Search results for: model reference adaptive control

Authors: K. Parandhama Gowd

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Space technologies have changed the way we live in the present day society and manage many aspects of our daily affairs through Remote sensing, Navigation & Communications. Further, defense and military usage of spacecraft has increased tremendously along with civilian purposes. The number of satellites deployed in space in Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and the Geostationary Orbit (GEO) has gone up. The dependency on remote sensing and operational capabilities are most invariably to be exploited more and more in future. Every country is acquiring spacecraft in one way or other for their daily needs, and spacecraft numbers are likely to increase significantly and create spacecraft traffic problems. The aim of this research paper is to propose innovative design concepts for adaptive spacecraft. The main idea here is to improve existing design methods of spacecraft design and development to further improve upon design considerations for futuristic adaptive spacecraft with inbuilt features for automatic adaptability and self-protection. In other words, the innovative design considerations proposed here are to have future spacecraft with self-organizing capabilities for orbital control and protection from anti-satellite weapons (ASAT). Here, an attempt is made to propose design and develop futuristic spacecraft for 2030 and beyond due to tremendous advancements in VVLSI, miniaturization, and nano antenna array technologies, including nano technologies are expected.

Keywords: satellites, low earth orbit (LEO), medium earth orbit (MEO), geostationary earth orbit (GEO), self-organizing control system, anti-satellite weapons (ASAT), orbital control, radar warning receiver, missile warning receiver, laser warning receiver, attitude and orbit control systems (AOCS), command and data handling (CDH)

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Authors: Ali Mausmi, Ahmed Abbou, Rachid El Akhrif

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This research paper aims to reduce the chattering phenomenon due to control by sliding mode control applied on a wind energy conversion system based on the doubly fed induction generator (DFIG). Our goal is to offset the effect of parametric uncertainties and come as close as possible to the dynamic response solicited by the control law in the ideal case and therefore force the active and reactive power generated by the DFIG to accurately follow the reference values which are provided to it. The simulation results using Matlab / Simulink demonstrate the efficiency and performance of the proposed technique while maintaining the simplicity of control by first order sliding mode.

Keywords: control of speed, correction of the equivalent command, induction generator, sliding mode

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Authors: Pornsiri Kaewpradit, Chalisa Pourneaw

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Temperature control improvement of a membrane reactor with exothermic and reversible esterification reaction is studied in this work. It is well known that a batch membrane reactor requires different control strategies from a continuous one due to the fact that it is operated dynamically. Due to the effect of the operating temperature, the suitable control scheme has to be designed based reliable predictive model to achieve a desired objective. In the study, the optimization framework has been preliminary formulated in order to determine an optimal temperature trajectory for maximizing a desired product. In model predictive control scheme, a set of predictive models have been initially developed corresponding to the possible operating points of the system. The multiple predictive control moves have been further calculated on-line using the developed models corresponding to current operating point. It is obviously seen in the simulation results that the temperature control has been improved compared to the performance obtained by the conventional predictive controller. Further robustness tests have also been investigated in this study.

Keywords: model predictive control, batch reactor, temperature control, membrane reactor

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Authors: Sohail Ahmed, Ke Xing

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This research theoretically explored the evolution mechanism of enterprise technological innovation capability system (ETICS) from the perspective of complex adaptive systems (CAS). This research proposed an analytical framework for ETICS, its concepts, and theory by integrating CAS methodology into the management of the technological innovation capability of enterprises and discusses how to use the principles of complexity to analyze the composition, evolution, and realization of the technological innovation capabilities in complex dynamic environments. This paper introduces the concept and interaction of multi-agent, the theoretical background of CAS, and summarizes the sources of technological innovation, the elements of each subject, and the main clusters of adaptive interactions and innovation activities. The concept of multi-agents is applied through the linkages of enterprises, research institutions, and government agencies with the leading enterprises in industrial settings. The study was exploratory and based on CAS theory. Theoretical model is built by considering technological and innovation literature from foundational to state of the art projects of technological enterprises. On this basis, the theoretical model is developed to measure the evolution mechanism of the enterprise's technological innovation capability system. This paper concludes that the main characteristics for evolution in technological systems are based on the enterprise’s research and development personnel, investments in technological processes, and innovation resources are responsible for the evolution of enterprise technological innovation performance. The research specifically enriched the application process of technological innovation in institutional networks related to enterprises.

