DESIGN AND ANALYSIS OF BRAIN EMOTIONAL LEARNING BASED INTELLIGENT CONTROLLER (BELBIC) FOR TEMPERATURE CONTROL

This report presents the project undertaken to design and analyze the performance of temperature control using brain emotional learning control approach. In recent years, theory and applications of intelligent control systems have been a focus in control engineering. Among intelligent control approaches are Artificial Neural Network, Fuzzy Control and Genetic Algorithm

Approaches based on LAMDA control applied to regulate HVAC systems for buildings

The control of HVAC (Heating Ventilation and Air Conditioning) systems is usually complex because its modeling in certain cases is difficult, since these systems have a large number of components. Heat exchangers, chillers, valves, sensors, and actuators, increase the non-linear characteristics of the complete model, so it is necessary to propose new control strategies that can handle the uncertainty generated by all these elements working together. On the other hand, artificial intelligence is a powerful tool that allows improving the performance of control systems with inexact models and uncertainties. This paper presents new control alternatives for HVAC systems based on LAMDA (Learning Algorithm for Multivariable Data Analysis). This algorithm has been used in the field of machine learning, however, we have taken advantage of its learning characteristics to propose different types of intelligent controllers to improve the performance of the overall control system in tasks of regulation and reference change. In order to perform a comparative analysis in the context of HVAC systems, conventional methods such as PID and Fuzzy-PID are compared with LAMDA-PID, LAMDA-Sliding Mode Control based on Z-numbers (ZLSMC), and Adaptive LAMDA. Specifically, two HVAC systems are implemented by simulations to evaluate the proposals: an MIMO (Multiple-input Multiple-output) HVAC system and an HVAC system with dead time, which are used to compare the results qualitatively and quantitatively. The results show that ZLSMC is the most robust controller, which efficiently controls HVAC systems in cases of reference changes and the presence of disturbances.European CommissionAgencia Estatal de InvestigaciónJunta de Comunidades de Castilla-La Manch

Real Time Emotional Control for Anti-Swing and Positioning Control of SIMO Overhead Traveling Crane

Jamali MR, Arami A, Hosseini B, Moshiri B, Lukas C. Real Time Emotional Control for Anti-Swing and Positioning Control of SIMO Overhead Traveling Crane. International Journal of Innovative Computing, Information, and Control. 2008;4(9):2333-2344

An Efficient Optimal Fractional Emotional Intelligent Controller for an AVR System in Power Systems

In this paper, a high-performance optimal fractional emotional intelligent controller for an Automatic Voltage Regulator (AVR) in power system using Cuckoo optimization algorithm (COA) is proposed. AVR is the main controller within the excitation system that preserves the terminal voltage of a synchronous generator at a specified level. The proposed control strategy is based on brain emotional learning, which is a self-tuning controller so-called brain emotional learning based intelligent controller (BELBIC) and is based on sensory inputs and emotional cues. The major contribution of the paper is that to use the merits of fractional order PID (FOPID) controllers, a FOPID controller is employed to formulate stimulant input (SI) signal. This is a distinct advantage over published papers in the literature that a PID controller used to generate SI. Furthermore, another remarkable feature of the proposed approach is that it is a model-free controller. The proposed control strategy can be a promising controller in terms of simplicity of design, ease of implementation and less time-consuming. In addition, in order to enhance the performance of the proposed controller, its parameters are tuned by COA. In order to design BELBIC controller for AVR system a multi-objective optimization problem including overshoot, settling time, rise time and steady-state error is formulated. Simulation studies confirm that the proposed controller compared to classical PID and FOPID controllers introduced in the literature shows superior performance regarding model uncertainties. Having applied the proposed controller, the rise time and settling time are improved 47% and 57%, respectively

A two-stage probability based, conservatism reduction methodology for traditional Minimax robust control system design

A two-stage, probability-based controller design methodology is proposed to reduce the conservatism from traditional robust minimax controller design method, by relaxing the norm-bounded parameter uncertainty constraint and incorporating uncertain parameters' probabilistic information.Ph.D

Advanced control strategies for optimal operation of a combined solar and heat pump system

