Optimizing Three-Tank Liquid Level Control: Insights from Prairie Dog Optimization

The management of chemical process liquid levels poses a significant challenge in industrial process control, affecting the efficiency and stability of various sectors such as food processing, nuclear power generation, and pharmaceutical industries. While Proportional-Integral-Derivative (PID) control is a widely-used technique for maintaining liquid levels in tanks, its efficacy in optimizing complex and nonlinear systems has limitations. To overcome this, researchers are exploring the potential of metaheuristic algorithms, which offer robust optimization capabilities. This study introduces a novel approach to liquid level control using the Prairie Dog Optimization (PDO) algorithm, a metaheuristic algorithm inspired by prairie dog behavior. The primary objective is to design and implement a PID-controlled three-tank liquid level system that leverages PDO to regulate liquid levels effectively, ensuring enhanced stability and performance. The performance of the proposed system is evaluated using the ZLG criterion, a time domain metric-based objective function that quantifies the system's efficiency in maintaining desired liquid levels. Several analysis techniques are employed to understand the behavior of the system. Convergence curve analysis assesses the PDO-controlled system's convergence characteristics, providing insights into its efficiency and stability. Statistical analysis determines the algorithm's reliability and robustness across multiple runs. Stability analysis from both time and frequency response perspectives further validates the system's performance. A comprehensive comparison study with state-of-the-art metaheuristic algorithms, including AOA-HHO, CMA-ES, PSO, and ALC-PSODE, is conducted to benchmark the performance of PDO. The results highlight PDO's superior convergence, stability, and optimization capabilities, establishing its efficacy in real-world industrial applications. The research findings underscore the potential of PDO in PID control applications for three-tank liquid level systems. By outperforming benchmark algorithms, PDO demonstrates its value in industrial control scenarios, contributing to the advancement of metaheuristic-based control techniques and process optimization. This study opens avenues for engineers and practitioners to harness advanced control solutions, thereby enhancing industrial processes and automation

