Optimal Fractional-order Predictive PI Controllers

Table of Contents

1. Frontmatter

2. Chapter 1. Introduction

   Arun Mozhi Devan Panneer Selvam, Fawnizu Azmadi Hussin, Rosdiazli Ibrahim, Kishore Bingi, Nagarajapandian M.

   Abstract

   This chapter discusses the general introduction of the traditional PI controllers, fractional-order controllers with different implementable mechanisms, and optimization algorithms like sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) for various applications.

3. Fractional-Order Predictive PI Controllers

   1. Frontmatter

   2. Chapter 2. Fractional-Order Predictive PI Controller for Dead-Time Process Plants

      Arun Mozhi Devan Panneer Selvam, Fawnizu Azmadi Hussin, Rosdiazli Ibrahim, Kishore Bingi, Nagarajapandian M.

      Abstract

      In the initial parts of this chapter, a detailed review of the PI controllers and their different modifications like predictive PI (PPI), fractional-order PI (FOPI), set-point weighted PI (SWPI), fuzzy PI (FPI), filtered predictive PI (FPPI), non-linear PI (NPI), iterative learning controller PI (ILCPI), internal model controller PI (IMCPI), and model predictive control PI (MPCPI) are presented. The second part discusses the design of a fractional-order predictive PI (FOPPI) controller with the dead-time compensating structure. The following section presents the selection of benchmark process models for simulation analysis and the real-time experimental analysis of the industrial-scale pressure process plant. Results and discussion will be carried out for disturbance rejection, effective control signal generation, and variable set-point tracking performance. Finally, the last section will summarize the chapter.

   3. Chapter 3. Hybrid Iterative Learning Controller-Based Fractional-Order Predictive PI Controller

      Arun Mozhi Devan Panneer Selvam, Fawnizu Azmadi Hussin, Rosdiazli Ibrahim, Kishore Bingi, Nagarajapandian M.

      Abstract

      In the initial parts of this chapter, a detailed review of the PI-type iterative learning controllers with their different modifications is discussed. The second part discusses the development of a hybrid iterative learning controller-based fractional-order predictive PI controller (ILC-FOPPI) is presented with their learning function and q-filter design. The following section presents the selection of first-order and second-order benchmark process models for simulation analysis and the real-time experimental results on the industrial-scale pressure process plant. Finally, the last section will summarize the chapter.

4. Filtering and Optimization Techniques

   1. Frontmatter

   2. Chapter 4. Fractional-Order Filtering Techniques

      Arun Mozhi Devan Panneer Selvam, Fawnizu Azmadi Hussin, Rosdiazli Ibrahim, Kishore Bingi, Nagarajapandian M.

      Abstract

      This chapter presents the design of fractional-order set-point and noise filters using the essential plant dynamics to handle the set-point load variations and stochastic noises. The proposed technique is compared with various existing methods using simulation analysis on benchmark process models and the real-time experimental investigation of the industrial-scale pressure process plant. Finally, the last section will summarize the chapter.

   3. Chapter 5. Arithmetic-Trigonometric Optimization Algorithm

      Arun Mozhi Devan Panneer Selvam, Fawnizu Azmadi Hussin, Rosdiazli Ibrahim, Kishore Bingi, Nagarajapandian M.

      Abstract

      This chapter focuses on obtaining an optimal controller parameter with faster convergence and the best solution. Hence, to obtain the effective parameters, a novel arithmetic-trigonometric optimization algorithm is developed by using the essential arithmetic operators and the basic trigonometric functions. Different combinations of the proposed technique are created with multiple combined variations from sin, cos, and tan functions. The advantage of the methods is that they are simple, easy to implement, and can fit around the engineering and optimization problems. The simulation analysis of over thirty-three different benchmark functions is carried out, followed by the experimental investigation of the real-time pressure process plant by comparing the performance with the conventional algorithm. Lastly, a summary of the chapter is provided.

5. Backmatter