Parametric and Nonparametric PI Controller Tuning Method for Integrating Processes Based on Magnitude Optimum

This paper presents comparison studies of conventional and evolutionary tuning methods for a PI and fuzzy PI controller. Genetic algorithms (GA) are employed as the evolutionary tuning method. These PI-controller tuning methods are based on novel closed loop performance statements and the closed loop stability. The control objective is flow regulation using a laboratory flow rig, which mimics a real industrial process.

This paper discuss PI controller optimization methods procedure. In its nature PI controller is a simple yet effective method for achieving desired reference, but tuning of the same is entirely different concept. There are numerous different methods for tuning a PI controller. Some optimizing one concept of control, i.e. current overshoot, other targeting different areas, i.e. settling time. Even with most advanced methods, sometimes, response has some unfavorable features. Sometimes PI tuning can require manual inputs, to lessen those inconveniences. Other times though, some tuning methods can prove to have better responses on the actual system than during simulations. Where better performances would be expected, sometimes, some unfavorable features can surface. This paper aims to show simulation and experimental comparison between two standard PI controller tuning methods. Methods were tested in Laboratory for Power electronic and electrical drives at Faculty of Technical Sciences.

On-Line PI Controller Tuning Using Closed-Loop Setpoint Response

In this paper Fuzzy adaptive proportional-integral (PI) controller is designed to control the level of interacting tank. In the interacting process the load on the first process affects the second process and vice versa. The conventional PI controller cannot reach satisfactory result. The gain parameters of PI control are obtained by Ziegler-Nichols (ZN) tuning method and can be adjusted automatically using fuzzy logic. This paper illustrates the comparative analysis of conventional PI and fuzzy adaptive PI controller.

[Departement_IRSTEA]RE [TR1_IRSTEA]GES / TRANSCAN

This paper proposes optimal tuning of Proportional Integral (PI) controller using Grey Wolf Optimizer (GWO) to overcome the slow convergence into local optimum associated with the Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) tuning methods. The optimal gains of the PI controller obtained from tuning using GWO, PSO and GA were implemented in the pitch angle control of the fixed speed Wind Turbine integrated into the distribution system. The objectives are to enhance the efficiency and reduced the fluctuation in the Wind Turbine output. Similarly, the Ziegler Nichols (ZN) tuned PI controller, the PI-fuzzy logic and Fuzzy Logic controllers were implemented in the same distribution system. The implementation of the tuned PI controllers has enhanced the output power of the Wind Turbine compared to the value obtained using the Hybrid or Fuzzy controllers. Furthermore, the GWO tuned PI controller recorded the high average output power of the Wind Turbine with low mean power error compared to other controllers. Also, it recorded the second-position in smoothing the Wind Turbine output power. The significance of the research is it has enhanced the efficiency and smoothing the output power of the fixed speed Wind Turbine.

Real Time Control For Time Delay System

Based on desired dynamic equation (DDE), a kind of PI controller tuning method is discussed in this paper. Using Nyquist stability criterion, the stable region of controller tunable parameter is calculated and compared with traditional PI controller, and then the necessity of introducing a scaling factor L is explained for better control performance. The DDE-based PI tuning method is applied to a gasifier temperature control. The simulation resultsaditional PI controller, and then the necessity of introducing a scaling factor L is explained for better control performance. The DDE-based PI tuning method is applied to a gasifier temperature control. The simulation results show its feasibility with lower tuning efforts and better performance.

