Improving a pipeline hybrid dynamic model using 2DOF PID

Similar documents
Digital Control of MS-150 Modular Position Servo System

Design of an Intelligent Pressure Control System Based on the Fuzzy Self-tuning PID Controller

1045. Vibration of flexible rotor systems with twodegree-of-freedom

TABLE OF CONTENTS CHAPTER TITLE PAGE DECLARATION DEDICATION ACKNOWLEDGEMENT ABSTRACT ABSTRAK

COMPARISON OF TUNING METHODS OF PID CONTROLLER USING VARIOUS TUNING TECHNIQUES WITH GENETIC ALGORITHM

Embedded based Automation System for Industrial Process Parameters

Relay Based Auto Tuner for Calibration of SCR Pump Controller Parameters in Diesel after Treatment Systems

Simulation Analysis of Control System in an Innovative Magnetically-Saturated Controllable Reactor

Some Tuning Methods of PID Controller For Different Processes

DC Motor Speed Control using Artificial Neural Network

CHAPTER 6. CALCULATION OF TUNING PARAMETERS FOR VIBRATION CONTROL USING LabVIEW

Chapter 4 PID Design Example

CONTROLLER TUNING FOR NONLINEAR HOPPER PROCESS TANK A REAL TIME ANALYSIS

Comparison Effectiveness of PID, Self-Tuning and Fuzzy Logic Controller in Heat Exchanger

Design and Simulation of Fuzzy Logic controller for DSTATCOM In Power System

Control Design Made Easy By Ryan Gordon

Temperature Control in HVAC Application using PID and Self-Tuning Adaptive Controller

Dynamic displacement estimation using data fusion

CONTROLLER DESIGN ON ARX MODEL OF ELECTRO-HYDRAULIC ACTUATOR

Controller Tuning for Disturbance Rejection Associated with Delayed Double Integrating Process, Part III: PI-PD Controller

STAND ALONE CONTROLLER FOR LINEAR INTERACTING SYSTEM

Loop Design. Chapter Introduction

ISSN Vol.04,Issue.06, June-2016, Pages:

Performance Comparisons between PID and Adaptive PID Controllers for Travel Angle Control of a Bench-Top Helicopter

CHASSIS DYNAMOMETER TORQUE CONTROL SYSTEM DESIGN BY DIRECT INVERSE COMPENSATION. C.Matthews, P.Dickinson, A.T.Shenton

Automatic Control Motion control Advanced control techniques

ACTIVE VIBRATION CONTROL OF HARD-DISK DRIVES USING PZT ACTUATED SUSPENSION SYSTEMS. Meng-Shiun Tsai, Wei-Hsiung Yuan and Jia-Ming Chang

FUZZY AND NEURO-FUZZY MODELLING AND CONTROL OF NONLINEAR SYSTEMS

6545(Print), ISSN (Online) Volume 4, Issue 1, January- February (2013), IAEME & TECHNOLOGY (IJEET)

Performance Analysis of Conventional Controllers for Automatic Voltage Regulator (AVR)

Second order Integral Sliding Mode Control: an approach to speed control of DC Motor

Simulation and Analysis of Cascaded PID Controller Design for Boiler Pressure Control System

Review of PI and PID Controllers

Modal damping identification of a gyroscopic rotor in active magnetic bearings

Estimation of State Variables of Active Suspension System using Kalman Filter

ROBUST SERVO CONTROL DESIGN USING THE H /µ METHOD 1

REDUCING THE VIBRATIONS OF AN UNBALANCED ROTARY ENGINE BY ACTIVE FORCE CONTROL. M. Mohebbi 1*, M. Hashemi 1

Implementation of Proportional and Derivative Controller in a Ball and Beam System

An Implementation for Comparison of Various PID Controllers Tuning Methodologies for Heat Exchanger Model

Lecture 10. Lab next week: Agenda: Control design fundamentals. Proportional Control Proportional-Integral Control

Non-contact structural vibration monitoring under varying environmental conditions

Hacettepe University, Ankara, Turkey. 2 Chemical Engineering Department,

International Journal of Advance Engineering and Research Development. Aircraft Pitch Control System Using LQR and Fuzzy Logic Controller

Relay Feedback based PID Controller for Nonlinear Process

Controller Tuning for Disturbance Rejection Associated with Delayed Double Integrating processes, Part IV: PID Plus First-Order Lag Controller

VARIABLE STRUCTURE CONTROL DESIGN OF PROCESS PLANT BASED ON SLIDING MODE APPROACH

New PID Tuning Rule Using ITAE Criteria

Cohen-coon PID Tuning Method; A Better Option to Ziegler Nichols-PID Tuning Method