Keywords: complex adaptive system, echo model, enterprise technological innovation capability system, research institutions, multi-agents

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Authors: Ibrahim Ozkan

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In this study, a cross-layer design which combines adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ) techniques for a cooperative wireless network is investigated analytically. Previous analyses of such systems in the literature are confined to the case where the fading channel is independent at each retransmission, which can be unrealistic unless the channel is varying very fast. On the other hand, temporal channel correlation can have a significant impact on the performance of HARQ systems. In this study, utilizing a Markov channel model which accounts for the temporal correlation, the performance of non-cooperative and cooperative networks are investigated in terms of packet loss rate and throughput metrics for Chase combining HARQ strategy.

Keywords: cooperative network, adaptive modulation and coding, hybrid ARQ, correlated fading

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Authors: Dimitar Karastoyanov, Todor Penchev

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The paper presents the research of a kind of autonomous mobile robots, intended for work and adaptive perception in unknown and unstructured environment. The objective are robots, dedicated for multi-sensory environment perception and exploration, like measurements and samples taking, discovering and putting a mark on the objects as well as environment interactions–transportation, carrying in and out of equipment and objects. At that ground classification of the different types mobile robots in accordance with the way of locomotion (wheel- or chain-driven, walking, etc.), used drive mechanisms, kind of sensors, end effectors, area of application, etc. is made. Modular system for the mechanical construction of the mobile robots is proposed. Special PLC on the base of AtMega128 processor for robot control is developed. Electronic modules for the wireless communication on the base of Jennic processor as well as the specific software are developed. The methods, means and algorithms for adaptive environment behaviour and tasks realization are examined. The methods of group control of mobile robots and for suspicious objects detecting and handling are discussed too.

Keywords: mobile robots, wireless communications, environment investigations, group control, suspicious objects

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Authors: Pornthep Rachnavy

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The purpose of this paper is twofold. First, we believe that there are a significant relationship between sculling angle and sculling style among adaptive rowing. Second, we introduce a methodology used for adaptive rowing, namely simulation, to identify effectiveness of adaptive rowing. For our study we simulate the arms only single scull of adaptive rowing. The method for rowing fastest under the 1000 meter was investigated by study sculling angle using the simulation modeling. A simulation model of a rowing system was developed using the Matlab software package base on equations of motion consist of many variation for moving the boat such as oars length, blade velocity and sculling style. The boat speed, power and energy consumption on the system were compute. This simulation modeling can predict the force acting on the boat. The optimum sculling angle was performing by computer simulation for compute the solution. Input to the model are sculling style of each rower and sculling angle. Outputs of the model are boat velocity at 1000 meter. The present study suggests that the optimum sculling angle exist depends on sculling styles. The optimum angle for blade entry and release with respect to the perpendicular through the pin of the first style is -57.00 and 22.0 degree. The optimum angle for blade entry and release with respect to the perpendicular through the pin of the second style is -57.00 and 22.0 degree. The optimum angle for blade entry and release with respect to the perpendicular through the pin of the third style is -51.57 and 28.65 degree. The optimum angle for blade entry and release with respect to the perpendicular through the pin of the fourth style is -45.84 and 34.38 degree. A theoretical simulation for rowing has been developed and presented. The results suggest that it may be advantageous for the rowers to select the sculling angles proper to sculling styles. The optimum sculling angles of the rower depends on the sculling styles made by each rower. The investigated of this paper can be concludes in three directions: 1;. There is the optimum sculling angle in arms only single scull of adaptive rowing. 2. The optimum sculling angles depend on the sculling styles. 3. Computer simulation of rowing can identify opportunities for improving rowing performance by utilizing the kinematic description of rowing. The freedom to explore alternatives in speed, thrust and timing with the computer simulation will provide the coach with a tool for systematic assessments of rowing technique In addition, the ability to use the computer to examine the very complex movements during rowing will help both the rower and the coach to conceptualize the components of movements that may have been previously unclear or even undefined.

