This book presents the study to design, develop, and implement improved PI control techniques using dead-time compensation, structure enhancements, learning functions and fractional ordering parameters. Two fractional-order PI controllers are proposed and designed: fractional-order predictive PI and hybrid iterative learning based fractional-order predictive PI controller. Furthermore, the proposed fractional-order control strategies and filters are simulated over first- and second-order benchmark process models and further validated using the real-time experimentation of the pilot pressure process plant.
In this book, five chapters are structured with a proper sequential flow of details to provide a better understanding for the readers. A general introduction to the controllers, filters and optimization techniques is presented in Chapter 1. Reviews of the PI controllers family and their modifications are shown in the initial part of Chapter 2, followed by the development of the proposed fractional-order predictive PI (FOPPI) controller with dead-time compensation ability. In the first part of chapter 3, a review of the PI based iterative learning controllers, modified structures of the ILC and their modifications are presented. Then, the design of the proposed hybrid iterative learning controller-based fractional-order predictive PI controller based on the current cyclic feedback structure is presented. Lastly, the results and discussion of the proposed controller on benchmark process models and the real-time experimentation of the pilot pressure process plant are given. Chapter 4 presents the development of the proposed filtering techniques and their performance comparison with the conventional methods. Chapter 5 proposes the improvement of the existing sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) to form a novel arithmetic-trigonometric optimization algorithm (ATOA) to accelerate the rate of convergence in lesser iterations with mitigation towards getting caught in the same local position. The performance analysis of the optimization algorithm will be carried out on benchmark test functions and the real-time pressure process plant.
Author(s): Arun Mozhi Devan Panneer Selvam, Fawnizu Azmadi Hussin, Rosdiazli Ibrahim, Kishore Bingi, Nagarajapandian M.
Series: Studies in Infrastructure and Control
Publisher: Springer
Year: 2022
Language: English
Pages: 155
City: Singapore
Preface
Acknowledgements
Contents
About the Editors
Acronyms
1 Introduction
1.1 Introduction
1.2 Summary
References
Part I Fractional-Order Predictive PI Controllers
2 Fractional-Order Predictive PI Controller for Dead-Time Process Plants
2.1 PID Controller and Its Modifications
2.2 Related Works on Modified PI Control Strategies
2.2.1 Predictive PI Controller
2.2.2 Fractional-Order PI Controller
2.3 Related Works on Control of Pressure Process Plant
2.4 Development of Proposed Fractional-Order Predictive PI Controller
2.4.1 Fractional-Order PI Controller
2.4.2 Fractional-Order Predictive PI Controller
2.4.3 Stability Analysis
2.5 Selection of Benchmark Process Models
2.6 Case Studies on Real-Time Pressure Process Plant
2.6.1 Piping and Instrumentation Diagram of Pressure Process Plant
2.6.2 Schematics of Experimental Setup
2.6.3 Implementation of Proposed Fractional-Order Controllers and Filters
2.6.4 Performance Measure
2.7 Simulation Study on Benchmark Process Models
2.7.1 First-Order System
2.7.2 Second-Order System
2.8 Experimental Analysis on Real-Time Pilot Pressure Process Plant
2.8.1 Mathematical Modelling of Pressure Process Plant
2.8.2 Experimental Results
2.9 Summary
References
3 Hybrid Iterative Learning Controller-Based Fractional-Order Predictive PI Controller
3.1 PI-Type Iterative Learning Control
3.2 Development of Proposed Hybrid Iterative Learning Controller-Based …
3.2.1 Iterative Learning Controller
3.2.2 Learning Function and Q-Filter Design
3.2.3 Hybrid Iterative Learning Controller-Based Fractional-Order Predictive PI Controller
3.3 Simulation Study on Benchmark Process Models
3.3.1 First-Order System
3.3.2 Second-Order System
3.4 Real-Time Pilot Pressure Process Plant Experimentation
3.5 Summary
References
Part II Filtering and Optimization Techniques
4 Fractional-Order Filtering Techniques
4.1 Related Works on Filtering in Process Control Loop
4.1.1 Set-Point Filter
4.1.2 Noise Filter
4.2 Development of Proposed Fractional-Order Filtering Techniques
4.2.1 Fractional-Order Set-Point Filter
4.2.2 Fractional-Order Noise Filter
4.3 Simulation Study on Benchmark Process Models
4.3.1 Performance of Fractional-Order Set-Point Filter
4.3.2 Performance of Fractional-Order Noise Filter
4.3.3 Performance of Combined Fractional-Order Set-Point and Noise Filters
4.4 Real-Time Pilot Pressure Process Plant Experimentation
4.4.1 Performance of Proposed Set-Point Filter
4.4.2 Performance of Proposed Noise Filter
4.4.3 Performance of Combined Set-Point and Noise Filters
4.5 Summary
References
5 Arithmetic-Trigonometric Optimization Algorithm
5.1 Related Works on Optimization Algorithms
5.1.1 Sine Cosine Optimization Algorithm
5.1.2 Arithmetic Optimization Algorithm
5.2 Development of the Proposed Arithmetic-Trigonometric Optimization Algorithm
5.2.1 Sine Cosine Algorithm
5.2.2 Arithmetic Optimization Algorithm
5.2.3 Arithmetic-Trigonometric Optimization Algorithm
5.2.4 Pseudocode of Arithmetic-Trigonometric Optimization Algorithm
5.2.5 Tuning of Controller Parameters Using Arithmetic-Trigonometric Optimization Algorithm
5.3 Performance of the Proposed Arithmetic-Trigonometric Optimization Algorithm
5.3.1 Benchmark Functions
5.3.2 Numerical Analysis on Benchmark Functions
5.3.3 Convergence Analysis of Benchmark Functions
5.3.4 Experimental Analysis of Real-Time Pressure Process Plant for Optimized Fractional Predictive PI Controller
5.4 Summary
References
Appendix A Design of Fractional-Order PI and Predictive PI Controllers
A.1 Fractional-Order PI Controller
A.2 Typical PPI Structure
Appendix B Brief on SCA and AOA Algorithms
B.1 Sine Cosine Algorithm
B.2 Arithmetic Optimization Algorithm
B.3 Arithmetic–Trigonometric Optimization Algorithm
Index
