of the propulsion system and the trajectory tracking of a multi-rotor UAV using FANs is explored. The efficient learning algorithm of the FANs is applied to identify the parameters of a simplified model of the propulsion system and to the self-tuning proportional integral derivative (PID) controllers of the trajectory tracking system. The proposed simplified model with the identified parameters is tested with experimental data obtained with low speed wind tunnels. The proposed PID controllers with self-tuning gains defined by the algorithm of the FANs for trajectory tracking system, are verified with simulations in MATLAB/Simulink® environment. The results showed that the parameter identification and trajectory tracking with PID controllers with self-tuning gains defined by the algorithm of the FANs, are suitable for estimating the parameters of the simplified model and track the trajectory with better error reduction than a classical PID controller.

Proportional-Integral-Derivative (PID) scheme is the most commonly used algorithm for designing the controllers for unmanned aerial vehicles (UAVs). However, tuning PID gains is a non trivial task. A number of methods have been developed for tuning the PID gains for UAV systems. However, these methods do not handle wind disturbances, which is a major concern for small UAVs. In this paper, we propose a new method for determining optimized PID gains in the ℋ 2 optimal control framework, which achieves improved wind disturbance rejection. The proposed method compares the classical PID control law with the ℋ 2 optimal controller to determine the ℋ 2 optimal PID gains, and involves solving a convex optimization problem. The proposed controller is tested in two scenarios, namely, vertical velocity control, and vertical position control. The results are compared with the existing LQR based PID tuning method.

Unmanned aerial vehicles (UAVs) have gained very much importance in both civilian and industrial areas. Especially, in military applications, there are growing demands on the UAVs’ usage for reconnaissance, surveillance, defence, and attack purposes. Therefore, autonomous control of the UAVs has great importance to achieve stable and successful missions. Most of the control techniques in the aircraft control systems use the classical PID (proportional + integral + derivative) control method but this method may have some insufficient response characteristics in terms of changing weather conditions and disturbances. At this point, because of the PID usage in most UAV systems, PID controller parameters must have been obtained for optimum control performance of any state dynamics of the UAV. In this study, longitudinal dynamics are obtained for pitch angle and altitude dynamics of a mini fixed-wing Cessna-182 UAV at 60 km/h flight speed. PID parameters are investigated for the best performance of evolutional genetic algorithms (GA) in terms of different performance indexes such as integrated square error (ISE), integrated absolute error (IAE), time multiply of square error and absolute errors (ITSE, ITAE) and linear quadratic regulator (LQR) cost functions. The simulation results indicate the best performance obtained for the LQR index in the point of overshoot and settling time. In addition, proportioned hybrid ITSE and ITAE cost also developed their performance either for overshoot or settling time. This paper contributes to sustainable development goal (SDG9) by investigating aerial vehicle innovation for a stable and reliable flight purpose in the industrial and civilian applications.KeywordsSDG9UAVGenetic algorithmPID controlLongitudinal dynamic