A New Approach of Control Strategy for a Spherical Tank Level Process (original) (raw)
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Fuzzy Logic Based PID Controller for a Non Linear Spherical Tank System
2014
Non-linear process control is a difficult task in process Industries. Spherical tank level control is one among them due to the variation in cross sectional area. In this project modeling of Spherical tank system is done. The implementation of fuzzy logic controller (FLC) and Fuzzy PID for a spherical tank to control liquid level is studied. System identification of spherical tank system is done which is identified to be non-linear. Here the conventional PID controller parameters are designed based on Ziegler-Nicholas tuning method. The process is designed and implemented in Mat lab. It is observed from the result that Fuzzy based PID controller perform well in terms of less settling time, rise time and no overshoot in process output.
Real Time Implementation of Fuzzy Based Adaptive PI Controller for a Spherical Tank System
International Journal of Simulation Systems Science & Technology, 2013
This paper proposes a new fuzzy adaptive variable digital PI controller for a single input single output non-linear spherical tank level process system. The open loop transfer function models are carried out at three different operating regions and those models are formulated based on the real laboratory scale system. The proposed FAPI controller is a combination of two input two output Fuzzy logic controller and a Variable digital PI controller. The input to the fuzzy controller is error and change in error and its outputs are K P and K I. The PI controller's parameters are estimated on-line based on error and change in error. The real time implementation and control of the process plant is done in MATLAB using VMAT-01 Data Acquisition Module. The objective is to make the output to settle fast with minimum overshoot and the disturbances do not affect the performances of the system.
Investigation of Fuzzy Logic Controller for Conical Tank Process
The PID Controllers are commonly used in industries for nearly a century due to its simplicity, efficiency and flexibility. Recently, the control of non-linear processes in the industries have turned the attention towards the intelligent controllers such as, Neural Networks, Fuzzy Logic Controller, Genetic Algorithm tuned Controllers, Adaptive Controller, etc. This paper focuses on the Investigation of Intelligent Controllers for conical tank level process. A conical tank is a highly nonlinear process due to the variation in the area of cross section of the level system with change in shape. In this work, Fuzzy Logic Controller is designed for the control of nonlinear process to ensure the exact level maintenance. The simulation results are obtained by Servo and Regulator operation of the nonlinear conical tank process. For this work, Fuzzy Logic Controller is compared with Conventional PI Controller.
Design and Performance Analysis of Level Control Strategies in a Nonlinear Spherical Tank
Processes
This work seeks to contribute to the study of techniques for level control considering a nonlinear plant model. To achieve this goal, different approaches are applied to classical control techniques and their results are analyzed. Fuzzy Logic Control (FLC), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Model Predictive Control (MPC) and Nonlinear Auto-Regressive Moving Average (NARMA-L2) controllers are designed for the level control of a spherical tank. Subsequently, several tests and scenarios similar to those present in industrial processes are established, while the transient response of the controllers, their performance indices for monitoring the reference value, the rejection of disturbances, the presence of parameter uncertainties and the effects of noise are analyzed. The results show good reference tracking, with a settling time of approximately 5 s for 5 cm and a rise time of less than 4 s. No evidence for steady-state error or overshoot ...
This paper proposes a new tuning method of the PI controller based on New Modified Repetitive Control Strategy (NMRCS) approach. A non linear spherical tank liquid level system is considered here. The NMRC incorporates the idea of Repetitive Control (RC) which accomplishes perfect asymptotic set point tracking. The process dynamics of the level process in spherical tank are described by the differential equation and worst case model parameters are identified by influencing the step test technique. By utilizing relay feedback technique, the periodic reference signal of NMRCS is generated. From the input and output chattering signals of the NMRCS, optimized PI controller parameters are identified using Recursive Least Squares (RLS) fitting technique. Simulation results are endowed to demonstrate the efficiency of the proposed tuning method. A proof of robustness of the NMRCS is also analyzed.