Fuzzy-pso Control Of Linear And Nonlinear Systems

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2014Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2014Bu tezin amacı, yeni optimizasyon yöntemi olan parçacık sürü optimizasyon algoritmasını MATLAB’e uygulayarak bulanık PID kontrolörü katsayıları ve Takagi-Sugeno kural tabanındaki keskin değerleri cevrimdışı optimize ederek doğrusal ve doğrusal olmayan sistemlerin belirli çalışma koşulları altında kontrolünü sağlamaktır. Parçacık sürü optimizasyonunun diğer optimizasyon yöntemlerinden, örnek olarak verilmesi gerekirse genetik algoritmadan, en önemli avantajı optimizasyon sırasında az sayıda iterasyon içermesi, kolay anlaşılabilir olması ve bize kompleks olmayan az sayıda yazılmış bilgisayar kodları ile kolay ve ucuz bir şekilde uğraşmamızı sağlamasıdır. Genetik algoritma ile olan benzerlikleri ise her ikiside populasyon tabanlı olup, tek set değerden diğer set değerlere geçerken deterministik ve olası kuralları kullanmaları sayılabilir. Son yapılan çalışmalara istinaden parçacık sürü optimizasyon yöntemi en az genetik algoritma kadar büyük oranda doğrusal olmayan yapıların çözülmesinde, yakınsama oranı ve yakınsama hassasiyeti bazında aynı sonuçları vermektedir. Ayrıca basit kodlar içermesinden dolayı hem bilgisayar hafızasından hem de zamandan tasarruf ettirip sonuclara en hızlı ve verimli şekilde ulaşmamıza yardımcı olmaktadır. Parçacık sürü optimizasyon yöntemi doğrusal olmayan ve zamanla değişen karakteristiğe sahip olan ikili tank sisteminde belirli çalışma aralıkları içerisinde bulanık PID kontrolör tasarımında kolayca ve başarılı bir şekilde uygulanabilmiştir. Yukarıda bahsedildiği gibi ikili tank sisteminin doğrusal olmayan ve zamanla değişen yapısından dolayı, kontrolör tasarımında tek set parametrelerin bulunması ve kontrol sırasında her bölge için aynı parametrelerin kullanılması neredeyse imkansızdır. Bu yüzden daha önceden belirlenmiş çalışma aralıkları içerisinde, Takagi-Sugeno kural tabanındaki parçacık sürü optimizasyon yöntemi ile optimize edilmiş katsayılar her bölge için sabit tutularak, değişik bölgeler için değişik optimal kontol parametreleri bulunup kontrol sırasında çevrimiçi olarak PID katsayılar hesaplanmıştır. Bulanık PID kontrolör parametreleri aynı zamanda ikili tank sisteminin ikinci tankındaki sıvı seviyesini giriş set değeri alarak farklı çalışma aralıklarında doğrusal regresyon yöntemi ile bulunan değişik kontrolör parametre fonksiyonları ile esnek bir yapıya dönüştürülüp farklı giriş değerleri, sistem gürültülerini hatta sistem hatalarını kompanze edecek duruma getirilimiştir. Böylelikle belirlenen çalışma bölgelerinde istenilen kontrol şartlarını sağlayan, değişik senaryolara sahip sistem hataları ve sistem gürültülerini bastıran adaptif yapıya sahip doğrusal olmayan bir sistemin geliştirilmiş parçacık sürü optimizasyonu yöntemi ve bulanık PID kontrolörü ile kontrolü sağlanmıştır.The goal of the thesis is to introduce a new global optimization method called particle swarm optimization that is implemented via MATLAB to use to find the optimal parameters for PID coefficients and Takagi-Sugeno rule base’s crisp values in order to control linear and nonlinear systems within specified operating conditions. The most important advantages of particle swarm optimization algorithm is that it requires less number of iterations and it enables us to deal with a few lines of computer codes in a cheapest manner rather than other optimization methods such as genetic algorithm. It requires only primitive mathematical operators in terms of both necessity of more available memory and speed. Particle swarm optimization method has been successfully applied to the design of coupled tanks system control with meaningful time domain criteria. Since the coupled tank system to be controlled is nonlinear and time varying charecteristic, it is almost not possible to find one set of parameters that satisfy for all operating conditions. Therefore some predetermined operating points have been chosen and find out the optimal control parameters’ values for the operating points while keeping Takagi-Sugeno crisps values constant for all operating points within the different ranges. Different functions are calculated for each controller parameters within different operating points based on the referenced height of tank two as an input value to the coupled tank system by using the predetermined points and least curve-fitting algorithm. It has been observed that these functions, which derive fuzzy controller parameters, have achieved very satisfactorly systems responses.The water levels between different ranges are chosen respectively as a three typical operating regions of second tank and input space is divided into three fuzzy subspaces based on operating regions. Fuzzy PID parameters have been calculated online by proposed method despite of the fact that Takagi Sugeno crisp values have been calculated offline and stored before calculating PID parameters for the three operating regions. We can generalize that Takagi-Sugeno crisp values, which are structural parameters, are determined offline design while the tuning parameters are calculated during online adjustment of fuzzy PID controller to enhance the process performance, as well as to accommodate the adaptive capability to system uncertainty and process disturbances. The proposed architecture is also tested in case of process disturbance and systems faults. Simulation results showed that the couple tank system was successfully controlled with acceptable performance criterions in both cases.Yüksek LisansM.Sc

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

NON-LINEAR MODEL PREDICTIVE CONTROL STRATEGIES FOR PROCESS PLANTS USING SOFT COMPUTING APPROACHES

The developments of advanced non-linear control strategies have attracted a considerable research interests over the past decades especially in process control. Rather than an absolute reliance on mathematical models of process plants which often brings discrepancies especially owing to design errors and equipment degradation, non-linear models are however required because they provide improved prediction capabilities but they are very difficult to derive. In addition, the derivation of the global optimal solution gets more difficult especially when multivariable and non-linear systems are involved. Hence, this research investigates soft computing techniques for the implementation of a novel real time constrained non-linear model predictive controller (NMPC). The time-frequency localisation characteristics of wavelet neural network (WNN) were utilised for the non-linear models design using system identification approach from experimental data and improve upon the conventional artificial neural network (ANN) which is prone to low convergence rate and the difficulties in locating the global minimum point during training process. Salient features of particle swarm optimisation and a genetic algorithm (GA) were combined to optimise the network weights. Real time optimisation occurring at every sampling instant is achieved using a GA to deliver results both in simulations and real time implementation on coupled tank systems with further extension to a complex quadruple tank process in simulations. The results show the superiority of the novel WNN-NMPC approach in terms of the average controller energy and mean squared error over the conventional ANN-NMPC strategies and PID control strategy for both SISO and MIMO systemsPetroleum Training Development Fun