The design of decentralized proportional integral (PI) controller based on biggest log modulus tuning (BLT) tuning method is extended to design centralized PI controllers for two-input–two-output (TITO) systems. The multivariable PI control structure proposed by Tanttu and Lieslehto (1991) is considered. The single detuning parameter (F) is selected in order to get the biggest log modulus as 4 dB. Decentralized PI controllers are also designed using the IMC to get the biggest log modulus as 4 dB. The centralized PI controllers give improved main responses and decreased interactions. This improvement is shown particularly for TITO systems having the relative gain element, λij < 1. The robustness of these controllers is evaluated by the inverse maximum singular value versus frequency plot for both the input and output multiplicative uncertainties. Simulation results are given for two TITO examples. The present controllers give better performances over that of the original Tanttu and Lieslehto (TL) method. The present decentralized PI controllers give improved performances over the decentralized PI controllers proposed by Xiong and Cai (2006).

Анотація . Розроблено метод оптимального налаштування ПІ-регуляторів при врахуванні обмежень. Метод ґрунтується на зведенні задачі налаштування до задачі безумовної оптимізації. Для цього було запропоновано спеціальний комплексний критерій, який характеризується бажаними топологічними властивостями. Останні отримані за допомогою вагових коефіцієнтів, що враховують вимоги щодо стійкості системи, обмежень та комплексу критеріїв, які відображають небажані показники регулювання. Наперед задані топологічні властивості критерію дали змогу знайти його глобальний. У роботі для цієї мети була використана модифікація методу рою часточок ME - PSO , яка характеризується переініціалізацією рою при умові його стагнації. Для оцінки ефективності використання методу були виконані розрахунки коефіцієнтів ПІ-регулятора при обмеженні за перерегулюванням (прийнято, що воно повинно бути нульовим). Критерієм оптимізації була швидкодія регулювання. У результаті отримано, що для чотирьох тестових передаточних функцій (другого та третього порядків; одна передаточна функція відповідає об'єкту регулювання із часовою затримкою) швидкодія регулювання значно збільшується при забезпеченні обмеження за перерегулюванням. Отримані результати порівнювались із тими, що відповідають регуляторам, що налаштовані із використанням інших (відомих у інженерній практиці) методів налаштування ПІ-регуляторів (порівнювані методи дають змогу отримати нульове перерегулювання). Крім того, аналіз якості автоматичного регулювання був проведений із використанням показників: середньоінтегральної похибки та середньоінтегрального регулювання. Розроблений підхід доволі універсальний. Він може використовуватись для синтезу оптимальних систем регулювання у тому числі і нелінійних. Ключові слова: регулятор, оптимізація, критерій, обмеження, налаштування, метод рою часточок

The oil feeding system is an oil distribution system used in engine lubrication by flowing it directly to the engine parts to be lubricated through pipes. In addition, it is also a raw material for the production process by collecting the oil first in the storage tank, then weighing it on the oil scale before use in the production process. The current control is still using the conventional model. The operating system is still manual, and the absence of identity and damage information makes it difficult for the engineer to troubleshoot. The research method is to modify the oil feeding system control using PLC (Programmable Logic Controller) and Visual Basic to display process information. This process uses the Neural Network (NN) method. The simulation results show that the PLC program and visual basic software can be connected properly. The speed of the data transfer test connection that can be obtained is 32 ms. The prediction process of the oil feeding system using the backpropagation algorithm Neural Network and the activation function, which uses the binary sigmoid function (logsig) with the 17-10-1 architecture having very good performance getting the MSE value below the error value of 0.001 maximum epoch 961 and hidden layer 10 with an MSE value of 0.00099915.

Online Auto Selection of Tuning Methods and Auto Tuning PI Controller in FOPDT Real Time Process-pH Neutralization

The closed-loop control system plays an important role in modern process control industries. Many challenging control problems arise in the chemical process industries, due to nonlinear dynamic behavior, uncertainty time varying parameters, unmeasured frequency disturbance and dead time. In chemical process industries, the control of water level for a spherical tank system (STS) is a difficult task, due to variations present in the cross sectional area with respect to height. The research work, the entire operating region of STS is categorized into three approximate linear regions and their open-loop responses transfer function models are derived by using the process reaction curve method. The Proportional Integral (PI) controller has been designed to control the water level in the spherical tank system. The PI controller parameters are tuned by using the conventional Ziegler-Nichols Tuning (ZNT) method and proposed Root Locus Technique (RLT). The real-time experimental regulatory responses of the STS for PI controller parameter tuned by ZNT method and RLT are compared based on performance indices such as IAE and ISE.