The UK domestic sector accounts for more than a quarter of total energy use. This energy use can be reduced through more efficient building operations. The energy efficiency can be improved through better control of heating in houses, which account for a large portion of total energy consumption. The energy consumption can be lowered by using renewable energy systems, which will also help the UK government to meet its targets towards reduction in carbon emissions and generation of clean energy. Building control has gained considerable interest from researchers and much improved ways of control strategies for heating and hot water systems have been investigated. This intensified research is because heating systems represent a significant share of our primary energy consumption to meet thermal comfort and indoor air quality criteria. Advances in computing control and research in advanced control theory have made it possible to implement advanced controllers in building control applications. Heating control system is a difficult problem because of the non-linearities in the system and the wide range of operating conditions under which the system must function. A model of a two zone building was developed in this research to assess the performance of different control strategies. Two conventional (On-Off and proportional integral controllers) and one advanced control strategies (model predictive controller) were applied to a solar heating system combined with a heat pump. The building was modelled by using a lumped approach and different methods were deployed to obtain a suitable model for an air source heat pump. The control objectives were to reduce electricity costs by optimizing the operation of the heat pump, integrating the available solar energy, shifting electricity consumption to the cheaper night-time tariff and providing better thermal comfort to the occupants. Different climatic conditions were simulated to test the mentioned controllers. Both on-off and PI controllers were able to maintain the tank and room temperatures to the desired set-point temperatures however they did not make use of night-time electricity. PI controller and Model Predictive Controller (MPC) based on thermal comfort are developed in this thesis. Predicted mean vote (PMV) was used for controlling purposes and it was modelled by using room air and radiant temperatures as the varying parameters while assuming other parameters as constants. The MPC dealt well with the disturbances and occupancy patterns. Heat energy was also stored into the fabric by using lower night-time electricity tariffs. This research also investigated the issue of model mismatch and its effect on the prediction results of MPC. MPC performed well when there was no mismatch in the MPC model and simulation model but it struggled when there was a mismatch. A genetic algorithm (GA) known as a non-dominated sorting genetic algorithm (NSGA II) was used to solve two different objective functions, and the mixed objective from the application domain led to slightly superior results. Overall results showed that the MPC performed best by providing better thermal comfort, consuming less electric energy and making better use of cheap night-time electricity by load shifting and storing heat energy in the heating tank. The energy cost was reduced after using the model predictive controller

Sviluppo di un metodo innovativo per la misura del comfort termico attraverso il monitoraggio di parametri fisiologici e ambientali in ambienti indoor

