Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving hard optimization problems. As their popularity has increased, applications of these algorithms have grown in more than equal measure. While many of the books available on these subjects only provide a cursory discussion of theory, the present book gives special emphasis to the theoretical background that is behind these algorithms and their applications. Moreover, this book introduces a novel real time control algorithm, that uses genetic algorithm and ant colony optimization algorithms for optimizing PID controller parameters. In general, the present book represents a solid survey on artificial neural networks, genetic algorithms and the ant colony optimization algorithm and introduces novel practical elements related to the application of these methods to process system control.

This book discusses in-depth role of optimization to optimize the controller parameters with reference to bio-inspired algorithms. Comparative studies to evaluate the performance of different optimization techniques in terms of the settling time, overshoot and undershoot responses of the frequency deviations, tie-line power flow deviations, and the area control error are included, supported by examples. The book also includes different scenarios of the load frequency controller for single area as well as multi-area thermal power generating unit considering different algorithms. Key Features: Highlights the importance of tuning the power system controller parameters with emphasis on bio-inspiration algorithms Provides some applied applications/examples of the thermal power system Focusses on power system applications based on the optimization algorithms with different single area and multi-area thermal power systems Reports different cases on the interconnected power systems with providing optimal performance by optimizing the controller’s parameters

Industrial PID Controller Tuning presents a different view of the servo/regulator compromise that has been studied for a long time in industrial control research. Optimal tuning generally involves comparison of cost functions (e.g., a quadratic function of the error or a time-weighted absolute value of the error) but without taking advantage of available multi-objective optimization methods. The book does make use of multi-objective optimization to account for several sources of disturbance, applying them to a more realistic problem: how to select the tuning of a controller when both servo and regulator responses are important. The authors review the different deterministic multi-objective optimization methods. In order to ameliorate the consequences of the computational expense typically involved in their use—specifically the generation of multiple solutions among which the control engineer still has to choose—algorithms for two-degree-of-freedom PID control are implemented in MATLAB®. MATLAB code and a MATLAB-compatible program are provided for download and will help readers to adapt the ideas presented in the text for use in their own systems. Further practical guidance is offered by the inclusion of several examples of common industrial processes amenable to the use of the authors’ methods. Researchers interested in non-heuristic approaches to controller tuning or in decision-making after a Pareto set has been established and graduate students interested in beginning a career working with PID control and/or industrial controller tuning will find this book a valuable reference and source of ideas. Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

With numerous new opportunities and challenges emerging from the topic of the cognition and control of complex systems, the methods related to PID control, or control based on a PID framework, will continue to grow and expand. This book covers some of the recent results that include improvements to the PID controller. Some examples of these improvements are as follows: •The novelty method of the variable, fractional-order PID controller •The optimization of PID controller, such as the hybrid LQR-PID controller by using genetic algorithm (GA) with the application for the control of helicopter systems •The optimized tuning approach of PID controller with disturbance rejection •A controller adjustment method based on the internal product of PID terms •The PI-PD controller, incorporated with the model-based feedforward control (FF) and the disturbance compensator (Kz), which is used for the control of magnetic levitation systems •The proper control with PID framework used to improve the cognition or identification for complex systems

The two-volume set (LNCS 6728 and 6729) constitutes the refereed proceedings of the International Conference on Swarm Intelligence, ICSI 2011, held in Chongqing, China, in June 2011. The 143 revised full papers presented were carefully reviewed and selected from 298 submissions. The papers are organized in topical sections on theoretical analysis of swarm intelligence algorithms, particle swarm optimization, applications of pso algorithms, ant colony optimization algorithms, bee colony algorithms, novel swarm-based optimization algorithms, artificial immune system, differential evolution, neural networks, genetic algorithms, evolutionary computation, fuzzy methods, and hybrid algorithms - for part I. Topics addressed in part II are such as multi-objective optimization algorithms, multi-robot, swarm-robot, and multi-agent systems, data mining methods, machine learning methods, feature selection algorithms, pattern recognition methods, intelligent control, other optimization algorithms and applications, data fusion and swarm intelligence, as well as fish school search - foundations and applications.