Sloshing Damping Control in a Cylindrical Container on a Wheeled Mobile Robot Using Dual-Swing Active-Vibration Reduction

Load frequency control of interconnected system

Vibration Control of Mechanical Suspension System Using Active Force Control

CHAPTER 7 CONCLUSIONS AND FUTURE SCOPE

DC Motor Speed Control: A Case between PID Controller and Fuzzy Logic Controller

1712. Experimental study on high frequency chatter attenuation in 2-D vibration assisted micro milling process

Disturbance Rejection Using Self-Tuning ARMARKOV Adaptive Control with Simultaneous Identification

Glossary of terms. Short explanation

Diagnostics of bearings in hoisting machine by cyclostationary analysis

CONTINUOUS MOTION NOMINAL CHARACTERISTIC TRAJECTORY FOLLOWING CONTROL FOR POSITION CONTROL OF AN AC DRIVEN X-Y BALL SCREW MECHANISM

Non Linear Tank Level Control using LabVIEW Jagatis Kumaar B 1 Vinoth K 2 Vivek Vijayan C 3 P Aravind 4

EMPIRICAL MODEL IDENTIFICATION AND PID CONTROLLER TUNING FOR A FLOW PROCESS

NINTH INTERNATIONAL CONGRESS ON SOUND AND VIBRATION, ICSV9 ACTIVE VIBRATION ISOLATION OF DIESEL ENGINES IN SHIPS

Development of Fuzzy Logic Controller for Quanser Bench-Top Helicopter

1319. A new method for spectral analysis of non-stationary signals from impact tests

TRACK-FOLLOWING CONTROLLER FOR HARD DISK DRIVE ACTUATOR USING QUANTITATIVE FEEDBACK THEORY

BIDIRECTIONAL SOFT-SWITCHING SERIES AC-LINK INVERTER WITH PI CONTROLLER

MD-TDOF-PID Controller Based on LabView Yu Jian, Liu Changliang

Arvind Pahade and Nitin Saxena Department of Electrical Engineering, Jabalpur Engineering College, Jabalpur, (MP), India

INTELLIGENT ACTIVE FORCE CONTROL APPLIED TO PRECISE MACHINE UMP, Pekan, Pahang, Malaysia Shah Alam, Selangor, Malaysia ABSTRACT

Review Paper on Comparison of various PID Controllers Tuning Methodologies for Heat Exchanger Model

STABILITY IMPROVEMENT OF POWER SYSTEM BY USING PSS WITH PID AVR CONTROLLER IN THE HIGH DAM POWER STATION ASWAN EGYPT

Research Article Multi-objective PID Optimization for Speed Control of an Isolated Steam Turbine using Gentic Algorithm

DSP-Based Simple Technique for Synchronization of 3 phase Alternators with Active and Reactive Power Load Sharing

Application of Proposed Improved Relay Tuning. for Design of Optimum PID Control of SOPTD Model

1818. Evaluation of arbitrary waveform acoustic signal generation techniques in dispersive waveguides

Parameter Estimation based Optimal control for a Bubble Cap Distillation Column

Design of Fractional Order Proportionalintegrator-derivative. Loop of Permanent Magnet Synchronous Motor

Fuzzy Logic Controller on DC/DC Boost Converter

Auto-tuning of PID Controller for the Cases Given by Forbes Marshall

PID CONTROLLERS DESIGN APPLIED TO POSITIONING OF BALL ON THE STEWART PLATFORM

SIMULINK MODELING OF FUZZY CONTROLLER FOR CANE LEVEL CONTROLLING

Development of a Fuzzy Logic Controller for Industrial Conveyor Systems

AUTOMATIC PID PARAMETER TUNING BASED ON UNFALSIFIED CONTROL

Modified ultimate cycle method relay auto-tuning

SIMULATION AND IMPLEMENTATION OF PID-ANN CONTROLLER FOR CHOPPER FED EMBEDDED PMDC MOTOR

Design Of PID Controller In Automatic Voltage Regulator (AVR) System Using PSO Technique

Online Tuning of Two Conical Tank Interacting Level Process

PID control of dead-time processes: robustness, dead-time compensation and constraints handling

PID Controller Design Based on Radial Basis Function Neural Networks for the Steam Generator Level Control

Anfis Based Soft Switched Dc-Dc Buck Converter with Coupled Inductor

A CONTROL STRATEGY TO STABILIZE PWM DC-DC BUCK CONVERTER WITH INPUT FILTER USING FUZZY-PI AND ITS COMPARISON USING PI AND FUZZY CONTROLLERS