Keywords: simulation, sculling, adaptive, rowing

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Authors: Majid Rezayi, Sh. Shahaboddin, HNM E. Mahmud, A. Yadollah, A. Saeid, A. Yatimah

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In this study, the adaptive neuro-fuzzy inference system (ANFIS) was applied to obtain the membrane composition model affecting the potential response of our reported polymeric PVC sensor for determining the titanium (III) ions. The performance statistics of the artificial neural network (ANN) and linear regression models for potential slope prediction of membrane composition of titanium (III) ion selective electrode were compared with ANFIS technique. The results show that the ANFIS model can be used as a practical tool for obtaining the Nerntian slope of the proposed sensor in this study.

Keywords: adaptive neuro fuzzy inference, PVC sensor, titanium (III) ions, Nerntian slope

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Authors: Xuezhang Hou

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In the present paper, a hybrid hyperbolic dynamic system formulated by partial differential equations with initial and boundary conditions is considered. First, the system is transformed to an abstract evolution system in an appropriate Hilbert space, and spectral analysis and semigroup generation of the system operator is discussed. Subsequently, a sliding model control problem is proposed and investigated, and an equivalent control method is introduced and applied to the system. Finally, a significant result that the state of the system can be approximated by an ideal sliding mode under control in any accuracy is derived and examined.

Keywords: hyperbolic dynamic system, sliding model control, semigroup of linear operators, partial differential equations

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Authors: Mohamed El Hachimi, Abdelhakim Ballouk, Ilyas Khelafa, Abdelaziz Mouhou

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Introduction: Type 1 diabetes occurs when beta cells are destroyed by the body's own immune system. Treatment of type 1 diabetes mellitus could be greatly improved by applying a closed-loop control strategy to insulin delivery, also known as an Artificial Pancreas (AP). Method: In this paper, we present a new formulation of the cost function for a Model Predictive Control (MPC) utilizing a technic which accelerates the speed of control of the AP and tackles the nonlinearity of the control problem via asymmetric objective functions. Finding: The finding of this work consists in a new Model Predictive Control algorithm that leads to good performances like decreasing the time of hyperglycaemia and avoiding hypoglycaemia. Conclusion: These performances are validated under in silico trials.

Keywords: artificial pancreas, control algorithm, biomedical control, MPC, objective function, nonlinearity

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Authors: Taiki Baba, Tomoaki Hashimoto

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The random dither quantization method enables us to achieve much better performance than the simple uniform quantization method for the design of quantized control systems. Motivated by this fact, the stochastic model predictive control method in which a performance index is minimized subject to probabilistic constraints imposed on the state variables of systems has been proposed for linear feedback control systems with random dither quantization. In other words, a method for solving optimal control problems subject to probabilistic state constraints for linear discrete-time control systems with random dither quantization has been already established. To our best knowledge, however, the feasibility of such a kind of optimal control problems has not yet been studied. Our objective in this paper is to investigate the feasibility of stochastic model predictive control problems for linear discrete-time control systems with random dither quantization. To this end, we provide the results of numerical simulations that verify the feasibility of stochastic model predictive control problems for linear discrete-time control systems with random dither quantization.

Keywords: model predictive control, stochastic systems, probabilistic constraints, random dither quantization

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Authors: Mubarak Alhajri

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This paper addresses certain inherent limitations of local priority hysteresis switching logic. Our main result establishes that under persistent excitation assumption, it is possible to relax constraints requiring strict positivity of local priority and hysteresis switching constants. Relaxing these constraints allows the adaptive system to reach optimality which implies the performance improvement. The unconstrained local priority hysteresis switching logic is examined and conditions for global convergence are derived.