openLa misura del comfort termico in ambienti indoor è un argomento di interesse per la comunità scientifica, poiché il comfort termico incide profondamente sul benessere degli utenti ed inoltre, per garantire condizioni di comfort ottimali, gli edifici devono affrontare costi energetici elevati. Anche se esistono norme nel campo dell'ergonomia del comfort che forniscono linee guida per la valutazione del comfort termico, può succedere che in contesti reali sia molto difficile ottenere una misurazione accurata. Pertanto, per migliorare la misura del comfort termico negli edifici, la ricerca si sta concentrando sulla valutazione dei parametri personali e fisiologici legati al comfort termico, per creare ambienti su misura per l’utente. Questa tesi presenta diversi contributi riguardo questo argomento. Infatti, in questo lavoro di ricerca, sono stati implementati una serie di studi per sviluppare e testare procedure di misurazione in grado di valutare quantitativamente il comfort termico umano, tramite parametri ambientali e fisiologici, per catturare le peculiarità che esistono tra i diversi utenti. In primo luogo, è stato condotto uno studio in una camera climatica controllata, con un set di sensori invasivi utilizzati per la misurazione dei parametri fisiologici. L'esito di questa ricerca è stato utile per ottenere una prima accuratezza nella misurazione del comfort termico dell'82%, ottenuta mediante algoritmi di machine learning (ML) che forniscono la sensazione termica (TSV) utilizzando la variabilità della frequenza cardiaca (HRV) , parametro che la letteratura ha spesso riportato legato sia al comfort termico dell'utenza che alle grandezze ambientali. Questa ricerca ha dato origine a uno studio successivo in cui la valutazione del comfort termico è stata effettuata utilizzando uno smartwatch minimamente invasivo per la raccolta dell’HRV. Questo secondo studio consisteva nel variare le condizioni ambientali di una stanza semi-controllata, mentre i partecipanti potevano svolgere attività di ufficio ma in modo limitato, ovvero evitando il più possibile i movimenti della mano su cui era indossato lo smartwatch. Con questa configurazione, è stato possibile stabilire che l'uso di algoritmi di intelligenza artificiale (AI) e il set di dati eterogeneo creato aggregando parametri ambientali e fisiologici, può fornire una misura di TSV con un errore medio assoluto (MAE) di 1.2 e un errore percentuale medio assoluto (MAPE) del 20%. Inoltre, tramite il Metodo Monte Carlo (MCM) è stato possibile calcolare l'impatto delle grandezze in ingresso sul calcolo del TSV. L'incertezza più alta è stata raggiunta a causa dell'incertezza nella misura della temperatura dell'aria (U = 14%) e dell'umidità relativa (U = 10,5%). L'ultimo contributo rilevante ottenuto con questa ricerca riguarda la misura del comfort termico in ambiente reale, semi controllato, in cui il partecipante non è stato costretto a limitare i propri movimenti. La temperatura della pelle è stata inclusa nel set-up sperimentale, per migliorare la misurazione del TSV. I risultati hanno mostrato che l'inclusione della temperatura della pelle per la creazione di modelli personalizzati, realizzati utilizzando i dati provenienti dal singolo partecipante, porta a risultati soddisfacenti (MAE = 0,001±0,0003 e MAPE = 0,02%±0,09%). L'approccio più generalizzato, invece, che consiste nell'addestrare gli algoritmi sull'intero gruppo di partecipanti tranne uno, e utilizzare quello tralasciato per il test, fornisce prestazioni leggermente inferiori (MAE = 1±0.2 e MAPE = 25% ±6%). Questo risultato evidenzia come in condizioni semi-controllate, la previsione di TSV utilizzando la temperatura della pelle e l'HRV possa essere eseguita con un certo grado di incertezza.Measuring human thermal comfort in indoor environments is a topic of interest in the scientific community, since thermal comfort deeply affects the well-being of occupants and furthermore, to guarantee optimal comfort conditions, buildings must face high energy costs. Even if there are standards in the field of the ergonomics of the thermal environment that provide guidelines for thermal comfort assessment, it can happen that in real-world settings it is very difficult to obtain an accurate measurement. Therefore, to improve the measurement of thermal comfort of occupants in buildings, research is focusing on the assessment of personal and physiological parameters related to thermal comfort, to create environments carefully tailored to the occupant that lives in it. This thesis presents several contributions to this topic. In fact, in the following research work, a set of studies were implemented to develop and test measurement procedures capable of quantitatively assessing human thermal comfort, by means of environmental and physiological parameters, to capture peculiarities among different occupants. Firstly, it was conducted a study in a controlled climatic chamber with an invasive set of sensors used for measuring physiological parameters. The outcome of this research was helpful to achieve a first accuracy in the measurement of thermal comfort of 82%, obtained by training machine learning (ML) algorithms that provide the thermal sensation vote (TSV) by means of environmental quantities and heart rate variability (HRV), a parameter that literature has often reported being related to both users' thermal comfort. This research gives rise to a subsequent study in which thermal comfort assessment was made by using a minimally invasive smartwatch for collecting HRV. This second study consisted in varying the environmental conditions of a semi-controlled test-room, while participants could carry out light-office activities but in a limited way, i.e. avoiding the movements of the hand on which the smartwatch was worn as much as possible. With this experimental setup, it was possible to establish that the use of artificial intelligence (AI) algorithms (such as random forest or convolutional neural networks) and the heterogeneous dataset created by aggregating environmental and physiological parameters, can provide a measure of TSV with a mean absolute error (MAE) of 1.2 and a mean absolute percentage error (MAPE) of 20%. In addition, by using of Monte Carlo Method (MCM), it was possible to compute the impact of the uncertainty of the input quantities on the computation of the TSV. The highest uncertainty was reached due to the air temperature uncertainty (U = 14%) and relative humidity (U = 10.5%). The last relevant contribution obtained with this research work concerns the measurement of thermal comfort in a real-life setting, semi-controlled environment, in which the participant was not forced to limit its movements. Skin temperature was included in the experimental set-up, to improve the measurement of TSV. The results showed that the inclusion of skin temperature for the creation of personalized models, made by using data coming from the single participant brings satisfactory results (MAE = 0.001±0.0003 and MAPE = 0.02%±0.09%). On the other hand, the more generalized approach, which consists in training the algorithms on the whole bunch of participants except one, and using the one left out for the test, provides slightly lower performances (MAE = 1±0.2 and MAPE = 25%±6%). This result highlights how in semi-controlled conditions, the prediction of TSV using skin temperature and HRV can be performed with acceptable accuracy.INGEGNERIA INDUSTRIALEembargoed_20220321Morresi, Nicol

