Advanced Control for a Fire-Tube Shell Boiler System

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Tarmidi Abu Bakar ◽  
Mohamed Azlan Hussain
Keyword(s):  
Fuel Consumption ◽  
Pid Controller ◽  
Good Control ◽  
Steam Pressure ◽  
Heat Transfer Process ◽  
Co2 Removal ◽  
Combustion Gas ◽  
Point Tracking ◽  
The Neural Network ◽  
Tube Shell

The design of a fire-tube shell boiler consists of a bundle of tubes contained inside a shell. The heat transfer process from the combustion gas to the boiling water via the tube surface is extremely complicated as it involves combustion, convection, conduction, and boiling process. Severe boiling and evaporating processes take place outside the fire-tube shell boiler where the steam is generated. However, many processes such as the regeneration of absorbent for CO2 removal needs optimum steam consumption, which reflects its high costs for operation. Therefore, good control performance of steam pressure becomes important. This study is aimed at developing a control scheme to minimize the effect of over-firing on the fire-tube shell boiler which impacts on excessive fuel consumption. The Neural Network Predictive Controller (NNPC) is used in this work, using the optimization and neural toolboxes which are written in the MATLAB code and were compared with the PID controller for set-point tracking and disturbance rejection ability. The comparison reveals that NNPC gives an excellent alternative to PID controller due to the non-linearity of the fire-tube shell boiler system. Since NNPC can reduce the fuel consumption by minimizing actuator moves, the control of boiler and burners in this plant in Brunei is recommended to be upgraded to replace the existing PID controller.

scholarly journals A Modified 2-DOF Control Framework and GA Based Intelligent Tuning of PID Controllers

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 423
Author(s):  
Gun-Baek So
Keyword(s):  
Disturbance Rejection ◽  
Pid Controller ◽  
Weighting Function ◽  
Pid Controllers ◽  
Set Point ◽  
Tuning Method ◽  
Point Tracking ◽  
Load Disturbance ◽  
Control Framework ◽  
Feedforward Controller

Although a controller is well-tuned for set-point tracking, it shows poor control results for load disturbance rejection and vice versa. In this paper, a modified two-degree-of-freedom (2-DOF) control framework to solve this problem is proposed, and an optimal tuning method for the pa-rameters of each proportional integral derivative (PID) controller is discussed. The unique feature of the proposed scheme is that a feedforward controller is embedded in the parallel control structure to improve set-point tracking performance. This feedforward controller and the standard PID con-troller are combined to create a new set-point weighted PID controller with a set-point weighting function. Therefore, in this study, two controllers are used: a set-point weighted PID controller for set-point tracking and a conventional PID controller for load disturbance rejection. The parameters included in the two controllers are tuned separately to improve set-point tracking and load dis-turbance rejection performances, respectively. Each controller is optimally tuned by genetic algo-rithm (GA) in terms of minimizing the IAE performance index, and what is special at this time is that it also tunes the set-point weighting parameter simultaneously. The simulation results performed on four virtual processes verify that the proposed method shows better performance in set-point tracking and load disturbance rejection than those of the other methods.

The Optimization of Adaptive PID Control Algorithm Based on RBF Neural Network

2014 ◽  
Vol 998-999 ◽  
pp. 943-946
Author(s):  
Jing Liu ◽  
Guo Xin Wang
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Rbf Neural Network ◽  
High Reliability ◽  
Simple Algorithm ◽  
Poor Performance ◽  
Learning Ability ◽  
The Neural Network ◽  
Pid Controller Tuning ◽  
Deterministic Control

As the earliest practical controller, PID controller has more than 50 years of history, and it is still the most widely used and most common industrial controllers. PID controller is simple to understand and use, without a prerequisite for an accurate model of the physical system, thus become the most popular, the most common controller. The reason why PID controller is the first developed one is that its simple algorithm, robustness and high reliability. It is widely used in process control and motion control, especially for accurate mathematical model that can be established deterministic control system. But the conventional PID controller tuning parameters are often poor performance, poor adaptability to the operating environment. The neural network has a strong nonlinear mapping ability, competence, self-learning ability of associative memory, and has a viable quantities of information processing methods and good fault tolerance.

scholarly journals Tracking control and robustness study of shifting process

2018 ◽  
Vol 46 (2) ◽  
pp. 99-106
Author(s):  
Xin-xin Zhao ◽  
Chao Guan
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Tracking Control ◽  
Pid Controller ◽  
Automatic Transmission ◽  
Good Control ◽  
Simulation Software ◽  
Control Effect ◽  
Engine Torque ◽  
Shift Quality