Design of Joint Controller for Welding Robot and Parameter Optimization

TUNING OF PID CONTROLLER USING PSO AND ITS PERFORMANCES ON ELECTRO-HYDRAULIC SERVO SYSTEM

Further Control Systems Engineering

MODEL BASED CONTROL FOR INTERACTING AND NON-INTERACTING LEVEL PROCESS USING LABVIEW

DECENTRALISED ACTIVE VIBRATION CONTROL USING A REMOTE SENSING STRATEGY

Transient stability improvement by using shunt FACT device (STATCOM) with Reference Voltage Compensation (RVC) control scheme

Application of Fuzzy Logic Controller in Shunt Active Power Filter

Comparative Analysis of PID, SMC, SMC with PID Controller for Speed Control of DC Motor

Genetic Algorithm Optimisation of PID Controllers for a Multivariable Process

Comparative Analysis of a PID Controller using Ziegler- Nichols and Auto Turning Method

Transcription:

Improving a pipeline hybrid dynamic model using 2DOF PID Yongxiang Wang 1, A. H. El-Sinawi 2, Sami Ainane 3 The Petroleum Institute, Abu Dhabi, United Arab Emirates 2 Corresponding author E-mail: 1 yowang@pi.ac.ae, 2 aelsinawi@pi.ac.ae, 3 sainane@pi.ac.ae (Received 5 June 216; accepted 14 June 216) Abstract. This work presents a novel dynamic modeling technique to predict the vibration of a pipe at different locations. An initial model of the pipe was constructed using experimental input-output data and system identification (SysID) techniques. The model derived through SysID showed a poor performance in predicting the vibration of the pipeline at locations other than that of the output sensor. Improvement of the SysID model was achieved using the Linear Quadratic Gaussian (LQG) observer-regulator modeling approach. While the hybrid model, combining both SysID and LQG, performed better, its transient response was not accurate. Further improvement to the model was achieved by incorporating a two degree of freedom, (2DOF) Proportional-Integral-Derivative (PID) to improve transient characteristics of the model. Experimental evaluation of the 2DOF PID assisted hybrid model showed good performance that far exceeded that of the hybrid model alone. The validity of the proposed approach was experimentally verified and the results are presented. Keywords: 2DOF PID, Hybrid Dynamic Model, System Identification, LQE, LQR, Pipeline. 1. Introduction Because of their easy integration in software and hardware, and because they are easy to design and tune, PID controllers are widely used in chemical process, electrical and dynamic systems [1, 2]. The performance of a controller is rated according to three criteria: load disturbance rejection, set-point response and robustness to model uncertainties in the process [3]. In conventional PID controllers, the disturbance response and set-point response influence each other making it impossible to properly tune both at the same time. On the other hand, 2DOF PID controllers do not suffer from such a limitation as disturbance rejection parameters and set-point response parameters can be adjusted separately. Araki and Taguchi [4] presented a survey of PID controllers performance which included equivalent transformations, described the effect of the 2DOF structure and proposed an optimal tuning method. Yukitomo, Shigemasa [5] introduced the Model-Driven TDOF PID control system, based on the Model-Driven control concept and used the quick responses for both set-point tracking and disturbance regulating response to show the effectiveness of the control system. 2. Improvement of the model 2.1. Construction of the hybrid model without 2DOF PID In the experimental setup shown in Fig. 1 accelerometer (a) is located at the right end of the pipe section, accelerometer (b) is located in the middle of the section and accelerometer (c) is located at the left of the section. An instrumented hammer applies the excitation force at the right end of the pipe. DSpace and its data acquisition system measures the hammer force as well as the accelerometer responses. The experimental data is utilized in a hardware-in-the-loop format as shown in Fig. 2. to produce an optimal estimate of the pipe vibration at any desired location. It should be noted that only one accelerometer measurement is used to update the model (i.e., feedback signal), the 28 JVE INTERNATIONAL LTD. VIBROENGINEERING PROCEDIA. OCT 216, VOL. 8. ISSN 2345-533

remaining accelerometers are used for comparison with estimates at corresponding locations. The system identification toolbox in MATLAB is utilized to obtain the initial model from raw experimental data [6, 7]. Moreover, the initial model is used to construct the hybrid model which uses the LQE and LQR techniques to generate optimal estimates of the system s states such that the associated cost functions are minimized. Fig. 1. Experimental setup 2.2. Improving the model with 2DOF PID Fig. 2. Schematic of the hybrid model without 2DOF PID A 2DOF PI controller is used to improve the performance of the hybrid model, the general block diagram of the PID control system [8] is shown in Fig. 3. Fig. 3. Block diagram of 2DOF control system In the process, s, s and G s are transfer functions which are feedback and feedforward controllers as shown in Eqs. (1), (2) and (3): 1 1, JVE INTERNATIONAL LTD. VIBROENGINEERING PROCEDIA. OCT 216, VOL. 8. ISSN 2345-533 281 (1)

