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A Performance Comparison of PID, FLC & MPC Controller

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A Performance Comparison of PID, FLC & MPC Controller A Performance Comparison Of PID, FLC & MPC Controller: A Comparative Case Study Thesis submitted in partial fulfilment of the requirement for the award of degree of Master of Engineering in Electronics Instrumentation and Control Engineering SUBMITTED By: Sachin Agrahari ROLL NO: 80751020 Under the supervision of: Dr.Yaduvir Singh Dr.Hardeep Singh Assost. Professor, EIED Assist. Professor, ECED DEPARTMENT OF ELECTRICAL AND INSTRUMENTATION ENGINEERING THAPAR UNIVERSITY PATIALA – 147004 JULY 2009 i ii ABSTRACT This work provides the knowledge about the advance technique like Fuzzy Logic controller technique and MPC, Modal Predictive control technique, to get the better response of the system, when input like unit step input and unit ramp input is applied as the set point of the system. A comparative study is made on the performance of the PID,FLC and MPC controller getting better system parameter like Maximum overshoot( M p ),Rise time(t r ),Peak time(t p ) Settling time( t s ),Steady state error( ess ).Finally a simulation block is made using simulink library browser where all the blocks are given. In the response it is found that settling time, rise time steady state error is less in case of MPC controller rather than PID and FLC controller. Study shows more accurate result using Fuzzy controller over PID controller, further better result got in case of MPC controller. iii ORGANIZATION OF THESIS Chapter-1 It includes the background information and overview of thesis report. Chapter-2 This chapter describes the fundamental of boiler is and detailed building blocks of simulation model of three element control. Chapter-3 Three element control has been elaborated in this chapter. Chapter-4 Problem formulation and proposed solution with building simulation has been discussed in this chapter. Chapter-5 This chapter contain analysis of the system with different controller for two inputs. Chapter-6 The result and discussion has been described in this chapter Chapter-7 Thesis has been concluded with future scope in this chapter iv LIST OF CONTENTS CONTENTS PAGE NO. Declaration i Acknowledgement ii Abstract iii Organization of Thesis iv List of contents v List of figures viii List of tables ix Chapter 1: Introduction 1-3 1.1 Introduction 1 1.2 Three Element Control 1 1.3 Applications 2 1.4 Control Strategies 2 1.4.1 PID Controller 2 1.4.2 Fuzzy Logic Controller 2 1.4.3 MPC Controller 3 1.5 Structure of proposed system 3 Chapter 2: Fundamental of Boiler & Blocks 4-18 2.1 Introduction 4 2.2 Boiler 5 2.3 Types of Boiler 6 2.3.1 Pot boiler 6 2.3.2 Fire-tube boiler 6 2.3.3 Water-tube boiler 7 2.3.4 Flash boiler 8 2.3.5 Sectional boiler 8 2.3.6 Multi tube boiler 8 v 2.3.7 Superheated steam boilers 8 2.3.8 Hydronic boilers 9 2.4 Material 11 2.5 Fuel 11 2.6 Safety 11 2.7 Flow 12 2.8 Description of Model 13 2.8.1 Mux Block 13 2.8.2 Scope Block 14 2.8.3 Saturation Block 15 2.8.4 Transfer Function Block 16 2.8.5 Step Block 17 2.8.6 IMC Tuning 17 2.8.7 Fuzzy Logic Controller 18 Chapter 3: Three Element Control 20-28 3.1 Introduction 20 3.2 Boiler Drum Level Control 21 3.2.1 Single element drums level control 21 3.2.2 Two element drum level control 22 3.2.3 Three-element drum level control 24 3.3 Enhanced three element drum level control 25 3.4 Drum Level Control Systems: 25 3.5 Drum Level Controller 27 3.6 Feed water Flow Controller 28 Chapter 4: Methodology 29-33 4.1 Problem Definition 29 4.2 Proposed Solution 29 4.3 Working of the fuzzy controller 30 4.4 Flow chart for Modal Predictive control 31 4.5 Working modal of system 32 4.6 Simulink 32 4.7 Working with Simulink 33 vi Chapter 5: Simulation & Testing 38-66 5.1 Introduction 38 5.2 PID control scheme 39 5.2.1 PID Controller 40 5.2.2 PID controller theory 40 5.2.3 Proportional term 41 5.2.4 Integral term 42 5.3 Fuzzy Logic System 48 5.4 An Intelligent control scheme using FLS 48 5.5 Fuzzy Logic Control (FLC) Technique 49 5.5.1 Fuzzifier 49 5.5.2 Fuzzy rule base 49 5.5.3 Process knowledge and FL rules 49 5.5.4 Numerical data and FLC rules 50 5.5.6 Fuzzy inference engine 50 5.5.7 Defuzzifier 50 5.6 MPC controller (Modal predictive