Controle de nível de tanques interativos baseados em técnicas de redes neurais artificiais

Orientador: Ana Maria Frattini FiletiDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia QuímicaResumo: O controle de nível de tanques interativos a partir da vazão é um sistema MIMO (multiple input multiple output), que envolve uma série de desafios como não linearidades acentuadas, interação entre as variáveis do processo e tempos mortos e, por isso, nem sempre pode ser controlado por técnicas de controle convencionais como o PID. Rede neurais artificiais (RNA) são uma técnica de processamento paralelo capaz de capturar relações bastante não lineares entre várias variáveis de entradas e várias variáveis de saídas. Dessa forma, diversas técnicas de controle utilizando RNA tem sido propostas para processos em que o controle feedback tradicional possa não funcionar satisfatoriamente. O presente trabalho visava testar a viabilidade experimental de duas técnicas de controle baseadas em redes neurais aplicadas no controle de nível em tanques interativos: o controle preditivo baseado em redes neurais (MPC-RNA), que consiste em utilizar um modelo neural do processo e um algoritmo de otimização para obter uma performance satisfatória; e o controle neural inverso, que é uma técnica de controle baseada na predição da variável manipulada diretamente das variáveis controladas. Além disso, o trabalho também visava comparar a performance das duas técnicas mencionadas com a performance do controlador PID convencional. Os experimentos foram realizados no sistema de tanques interativos do Laboratório de controle e automação (LCAP) na Unicamp. Ambos os níveis dos tanques acoplados eram controlados a partir da manipulação das potências das duas bombas que regulavam as vazões. Uma válvula intermediária manual conectava os tanques e gerava não linearidades, bem como interação entre os níveis, o que dificultava o controle PID. A aplicação experimental das três técnicas mencionadas foi feita por meio de um programa desenvolvido em MATLAB® e um CLP foi utilizado para fazer a aquisição dos dados da planta. Uma comparação entre as duas técnicas de controle baseadas em redes neurais mostrou que o controle neural inverso não foi capaz de seguir o setpoint satisfatoriamente, já que a técnica deixou um offset. Enquanto isso, a técnica MPC-RNA foi capaz de seguir o setpoint mais rapidamente e com menores overshoots do que o PID. A performance melhor do MPC-RNA em relação ao PID pode ser atribuída a capacidade do algoritmo de controle preditivo de minimizar os desvios entre a saída desejada e predita, e a habilidade das redes neurais artificiais de lidar com não linearidades e interação entre variáveis manipuladas e controladas. Além disso, o controlador MPC-RNA acopla a estratégia feedback e feedforward, dessa forma, compensando desvios entre o valor real e o valor predito a partir do modelo distúrbioAbstract: The level control of interactive tanks adjusting flow rates is a multiple input multiple output (MIMO) system that poses many challenges in the control problem, such as nonlinearities, interactions between manipulated and process variables and dead times. Therefore, conventional techniques such as the Proportional Integral Derivative (PID) controller might not work properly in this process. Artificial neural network (ANN) is a parallel processing technique that can capture highly nonlinear relationships among input and output variables. Hence, some control techniques that use ANN have been proposed for processes in which traditional feedback techniques may not work properly. This work aimed to test the experimental feasibility of two control techniques based on artificial neural networks applied to level control in coupled tanks: the model predictive control based on neural modeling (MPC-ANN) and an inverse neural network control. In the first strategy, an artificial neural network model of the process and an optimization algorithm are used to derive a satisfactory error performance. The second one is a control technique based on predicting the manipulated variables straight from the measurements of the process variables. Moreover, this work aimed to compare the performance of the two techniques mentioned with the conventional PID. The experiments were carried out using interactive tanks set up in of the Laboratory of Control and Automation at the University of Campinas (UNICAMP). Both levels of coupled tanks were to be controlled by manipulating the power of the two pumps that regulates output flow rates. An intermediate manual valve connected the tanks, generating nonlinearities and interaction between the levels, which made the success of PID control more difficult. The experimental application of the three mentioned techniques was performed with algorithm developed in MATLAB® and using a PLC to acquire the plant data. The comparison between the two-control neural network control techniques showed that the inverse neural control was not capable to track the set-point satisfactorily since it left an offset while the MPC-ANN was capable to track the set-point faster than the PID and it left smaller overshoots than the PID. The MPC-ANN performed better than the PID due to the capacity of model predictive control algorithm to minimize the deviations between the desired and predicted outputs, and the ability of artificial neural networks to deal with nonlinearities and interactions between manipulated and controlled variables. Besides, MPC-ANN couples feedback and feedforward strategy so it compensates model plant mismatches with the disturbance modelMestradoEngenharia QuímicaMestre em Engenharia Química1776016CAPE