1, 1. (2) (3) Eq. (1) and (2) represent the PI controllers, in Eq. (2) the parameter has no influence on disturbance rejection, while it has a significant influence on the set point response. In Eq. (3), and are the process gain, time constant and dead time, respectively. Fig. 4. Parameter setting panel of 2DOF PID The parameter setting panel of 2DOF PID, in Simulink, is shown in Fig. 4. The parameter Derivative (D) is set to, to avoid adding any fictitious damping to the original model. Other parameters are properly adjusted to achieve proper tracking of the actual response of the pipeline at measurements location. The 2DOF controller is incorporated into the model as shown in Fig. 5. It is clear that the model uses feedback from the actual system to generate better estimates of the plant (actual pipe) states. The model produced can estimate, with good accuracy, the vibration at any location along the span of the pipeline without the need for any physical measurements at that location. This makes the proposed approach unique, as it uses minimal feedback measurements to yield good estimates at any desired location on the structure. This method can be used for any dynamic structure and it is not limited to pipelines only. Fig. 5. Schematic of improving the Hybrid Model with 2DOF PID 282 JVE INTERNATIONAL LTD. VIBROENGINEERING PROCEDIA. OCT 216, VOL. 8. ISSN 2345-533

3. Simulation results Comparison between the estimated response and the actual response using the hybrid model without 2DOF PID controller, in the frequency domain, is shown in Fig. 6 (the solid line represents the actual response and the dashed line represents the estimated one). It is clear that the model is capable of predicting the vibration fairly accurately with minimal discrepancies and exact resonant frequencies match. However, the amplitudes at each frequency are not accurate. However, applying the 2DOF PID controller shows that the amplitude scaling has a significant improvement as shown in Fig. 7. 1-1 -1-6 1 2 3 4 5 1 2 3 4 5 Fig. 6. Comparison of spectrogram between estimated and actual results (middle and right accelerometers) using hybrid model 1-1 -1-6 5 1 15 2 25 3 35 4 45 5 4. Conclusions Fig. 7. Comparison of spectrogram between estimated and actual results (middle and right accelerometers) using 2DOF PID In this work, a novel approach for modeling a dynamic system using triple components is presented. An initial SysID model is constructed from impulse response data of the structure. After improvement of the initial model using LQE and LQR, combined LQG technique, it is further improved using a 2DOF PID controller. The latter improved the model significantly and its accuracy was verified experimentally. The model succeeded in predicting the vibration at any location of choice along a pipe with good accuracy. Acknowledgement Authors acknowledge the support of the Petroleum Institute. -6 5 1 15 2 25 3 35 4 45 5 JVE INTERNATIONAL LTD. VIBROENGINEERING PROCEDIA. OCT 216, VOL. 8. ISSN 2345-533 283

References [1] Visioli A. Fuzzy logic based set-point weight tuning of PID controllers. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, Vol. 29, Issue 6, 1999, p. 587-592. [2] Kim D. H. Tuning of 2-DOF PID controller by immune algorithm. Proceedings of the 22 Congress on Evolutionary Computation, 22. [3] Nemati H., Bagheri P. A new approach to tune the two-degree-of-freedom (2DOF). IEEE International Symposium on Computer-Aided Control System Design (CACSD), 21. [4] Araki M., Taguchi H. Two-degree-of-freedom PID controllers. International Journal of Control, Automation, and Systems, Vol. 1, Issue 4, 23, p. 41-411. [5] Yukitomo M., et al. A two degrees of freedom PID control system, its features and applications. 5th Asian Control Conference, 24. [6] Ljung L. System Identification Toolbox: User s Guide. Citeseer, 1995. [7] Ljung L. System Identification Toolbox for Use with MATLAB. 27. [8] Åström K. J., Hägglund T. The future of PID control. Control Engineering Practice, Vol. 9, Issue 11, 21, p. 1163-1175. 284 JVE INTERNATIONAL LTD. VIBROENGINEERING PROCEDIA. OCT 216, VOL. 8. ISSN 2345-533