Control Of Non Linear Spherical Tank Process With PI-PID Controllers – A Review
This paper focuses on the review of control methods for spherical tank system and tuning of non-linear PI / PID controllers in real time. The control of liquid level in spherical tank is complicated with conventional controllers due to variation in the area of transverse section of the tank. Thus the proposed non-linear PI and PID controllers are simulated and compared with the conventional PI and PID controllers available in the literature for spherical tank system. The proposed controllers are tuned based on Cohen Coon tuning method from the open loop response of the experimental setup of spherical tank in real time. The results of the proposed control methods are presented and compared with the conventional PI and PID controllers. The performance of the proposed control methods are evaluated with time domain specifications. The proposed non-linear PI and PID controller provides better response than the conventional PI and PID controllers for the spherical tank system.
Intelligent Controllers for Conical Tank Process
2014
Level control is an important control objective in process industries. Determining the optimal controller is vital, as it result in precise control of liquid level in the conical tank. The conventional PID controllers are used which will not provide a satisfactory control for various operating conditions. To overcome these difficulties, an intelligent controller is to be proposed. The objective of this project is to implement an intelligent controller for conical tank process. A Fuzzy Logic, Fuzzy PI and Neural Network controllers are implemented. Each controller is constructed based on the data collected from the process. The optimal control is identified as the Neural Network controller based on the performance indices such as settling time and overshoot.
A Robust Fuzzy Logic Control of Two Tanks Liquid Level Process
— An attempt has been made in this paper to analyze the efficiency of Fuzzy Logic, PID controllers on Non Interacting Two Tanks (Cylindrical) Liquid Level Process. The liquid level process exhibits Nonlinear square root law flow characteristics. The control problem formulated as level in second tank is controlled variable and the inlet flow to the first tank is manipulated variable. The PID Controller is designed based on Internal Model Control (IMC) Method. The Artificial Intelligent Fuzzy logic controller is designed based on six rules with Gaussian and triangular fuzzy sets. MATLAB-Simulink has been used to simulate and verified the mathematical model of the controller. Simulation Results show that the proposed Fuzzy Logic Controller show robust performance with faster response and no overshoot, where as the conventional PID Controller shows oscillations responses for set point changes. Thus, the Artificial Intelligent FLC is founded to give superior performance for a Non linear problem like two tanks. This paper will help the method suitable for research findings concerning on two tank liquid level system. Keywords—Fuzzy Logic Controller, MATLAB– Simulink, PID and Two tank Non-interacting level system.
Synthesis of fuzzy sliding mode controller for liquid level control in spherical tank
Cogent Engineering
Spherical tanks are often used in process industries as storage or surge tank where control of level is essential. The liquid level in the spherical tank is modeled using first principle technique. A sliding mode controller (SMC) is designed initially to get the information of the stable closed loop process. Fuzzy-based SMC controller is developed by collecting the sliding surface data to reduce the chattering effect caused by SMC and to get the smooth sliding surface. The designed controller can drive the system states to the boundary layer. The performance of the proposed controller is compared with IMC based PI controller and the SMC. Proposed controller performance is encouraging and can be suggested for implementation in controlling the liquid level in spherical geometry.
Level Control of Two Conical Tank Non Interacting System using PID and Fuzzy Logic
IJIREEICE, 2017
Non-linear process control is a difficult problem in process industries. Conical tank level control is one among them. Conical tanks are widely used in many industries due to its shape which provides easy discharge of water when compared to other tanks. Moreover, liquid level control of a conical tank is still challenging for typical process control because of its nonlinearities by a reason of constantly changing cross section area. By using fuzzy logic, designers can realize lower development costs, superior features, and better end product performance. Fuzzy is often the very best way as they are faster and cheaper. One of successful application that used fuzzy control is liquid tank level control. The purpose of this project is to design a simulation system of fuzzy logic controller for liquid tank level control by using simulation package which is Fuzzy Logic Toolbox and Simulink in MATLAB software. In this paper the mathematical modeling of two non-interacting conical tanks by PID controller and fuzzy. In this paper, we take the liquid level water tank, and use MATLAB to design a Fuzzy Control. Then we analyze the control effect and compare it with the effect of PID controller. As a result of comparing, Fuzzy Control is superior to PID control. Especially it can give more attention to various parameters, such as the time of response, the error of steadying and overshoot. Comparison of the control results from these two systems indicated that the fuzzy logic controller significantly reduced overshoot and steady state error.