Keywords: adaptive control, convergence, hysteresis constant, hysteresis switching

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Authors: Byoung-Hee Lee

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Wireless battery charger with adaptive rapid charging algorithm is proposed. The proposed wireless charger adopts voltage regulation technique to reduce the number of power conversion steps. Moreover, based on battery models, an adaptive rapid charging algorithm for Li-ion batteries is obtained. Rapid-charging performance with the proposed wireless battery charger and the proposed rapid charging algorithm has been experimentally verified to show more than 70% charging time reduction compared to conventional constant-current constant-voltage (CC-CV) charging without the degradation of battery lifetime.

Keywords: wireless, battery charger, adaptive, rapid-charging

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Authors: Yasith Nayana, Janani Manamperuma, Lalindi Amarasinghe, Sasanka Kodithuwakku

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Educational games are popular among current e-learning systems. The approach to education through interactive media is expected to motivate students and encourage participation and engagement. ‘Kalayathra’ is an adaptive, player centered e-learning 3D game. The game identifies the player’s personality and adapt the gaming environment according to the player’s preference. Our platform measures the student’s performance and support learning through player assessment. Player experience is a good measure of the level of fun and education presented to players. To assess the level of playability we introduce an educational playability model. ‘Kalayathra’ is developed according to the GCE O/L syllabus and teaching guide in Sri Lankan education system. The game is capable of guiding players into the environment and aid them in tasks and activities depending on how much the player requires help.

Keywords: e-learning, games, adaptive, personality, gamification, player experience

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Authors: Fikret Yalcinkaya, Hamza Unsal

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To understand how neurons work, it is required to combine experimental studies on neural science with numerical simulations of neuron models in a computer environment. In this regard, the simplicity and applicability of spiking neuron modeling functions have been of great interest in computational neuron science and numerical neuroscience in recent years. Spiking neuron models can be classified by exhibiting various neuronal behaviors, such as spiking and bursting. These classifications are important for researchers working on theoretical neuroscience. In this paper, three different spiking neuron models; Izhikevich, Adaptive Exponential Integrate Fire (AEIF) and Hindmarsh Rose (HR), which are based on first order differential equations, are discussed and compared. First, the physical meanings, derivatives, and differential equations of each model are provided and simulated in the Matlab environment. Then, by selecting appropriate parameters, the models were visually examined in the Matlab environment and it was aimed to demonstrate which model can simulate well-known biological neuron behaviours such as Tonic Spiking, Tonic Bursting, Mixed Mode Firing, Spike Frequency Adaptation, Resonator and Integrator. As a result, the Izhikevich model has been shown to perform Regular Spiking, Continuous Explosion, Intrinsically Bursting, Thalmo Cortical, Low-Threshold Spiking and Resonator. The Adaptive Exponential Integrate Fire model has been able to produce firing patterns such as Regular Ignition, Adaptive Ignition, Initially Explosive Ignition, Regular Explosive Ignition, Delayed Ignition, Delayed Regular Explosive Ignition, Temporary Ignition and Irregular Ignition. The Hindmarsh Rose model showed three different dynamic neuron behaviours; Spike, Burst and Chaotic. From these results, the Izhikevich cell model may be preferred due to its ability to reflect the true behavior of the nerve cell, the ability to produce different types of spikes, and the suitability for use in larger scale brain models. The most important reason for choosing the Adaptive Exponential Integrate Fire model is that it can create rich ignition patterns with fewer parameters. The chaotic behaviours of the Hindmarsh Rose neuron model, like some chaotic systems, is thought to be used in many scientific and engineering applications such as physics, secure communication and signal processing.