Heavy dump vehicles are usually working with big load changes and bad work environment, thus change the friction performance of transmission clutches, as well as great affect the shift quality seriously, which influence the vehicle performance. Many researchers developed a lot to design a useful automatic transmission control system. Using PID tracking control and Monte Carlo method, the controller based on an dynamic model was set up to analyze the shifting process of automatic transmission and its robustness in this paper．The shift process was divided into four stages, low-gear phase, torque phase, inertia phase and high-gear phase. The model presents the process from the first gear to the second gear when the torque has big change．Since the jerk and the friction work of clutch are both related to the speed of clutch which was easier to control, it was chose as the target to control the oil pressure for satisfying the requirement of shift quality．The simulation software, Maplesim and Simulink, were used to build the vehicle model and shifting controller for simulation under different working conditions, and the maximum jerk was changed from 34 m/s3 to 12 m/s3 after the optimization. In this paper the Monte Carlo has been used to quantize and evaluate the robustness of the closed-loop system for the friction coefficients and output torque of turbine variation leading by the friction feature parameters and throttle angle changed. Monte Carlo method was used to analyze the effectiveness and robustness of PID controller, which proves that it has good control effect when the throttle is ongoing minor fluctuations. When the throttle is full opening, a quadratic optimal controller based on disturbance is designed by the method of multi-objective optimization. When it changes within 20 percent, PID controller was designed under the guidance of tracking thoughts. The results also show that the controller could still obtain better effect when the friction coefficient ranged from -40 % to 40 % as well as engine torque changed from -20 % to 20 %, which indicates the robustness of controller.

scholarly journals Neuro-Fuzzy System for Energy Management of Conventional Autonomous Vehicles

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1745 ◽  
Author(s):  
Duong Phan ◽  
Alireza Bab-Hadiashar ◽  
Reza Hoseinnezhad ◽  
Reza N. Jazar ◽  
Abhijit Date ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Autonomous Vehicles ◽  
Pid Controller ◽  
Autonomous Vehicle ◽  
Subtractive Clustering ◽  
Energy Management System ◽  
Power Demand ◽  
Neuro Fuzzy ◽  
Simulation Results

This paper investigates the energy management system (EMS) of a conventional autonomous vehicle, with a view to enhance its powertrain efficiency. The designed EMS includes two neuro-fuzzy (NF) systems to produce the optimal torque of the engine. This control system uses the dynamic road power demand of the autonomous vehicle as an input, and a PID controller to regulate the air mass flow rate into the cylinder by changing the throttle angle. Two NF systems were trained by the Grid Partition (GP) and the Subtractive Clustering (SC) methods. The simulation results show that the proposed EMS can reduce the fuel consumption of the vehicle by 6.69 and 6.35 l/100 km using the SC and the GP, respectively. In addition, the EMS based on NF trained by GP and NF trained by SC can reduce the fuel consumption of the vehicle by 11.8% and 7.08% compared with the case without the controller, respectively.

scholarly journals Advanced Variable Step Size Incremental Conductance MPPT for a Standalone PV System Utilizing a GA-Tuned PID Controller

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4153 ◽  
Author(s):  
Adeel Feroz Mirza ◽  
Majad Mansoor ◽  
Qiang Ling ◽  
Muhammad Imran Khan ◽  
Omar M. Aldossary
Keyword(s):  
Steady State ◽  
Pid Controller ◽  
Superior Performance ◽  
Variable Step Size ◽  
Step Size ◽  
Pv System ◽  
Fast Tracking ◽  
Point Tracking ◽  
Incremental Conductance ◽  
Variable Step

In this article, a novel maximum power point tracking (MPPT) controller for the fast-changing irradiance of photovoltaic (PV) systems is introduced. Our technique utilizes a modified incremental conductance (IC) algorithm for the efficient and fast tracking of MPP. The proposed system has a simple implementation, fast tracking, and achieved steady-state oscillation. Traditional MPPT techniques use a tradeoff between steady-state and transition-state parameters. The shortfalls of various techniques are studied. A comprehensive comparative study is done to test various existing techniques against the proposed technique. The common parameters discussed in this study are fast convergence, efficiency, and reduced oscillations. The proposed method successfully addresses these issues and improves the results significantly by using a proportional integral deferential (PID) controller with a genetic algorithm (GA) to predict the variable step size of the IC-based MPPT technique. The system is designed and tested against the perturbation and observation (P&O)-based MPPT technique. Our technique effectively detects global maxima (GM) for fast-changing irradiance due to the adopted GA-based tuning of the controller. A comparative analysis of the results proves the superior performance and capabilities to track GM in fewer iterations.

scholarly journals Neural Network Approach to MPPT Control and Irradiance Estimation

2020 ◽  
Vol 10 (15) ◽  
pp. 5051
Author(s):  
Žarko Zečević ◽  
Maja Rolevski
Keyword(s):  
Neural Network ◽  
Computational Complexity ◽  
Low Complexity ◽  
Maximum Power ◽  
Photovoltaic Module ◽  
Neural Network Approach ◽  
Point Tracking ◽  
The Neural Network ◽  
Power Point ◽  
The One

Photovoltaic (PV) modules require maximum power point tracking (MPPT) algorithms to ensure that the amount of power extracted is maximized. In this paper, we propose a low-complexity MPPT algorithm that is based on the neural network (NN) model of the photovoltaic module. Namely, the expression for the output current of the NN model is used to derive the analytical, iterative rules for determining the maximal power point (MPP) voltage and irradiance estimation. In this way, the computational complexity is reduced compared to the other NN-based MPPT methods, in which the optimal voltage is predicted directly from the measurements. The proposed algorithm cannot instantaneously determine the optimal voltage, but it contains a tunable parameter for controlling the trade-off between the tracking speed and computational complexity. Numerical results indicate that the relative error between the actual maximum power and the one obtained by the proposed algorithm is less than 0.1%, which is up to ten times smaller than in the available algorithms.