Evolutionary algorithms can be used to solve interesting problems for aeronautical and astronautical applications, and it is a must to review the fundamentals of the most common evolutionary algorithms being used for those applications. Genetic algorithms, particle swarm optimization, firefly algorithm, ant colony optimization, artificial bee colony optimization, and the cuckoo search algorithm are presented and discussed with an emphasis on astronautical applications. In summary, the genetic algorithm and its variants can be used for a large parameter space but is more efficient in global optimization using a smaller chromosome size such that the number of parameters being optimized simultaneously is less than 1000. It is found that PID controller parameters, nonlinear parameter identification, and trajectory optimization are applications ripe for the genetic algorithm. Ant colony optimization and artificial bee colony optimization are optimization routines more suited for combinatorics, such as with trajectory optimization, path planning, scheduling, and spacecraft load bearing. Particle swarm optimization, firefly algorithm, and cuckoo search algorithms are best suited for large parameter spaces due to the decrease in computation need and function calls when compared to the genetic algorithm family of optimizers. Key areas of investigation for these social evolution algorithms are in spacecraft trajectory planning and in parameter identification.

Biomimicry uses our scienti?c understanding of biological systems to exploit ideas from nature in order to construct some technology. In this book, we focus onhowtousebiomimicryof the functionaloperationofthe “hardwareandso- ware” of biological systems for the development of optimization algorithms and feedbackcontrolsystemsthatextendourcapabilitiestoimplementsophisticated levels of automation. The primary focus is not on the modeling, emulation, or analysis of some biological system. The focus is on using “bio-inspiration” to inject new ideas, techniques, and perspective into the engineering of complex automation systems. There are many biological processes that, at some level of abstraction, can berepresentedasoptimizationprocesses,manyofwhichhaveasa basicpurpose automatic control, decision making, or automation. For instance, at the level of everyday experience, we can view the actions of a human operator of some process (e. g. , the driver of a car) as being a series of the best choices he or she makes in trying to achieve some goal (staying on the road); emulation of this decision-making process amounts to modeling a type of biological optimization and decision-making process, and implementation of the resulting algorithm results in “human mimicry” for automation. There are clearer examples of - ological optimization processes that are used for control and automation when you consider nonhuman biological or behavioral processes, or the (internal) - ology of the human and not the resulting external behavioral characteristics (like driving a car). For instance, there are homeostasis processes where, for instance, temperature is regulated in the human body.

Considered one of the most innovative research directions, computational intelligence (CI) embraces techniques that use global search optimization, machine learning, approximate reasoning, and connectionist systems to develop efficient, robust, and easy-to-use solutions amidst multiple decision variables, complex constraints, and tumultuous environments. CI techniques involve a combination of learning, adaptation, and evolution used for intelligent applications. Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/ Simulink® explores the performance of CI in terms of knowledge representation, adaptability, optimality, and processing speed for different real-world optimization problems. Focusing on the practical implementation of CI techniques, this book: Discusses the role of CI paradigms in engineering applications such as unit commitment and economic load dispatch, harmonic reduction, load frequency control and automatic voltage regulation, job shop scheduling, multidepot vehicle routing, and digital image watermarking Explains the impact of CI on power systems, control systems, industrial automation, and image processing through the above-mentioned applications Shows how to apply CI algorithms to constraint-based optimization problems using MATLAB® m-files and Simulink® models Includes experimental analyses and results of test systems Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/ Simulink® provides a valuable reference for industry professionals and advanced undergraduate, postgraduate, and research students.

This book discusses in-depth role of optimization to optimize the controller parameters with reference to bio-inspired algorithms. Comparative studies to evaluate the performance of different optimization techniques in terms of the settling time, overshoot and undershoot responses of the frequency deviations, tie-line power flow deviations, and the area control error are included, supported by examples. The book also includes different scenarios of the load frequency controller for single area as well as multi-area thermal power generating unit considering different algorithms. Key Features: Highlights the importance of tuning the power system controller parameters with emphasis on bio-inspiration algorithms Provides some applied applications/examples of the thermal power system Focusses on power system applications based on the optimization algorithms with different single area and multi-area thermal power systems Reports different cases on the interconnected power systems with providing optimal performance by optimizing the controller’s parameters