Towards a Sustainable Life: Smart and Green Design in Buildings and Community

This Special Issue includes contributions about occupants’ sustainable living in buildings and communities, highlighting issues surrounding the sustainable development of our environments and lives by emphasizing smart and green design perspectives. This Special Issue specifically focuses on research and case studies that develop promising methods for the sustainable development of our environment and identify factors critical to the application of a sustainable paradigm for quality of life from a user-oriented perspective. After a rigorous review of the submissions by experts, fourteen articles concerning sustainable living and development are published in this Special Issue, written by authors sharing their expertise and approaches to the concept and application of sustainability in their fields. The fourteen contributions to this special issue can be categorized into four groups, depending on the issues that they address. All the proposed methods, models, and applications in these studies contribute to the current understanding of the adoption of the sustainability paradigm and are likely to inspire further research addressing the challenges of constructing sustainable buildings and communities resulting in a sustainable life for all of society

Control and Management Strategy of Autonomous Vehicle Functions

In this research, an autonomous vehicle function management methodology is studied. In accordance with the traffic situation, the decision making level chooses the optimal function that guarantees safety and minimizes fuel consumption while the control level is implemented via neuromorphic strategy based on the brain limbic system. To realize the decision making strategy, the Analytic Hierarchy Process (AHP) is used by considering driving safety, driving speed, and fuel efficiency as the objectives. According to the traffic situation and predefined driving mode, Lane Change Maneuver (LCM) and Adaptive Cruise Control (ACC) are chosen as the alternative functions in the AHP framework. The adaptive AHP is utilized to cope with dynamically changing traffic environment. The proposed adaptive AHP algorithm provides an optimal relative importance matrix that is essential to make decisions under a varying traffic situation and driving modes. The simulation results show that proposed autonomous vehicle function management structure produces optimal decisions that satisfy the driving preference. The stability of BLS based control is also investigated via Cell-to-Cell Mapping. In this research, autonomous vehicle functions such as Lane change maneuver and Adaptive cruise control are developed by means of BLS based control. The simulation results considered various traffic situations that an autonomous vehicle can encounter. To demonstrate the suggested control method Cell-to-Cell Mapping is utilized. Subsequently, the autonomous vehicle function management strategy is developed by Applying AHP and an adaptive AHP strategy is developed to cope with various traffic situations and driving modes. The suggested method is verified numerical simulations