The purpose of this research is to design PID control on BLDC motors using 2 tuning methods, namely Cohen-Coon and Trial &amp; Error. PID control of formula calculations with calculations in Simulink Matlab. From the simulation results shown in graphical form, the use of the PID control gives a better effect than the use of the P and PI controls. This can be seen in the comparison curve which shows the speed of the initial start process when using the PID control. In the Trial &amp; Error method, the response value of the system to controller P is obtained, namely, rise time = 0.0151 s, settling time = 0.6 s, overshoot = 75.9%, peak time = 1.74 s, and time delay = 0.424 s. on the PI controller namely, rise time = 0.0148 s, settling time = 0.591 s, overshoot = 76.3%, peak time = 1.74 s, and time delay = 0.0416 s. on the PID controller namely, rise time = 0.0496 s, settling time = 0.55 s, overshoot = 44 %, peak time = 1.31 s, and time delay = 0.128 s. In the Cohen-Coon method, the response value of the system to controller P is obtained, namely, rise time = 0.0168 s, settling time = 0.575 s, overshoot = 73.3%, peak time = 1.71 s, and time delay = 0.0469 s. on the PI controller namely, rise time = 0.0573 s, settling time = 0.603 s, overshoot = 39.3%, peak time = 1.23 s, and time delay = 0.142 s. on the PID controller namely, rise time = 0.276 s, settling time = 0.658 s, overshoot = 2.42 %, peak time = 0.159 s, and time delay = 0.576 s. From the simulation results it is shown that the value for the Cohen-Coon tuning method is better than the Trial &amp; Error method, perhaps because the input value for the Trial &amp; Error method is larger.

Modern control techniques have been applied to a distillation column. Three control techniques were selected for evaluation. These are; a decoupling and disturbance rejection control scheme; an estimator aided control techniques using a Kalman filter; and an implicit generalised minimum variance self tuning control. A 10 tray pilot scale binary distillation column, interfaced with a microcomputer, was used for investigation of the process control techniques. A non-linear model of the column was developed. The reliability of this model was demonstrated. The model was therefore used for the design, analysis and screening of control systems for the pilot plant distillation column. The results of extensive simulations on linearised state variable models of the column simulator demonstrate that the decoupling and disturbance rejection controller works in the presence of load disturbances and setpoint changes. The proper choice of the values of a diagonal matrix in the precompensator of the controller required for accurate setpoint tracking has also been shown. By analogy with PI control, integral and derivative modes have been introduced into the controller to equip it with the ability to remove offsets. Simulation results demonstrate that the sensitivity of the controller to non-linear effects makes the controller inoperable on the column simulator, as well as on the pilot plant. Therefore, the use of an adaptive form of the controller is necessary to compensate for the non-linear effects and other model errors for on-line application to be practical on the pilot plant. On-line implementation of the Kalman filter algorithm using a linear state variable model of the column simulator as the filter model, was not possible because of the large memory requirement of the software, long execution time and the inability to produce satisfactory estimates of all the tray compositions. Simulated and experimental studies for both single temperature control and dual temperature control of the distillation column, demonstrated that self tuning control can provide tighter control of the products of distillation columns than PI control. An algorithm, called the Simplified Correction (SPC) method, has been implemented to prevent the parameters of a self tuning controller from reaching unsatisfactory values when the closed loop system is not sufficiently excited. Simulations show that the SPC can provide significant improvements even when only a subset of the controller parameters are prevented from attaining bad values. The findings in this work verify the degrading effects that model errors have on controller performance. Areas for future work have been suggested in the case of the on-line implementation of the control schemes selected m this work.