Controller) 54 5.6.1 Theory behind MPC 55 5.6.2 Principles of MPC 55 5.7 Description 56 5.8 Simulation with PID controller (Step Input) 58 5.9 Simulation using Fuzzy controller (Step Input) 60 5.10 Simulation using MPC Controller (Step Input) 61 5.11 Simulation with PID controller (Ramp Input) 62 5.12 Simulation with Fuzzy controller (Ramp Input) 65 5.13 Simulation with MPC controller (Ramp Input) 66 Chapter 6: Result & Discussion 67-69 Chapter 7: Conclusion & Future Scope 70 References: 71-74 vii LIST OF FIGURES Figure Figure Name Page No. Fig 2.1 An industrial boiler 5 Fig 2.2 Diagram of a fire-tube boiler 7 Fig 2.3 Diagram of a water-tube boiler 7 Fig 2.4 Block diagram of MUX 13 Fig 2.5 Scope 15 Fig 2.6 Scope window 15 Fig 2.7 Block diagram of saturation 15 Fig 2.8 Block diagram of Transfer Fn 17 Fig 2.9 Block diagram of Step Fn. 17 Fig 2.10 IMC parameters block 18 Fig 2.11 Block diagram of fuzzy controller 19 Fig 2.12 Block parameters 19 Fig 3.1 Diagram for three element control 22 Fig 3.2 Two element control 23 Fig 3.3 Three element control 25 Fig 3.4 Single element control 27 Fig 4.1 Fuzzy controller modal 30 Fig 4.2 Block diagram of MPC 31 Fig 4.3 Block diagram of system 32 Fig 4.4 Tool bar 33 Fig 4.5 Simulink Library Browser 33 Fig 4.6 Workspace window 34 Fig 4.7 List of Blocks 35 Fig 4.8 Dragging of block on window 35 Fig 4.9 Example taken 36 Fig 4.10 Connection of blocks 36 Fig 4.11 Scope 37 Fig 5.1 Plot of PV vs time, for three values of Kp (Ki and Kd held constant) 41 viii Fig 5.2 Plot of PV vs time, for three values of Ki (Kp and Kd held constant) 43 Fig 5.3 Plot of PV vs time, for three values of Kd (Kp and Ki held constant) 44 Fig 5.4 Block diagram of PID controller 45 Fig 5.5 Block parameters 45 Fig 5.6 Block diagram for power generation process 46 Fig 5.7 3-Element PID control 47 Fig 5.8 Fig block diagram using PID controller 47 Fig 5.9 Control using FLS 48 Fig 5.10 Fig Block diagram using Fuzzy controller 51 Fig 5.11 Fuzzy Logic Toolbox (Using mamdani method) 51 Fig 5.12 Input1 Membership Function 52 Fig 5.13 Input2 member ship function 53 Fig 5.14 Output Membership Function 53 Fig 5.15 Rule base 54 Fig 5.16 Scheme for Modal Predictive control 56 Fig 5.17 Block parameter of MPC controller 57 Fig 5.18 Block of MPC controller 58 Fig 5.19 Unit step input is applied 58 Fig 5.20 Step Response with PID controller 59 Fig 5.21 PID controller response with unit step input 60 Fig 5.22 Fuzzy controller response with unit step input 61 Fig 5.23 MPC controller response with unit step input 62 Fig 5.24 Unit ramp response with Fuzzy controller 63 Fig 5.25 PID controller response with ramp input 64 Fig 5.26 Fuzzy controller response with ramp input 65 Fig 5.27 MPC controller response with ramp input 66 Table 6.1 Value of parameters when Unit step is applied 67 Table 6.2 Value of parameters when Ramp input is applied 68 ix LITERATURE SURVEY M. H. DwarakanathEt.al. (1982).They have generalized the methodology for modelling various system components in power system dynamics simulation studies. One of the salient features of the method was its applicability to transient stability, mid-term dynamics simulation and long-term dynamics simulation. Also, the application of sparse matrix techniques to the computation of initial conditions was presented. This method accommodated models of varied degrees of complexity without the need for rewriting or modifying the computer software. The generalized methodology lended itself for implementation in an array processor. M.Nomura (1988) .He had described a new adaptive optimal control method for boiler steam temperature control of thermal power plants. He has introduced a model of a coal-fired power plant and the automatic plant control (APC) system which assumed to be a single controlled object in order to allow the APC to continue controlling the power plant, even if the adaptive optimal control system fails. Optimal controller gains were calculated using the identified model B.W.Hogg Et.al. (1990) They had presented an application of generalized predictive control (GPC) to superheat steam pressure of a 200 MW drum boiler. They first used a, single loop PI controllers, but seen that the performance of such controllers is limited since they do not account for variations in the system parameters.
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