Boiler Steam Pressure Control System Based on Fuzzy Control

2012 ◽  
Vol 462 ◽  
pp. 732-737
Author(s):  
Yi Heng Zhou ◽  
Long Yue Yang ◽  
Hai Lin Pu ◽  
Zi Yu Zhao ◽  
Fei Liu ◽  
...  
Keyword(s):  
Control System ◽  
Pid Control ◽  
Good Control ◽  
Pressure Control ◽  
Steam Pressure ◽  
Cascade Control ◽  
Normal Operation ◽  
Control Effect ◽  
Pressure Control System ◽  
Fuzzy Adaptive

Industrial boiler steam pressure is an important measure of boiler normal operation. Since there is delay and inertial part, single-loop PID control is difficult to achieve good dynamic characteristics. By analyzing the characteristics of the steam pressure controlled object, this paper presents a fuzzy adaptive PID control based on the cascade control system. Finally, in order to analyze the effect of the control system, mathematical model was constructed by MATLAB simulation to compare fuzzy adaptive PID cascade control with conventional PID control. The results prove that the former has a good control effect.

A Study of PID Control System Based on BP Neural Network

2011 ◽  
Vol 328-330 ◽  
pp. 1908-1911
Author(s):  
Wei Liu ◽  
Jian Jun Cai ◽  
Xi Pin Fan
Keyword(s):  
Neural Network ◽  
Control System ◽  
Conjugate Gradient ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Steepest Descent ◽  
The Neural Network ◽  
On Line ◽  
New Type

To deal with the defects of the steepest descent in slowly converging and easily immerging in partialm in imum,this paper proposes a new type of PID control system based on the BP neural network, which is a combination of the neural network and the PID strategy. It has the merits of both neural network and PID controller. Moreover, Fletcher-Reeves conjugate gradient in controller can make the training of network faster and can eliminate the disadvantages of steepest descent in BP algorithm. The parameters of the neural network PID controller are modified on line by the improved conjugate gradient. The programming steps under MATLAB are finally described. Simulation result shows that the controller is effective.

Force Control of Robot Manipulator by Neural Network Model - Experiment and Evaluation of One-Degree-of-Freedom Manipulator -

1990 ◽  
Vol 2 (4) ◽  
pp. 273-281 ◽  
Author(s):  
Masatoshi Tokita ◽  
◽  
Toyokazu Mitsuoka ◽  
Toshio Fukuda ◽  
Takashi Kurihara ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Force Control ◽  
Pid Controller ◽  
Robot Manipulator ◽  
Force Sensor ◽  
Robotic Manipulator ◽  
The Neural Network ◽  
The Stability

In this paper, a force control of a robotic manipulator based on a neural network model is proposed with consideration of the dynamics of both the force sensor and objects. This proposed system consists of the standard PID controller, the gains of which are augmented and adjusted depending on objects through a process of learning. The authors proposed a similar method previously for the force control of the robotic manipulator with consideration of dynamics of objects, but without consideration of dynamics of the force sensor, showing only simulation results. This paper shows the similar structure of the controller via the neural network model applicable to the cases with consideration of both effects and demonstrates that the proposed method shows the better performance than the conventional PID type of controller, yielding to the wider range of applications, consequently. Therefore, this method can be applied to the force/compliance control problems. The effects of the number of neurons and hidden layers of the neural network model are also discussed through the simulation and experimental results as well as the stability of the control system.

Steam Pressure of Foaming Machine of Fuzzy PID Control and Simulation

2012 ◽  
Vol 531-532 ◽  
pp. 726-731
Author(s):  
Yue Hua Xiong ◽  
Chun Liang Zhang ◽  
Bai Xiang Fu
Keyword(s):  
Pid Controller ◽  
Controller Design ◽  
Dynamic Performance ◽  
Steam Pressure ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Tuning Method ◽  
Fuzzy Pid Controller ◽  
Pid Controller Design ◽  
High Control

This paper focus on designing a fuzzy PID controller design about the vapor pressure of the EPE foaming machine parameters, and raise a self-tuning method of PID parameters, and use the fuzzy control toolbox of MATLAB to simulate its control system, which are compared with the simulation of conventional PID controller, the results show the design of fuzzy PID controller have high control precision, small overshoot, good dynamic performance characteristics.

Sign in / Sign up

Export Citation Format

Share Document