Keywords: Izhikevich, adaptive exponential integrate fire, Hindmarsh Rose, biological neuron behaviours, spiking neuron models

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Authors: Pavol Tanuska, Pavel Vazan, Michal Kebisek, Dominika Jurovata

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The paper gives the results of the project that was oriented on the usage of knowledge discoveries from production systems for needs of the hierarchical process control. One of the main project goals was the proposal of knowledge discovery model for process control. Specifics data mining methods and techniques was used for defined problems of the process control. The gained knowledge was used on the real production system, thus, the proposed solution has been verified. The paper documents how it is possible to apply new discovery knowledge to be used in the real hierarchical process control. There are specified the opportunities for application of the proposed knowledge discovery model for hierarchical process control.

Keywords: hierarchical process control, knowledge discovery from databases, neural network, process control

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Authors: Jin-Siang Shaw, Patricia Moya Caceres, Sheng-Xiang Xu

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This paper aims to integrate the particle swarm optimization (PSO) method with the adaptive fuzzy sliding mode controller (AFSMC) to achieve vibration attenuation in a piezoelectric actuator subject to base excitation. The piezoelectric actuator is a complicated system made of ferroelectric materials and its performance can be affected by nonlinear hysteresis loop and unknown system parameters and external disturbances. In this study, an adaptive fuzzy sliding mode controller is proposed for the vibration control of the system, because the fuzzy sliding mode controller is designed to tackle the unknown parameters and external disturbance of the system, and the adaptive algorithm is aimed for fine-tuning this controller for error converging purpose. Particle swarm optimization method is used in order to find the optimal controller parameters for the piezoelectric actuator. PSO starts with a population of random possible solutions, called particles. The particles move through the search space with dynamically adjusted speed and direction that change according to their historical behavior, allowing the values of the particles to quickly converge towards the best solutions for the proposed problem. In this paper, an initial set of controller parameters is applied to the piezoelectric actuator which is subject to resonant base excitation with large amplitude vibration. The resulting vibration suppression is about 50%. Then PSO is applied to search for an optimal controller in the neighborhood of this initial controller. The performance of the optimal fuzzy sliding mode controller found by PSO indeed improves up to 97.8% vibration attenuation. Finally, adaptive version of fuzzy sliding mode controller is adopted for further improving vibration suppression. Simulation result verifies the performance of the adaptive controller with 99.98% vibration reduction. Namely the vibration of the piezoelectric actuator subject to resonant base excitation can be completely annihilated using this PSO based adaptive fuzzy sliding mode controller.

Keywords: adaptive fuzzy sliding mode controller, particle swarm optimization, piezoelectric actuator, vibration suppression

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Authors: Magdi M. Nabi, Ding-Li Yu

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This paper presents a neural network based model predictive control (MPC) strategy to control a strongly exothermic reaction with complicated nonlinear kinetics given by Chylla-Haase polymerization reactor that requires a very precise temperature control to maintain product uniformity. In the benchmark scenario, the operation of the reactor must be guaranteed under various disturbing influences, e.g., changing ambient temperatures or impurity of the monomer. Such a process usually controlled by conventional cascade control, it provides a robust operation, but often lacks accuracy concerning the required strict temperature tolerances. The predictive control strategy based on the RBF neural model is applied to solve this problem to achieve set-point tracking of the reactor temperature against disturbances. The result shows that the RBF based model predictive control gives reliable result in the presence of some disturbances and keeps the reactor temperature within a tight tolerance range around the desired reaction temperature.

Keywords: Chylla-Haase reactor, RBF neural network modelling, model predictive control, semi-batch reactors

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Authors: Shukri Dughman, Anthony Rossiter

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This paper builds on earlier work which demonstrated that Model Predictive Control (MPC) may give a poor choice of default feed forward compensator. By first demonstrating the impact of future information of target changes on the performance, this paper proposes a pragmatic method for identifying the amount of future information on the target that can be utilised effectively in both finite and infinite horizon algorithms. Numerical illustrations in MATLAB give evidence of the efficacy of the proposal.