HOME ENERGY MANAGEMENT SYSTEM FOR DEMAND RESPONSE PURPOSES

The growing demand for electricity has led to increasing efforts to generate and satisfy the rising demand. This led to suppliers attempting to reduce consumption with the help of the users. Requests to shift unnecessary loads off the peak hours, using other sources of generators to supply the grid while offering incentives to the users have made a significant effect. Furthermore, automated solutions were implemented with the help of Home Energy Management Systems (HEMS) where the user can remotely manage household loads to reduce consumption or cost. Demand Response (DR) is the process of reducing power consumption in a response to demand signals generated by the utility based on many factors such as the Time of Use (ToU) prices. Automated HEMS use load scheduling techniques to control house appliances in response to DR signals. Scheduling can be purely user-dependent or fully automated with minimum effort from the user. This thesis presents a HEMS which automatically schedules appliances around the house to reduce the cost to the minimum. The main contributions in this thesis are the house controller model which models a variety of thermal loads in addition to two shiftable loads, and the optimizer which schedules the loads to reduce the cost depending on the DR signals. The controllers focus on the thermal loads since they have the biggest effect on the electricity bill, they also consider many factors ignored in similar models such as the physical properties of the room/medium, the outer temperatures, the comfort levels of the users, and the occupancy of the house during scheduling. The DR signal was the hourly electricity price; normally higher during the peak hours. Another main part of the thesis was studying multiple optimization algorithms and utilizing them to get the optimum scheduling. Results showed a maximum of 44% cost reduction using different metaheuristic optimization algorithms and different price and occupancy schemes

Multi-objective Optimization of Multi-loop Control Systems

Cascade Control systems are composed of inner and outer control loops. Compared to the traditional single feedback controls, the structure of cascade controls is more complex. As a result, the implementation of these control methods is costly because extra sensors are needed to measure the inner process states. On the other side, cascade control algorithms can significantly improve the controlled system performance if they are designed properly. For instance, cascade control strategies can act faster than single feedback methods to prevent undesired disturbances, which can drive the controlled system’s output away from its target value, from spreading through the process. As a result, cascade control techniques have received much attention recently. In this thesis, we present a multi-objective optimal design of linear cascade control systems using a multi-objective algorithm called the non-dominated sorting genetic algorithm (NSGA-II), which is one of the widely used algorithms in solving multi-objective optimization problems (MOPs). Two case studies have been considered. In the first case, a multi-objective optimal design of a cascade control system for an underactuated mechanical system consisting of a rotary servo motor, and a ball and beam is introduced. The setup parameters of the inner and outer control loops are tuned by the NSGA-II to achieve four objectives: 1) the closed-loop system should be robust against inevitable internal and outer disturbances, 2) the controlled system is insensitive to inescapable measurement noise affecting the feedback sensors, 3) the control signal driving the mechanical system is optimum, and 4) the dynamics of the inner closed-loop system has to be faster than that of the outer feedback system. By using the NSGAII algorithm, four design parameters and four conflicting objective functions are obtained. The second case study investigates a multi-objective optimal design of an aeroelastic cascade controller applied to an aircraft wing with a leading and trailing control surface. The dynamics of the actuators driving the control surfaces are considered in the design. Similarly, the NSGA-II is used to optimally adjust the parameters of the control algorithm. Ten design parameters and three conflicting objectives are considered in the design: the controlled system’s tracking error to an external gust load should be minimal, the actuators should be driven by minimum energy, and the dynamics of the closed-loop comprising the actuators and inner control algorithm should be faster than that of the aeroelastic structure and the outer control loop. Computer simulations show that the presented case studies may become the basis for multi-objective optimal design of multi-loop control systems