Keywords: model predictive control, tracking control, advance knowledge, feed forward

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Authors: Mamoun S. Ideis, Zein Salah

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Virtual Rehabilitation (VR) refers to using Virtual Reality’s hardware and simulations as means of exercising tools to rehabilitate patients in need. These patients will undergo their treatment exercises while playing different computer games, which helps achieve greater motivation for patients undergoing their therapeutic exercises. Virtual Rehabilitation systems adopt computer games as part of the treatment therapy. In this paper, we present a preliminary proposal to using adaptive computer games in Virtual Rehabilitation therapy. We also present some tips in designing those adaptive computer games by using different machine learning algorithms in order to create a personalized experience for each patient, which in turn, increases the potential benefits of the treatment that each patient receives. Furthermore, we propose a method of comparing the results of treatment using the adaptive computer games with the results of using static and classical computer games.

Keywords: virtual rehabilitation, physiotherapy, adaptive computer games, post-stroke, game design

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Authors: Gurmail Singh

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Effective supply chain is the main priority of every organization which is the outcome of strategic corporate investments with deliberate management action. Value-driven supply chain is defined through development, procurement and by configuring the appropriate resources, metrics and processes. However, responsiveness of the supply chain can be improved by proper coordination. So the Bullwhip effect (BWE) and Net stock amplification (NSAmp) values were anticipated and used for the control of inventory in organizations by both discrete wavelet transform-Artificial neural network (DWT-ANN) and Adaptive Network-based fuzzy inference system (ANFIS). This work presents a comparative methodology of forecasting for the customers demand which is non linear in nature for a multilevel supply chain structure using hybrid techniques such as Artificial intelligence techniques including Artificial neural networks (ANN) and Adaptive Network-based fuzzy inference system (ANFIS) and Discrete wavelet theory (DWT). The productiveness of these forecasting models are shown by computing the data from real world problems for Bullwhip effect and Net stock amplification. The results showed that these parameters were comparatively less in case of discrete wavelet transform-Artificial neural network (DWT-ANN) model and using Adaptive network-based fuzzy inference system (ANFIS).

Keywords: bullwhip effect, hybrid techniques, net stock amplification, supply chain flexibility

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Authors: Muhammad Nouman Qureshi, Muhammad Hanif

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Adaptive cluster sampling (ACS) is specifically developed for the estimation of highly clumped populations and applied to a wide range of situations like animals of rare and endangered species, uneven minerals, HIV patients and drug users. In this paper, we proposed a generalized semi-exponential estimator with two auxiliary variables under the framework of ACS design. The expressions of approximate bias and mean square error (MSE) of the proposed estimator are derived. Theoretical comparisons of the proposed estimator have been made with existing estimators. A numerical study is conducted on real and artificial populations to demonstrate and compare the efficiencies of the proposed estimator. The results indicate that the proposed generalized semi-exponential estimator performed considerably better than all the adaptive and non-adaptive estimators considered in this paper.

Keywords: auxiliary information, adaptive cluster sampling, clustered populations, Hansen-Hurwitz estimation

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Authors: Anel Hasić, Naser Prljača

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In the domain of autonomous or piloted flights, the accurate control of quadrotor trajectories is of paramount significance for large numbers of tasks. These adaptable aerial platforms find applications that span from high-precision aerial photography and surveillance to demanding search and rescue missions. Among the fundamental challenges confronting quadrotor operation is the demand for accurate following of desired flight paths. To address this control challenge, among others, two celebrated well-established control strategies have emerged as noteworthy contenders: Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) control. In this work, we focus on the extensive examination of MPC and PID control techniques by using comprehensive simulation studies in MATLAB/Simulink. Intensive simulation results demonstrate the performance of the studied control algorithms.