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

A Model-Based Framework for the Smart Manufacturing of Polymers

It is hard to point a daily activity in which polymeric materials or plastics are not involved. The synthesis of polymers occurs by reacting small molecules together to form, under certain conditions, long molecules. In polymer synthesis, it is mandatory to assure uniformity between batches, high-quality of end-products, efficiency, minimum environmental impact, and safety. It remains as a major challenge the establishment of operational conditions capable of achieving all objectives together. In this dissertation, different model-centric strategies are combined, assessed, and tested for two polymerization systems. The first system is the synthesis of polyacrylamide in aqueous solution using potassium persulfate as initiator in a semi-batch reactor. In this system, the proposed framework integrates nonlinear modelling, dynamic optimization, advanced control, and nonlinear state estimation. The objectives include the achievement of desired polymer characteristics through feedback control and a complete motoring during the reaction. The estimated properties are close to experimental values, and there is a visible noise reduction. A 42% improvement of set point accomplishment in average is observed when comparing feedback control combined with a hybrid discrete-time extended Kalman filter (h-DEKF) and feedback control only. The 4-state geometric observer (GO) with passive structure, another state estimation strategy, shows the best performance. Besides achieving smooth signal processing, the observer improves 52% the estimation of the final molecular weight distribution when compared with the h-DEKF. The second system corresponds to the copolymerization of ethylene with 1,9-decadiene using a metallocene catalyst in a semi-batch reactor. The evaluated operating conditions consider different diene concentrations and reaction temperatures. Initially, the nonlinear model is validated followed by a global sensitivity analysis, which permits the selection of the important parameters. Afterwards, the most important kinetic parameters are estimated online using an extended Kalman filter (EKF), a variation of the GO that uses a preconditioner, and a data-driven strategy referred as the retrospective cost model refinement (RCMR) algorithm. The first two strategies improve the measured signal, but fail to predict other properties. The RCMR algorithm demonstrates an adequate estimation of the unknown parameters, and the estimates converge close to theoretical values without requiring prior knowledge

Influence of initialization on the performance of metaheuristic optimizers

All metaheuristic optimization algorithms require some initialization, and the initialization for such optimizers is usually carried out randomly. However, initialization can have some significant influence on the performance of such algorithms. This paper presents a systematic comparison of 22 different initialization methods on the convergence and accuracy of five optimizers: differential evolution (DE), particle swarm optimization (PSO), cuckoo search (CS), artificial bee colony (ABC) algorithm and genetic algorithm (GA). We have used 19 different test functions with different properties and modalities to compare the possible effects of initialization, population sizes and the numbers of iterations. Rigorous statistical ranking tests indicate that 43.37% of the functions using the DE algorithm show significant differences for different initialization methods, while 73.68% of the functions using both PSO and CS algorithms are significantly affected by different initialization methods. The simulations show that DE is less sensitive to initialization, while both PSO and CS are more sensitive to initialization. In addition, under the condition of the same maximum number of function evaluations (FEs), the population size can also have a strong effect. Particle swarm optimization usually requires a larger population, while the cuckoo search needs only a small population size. Differential evolution depends more heavily on the number of iterations, a relatively small population with more iterations can lead to better results. Furthermore, ABC is more sensitive to initialization, while such initialization has little effect on GA. Some probability distributions such as the beta distribution, exponential distribution and Rayleigh distribution can usually lead to better performance. The implications of this study and further research topics are also discussed in detail

Applications of Mathematical Models in Engineering

The most influential research topic in the twenty-first century seems to be mathematics, as it generates innovation in a wide range of research fields. It supports all engineering fields, but also areas such as medicine, healthcare, business, etc. Therefore, the intention of this Special Issue is to deal with mathematical works related to engineering and multidisciplinary problems. Modern developments in theoretical and applied science have widely depended our knowledge of the derivatives and integrals of the fractional order appearing in engineering practices. Therefore, one goal of this Special Issue is to focus on recent achievements and future challenges in the theory and applications of fractional calculus in engineering sciences. The special issue included some original research articles that address significant issues and contribute towards the development of new concepts, methodologies, applications, trends and knowledge in mathematics. Potential topics include, but are not limited to, the following: Fractional mathematical models; Computational methods for the fractional PDEs in engineering; New mathematical approaches, innovations and challenges in biotechnologies and biomedicine; Applied mathematics; Engineering research based on advanced mathematical tools