Keywords: MATLAB, MPC, PID, quadcopter, simulink

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Authors: Sophia Fry, Mahir Irtiza, Alexa Hoffman, Yousef Sardahi

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This study presents an intelligent control algorithm for a flexible robotic arm. Fuzzy control is used to control the motion of the arm to maintain the arm tip at the desired position while reducing vibration and increasing the system speed of response. The Fuzzy controller (FC) is based on adding the tip angular position to the arm deflection angle and using their sum as a feedback signal to the control algorithm. This reduces the complexity of the FC in terms of the input variables, number of membership functions, fuzzy rules, and control structure. Also, the design of the fuzzy controller is model-free and uses only our knowledge about the system. To show the efficacy of the FC, the control algorithm is implemented on the flexible joint manipulator (FJM) developed by Quanser. The results show that the proposed control method is effective in terms of response time, overshoot, and vibration amplitude.

Keywords: fuzzy logic control, model-free control, flexible joint manipulators, nonlinear control

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Authors: Takashi Shimizu, Tomoaki Hashimoto

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A class of implicit systems is known as a more generalized class of systems than a class of explicit systems. To establish a control method for such a generalized class of systems, we adopt model predictive control method which is a kind of optimal feedback control with a performance index that has a moving initial time and terminal time. However, model predictive control method is inapplicable to systems whose all state variables are not exactly known. In other words, model predictive control method is inapplicable to systems with limited measurable states. In fact, it is usual that the state variables of systems are measured through outputs, hence, only limited parts of them can be used directly. It is also usual that output signals are disturbed by process and sensor noises. Hence, it is important to establish a state estimation method for nonlinear implicit systems with taking the process noise and sensor noise into consideration. To this purpose, we apply the model predictive control method and unscented Kalman filter for solving the optimization and estimation problems of nonlinear implicit systems, respectively. The objective of this study is to establish a model predictive control with unscented Kalman filter for nonlinear implicit systems.

Keywords: optimal control, nonlinear systems, state estimation, Kalman filter

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Authors: Sahar Mobeen, Anam Rafique, Irum Baig

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Acoustic echo cancellation in multichannel is a system identification application. In real time environment, signal changes very rapidly which required adaptive algorithms such as Least Mean Square (LMS), Leaky Least Mean Square (LLMS), Normalized Least Mean square (NLMS) and average (AFA) having high convergence rate and stable. LMS and NLMS are widely used adaptive algorithm due to less computational complexity and AFA used of its high convergence rate. This research is based on comparison of acoustic echo (generated in a room) cancellation thorough LMS, LLMS, NLMS, AFA and newly proposed average normalized leaky least mean square (ANLLMS) adaptive filters.

Keywords: LMS, LLMS, NLMS, AFA, ANLLMS

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Authors: Razieh Arian, Hadi Adibi-Asl

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This paper investigates an efficient combustion modeling for cycle simulation of internal combustion engine (ICE) studies. The term “efficient model” means that the models must generate desired simulation results while having fast simulation time. In other words, the efficient model is defined based on the application of the model. The objective of this study is to develop math-based models for control applications or shortly control-oriented models. This study compares different modeling approaches used to model the ICEs such as mean-value models, zero dimensional, quasi-dimensional, and multi-dimensional models for control applications. Mean-value models have been widely used for model-based control applications, but recently by developing advanced simulation tools (e.g. Maple/MapleSim) the higher order models (more complex) could be considered as control-oriented models. This paper presents the enhanced zero-dimensional cycle-by-cycle modeling and simulation of a spark ignition engine with a two-zone combustion model. The simulation results are cross-validated against the simulation results from GT-Power package and show a good agreement in terms of trends and values.

Keywords: Two-zone combustion, control-oriented model, wiebe function, internal combustion engine

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Authors: Abdelkader Harrouz, Omar Harrouz, Ali Benatiallah

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This paper is to present the essential definitions, roles and characteristics of automation of metering system. We discuss measurement, data acquisition and metrological control of a signal sensor from dynamic metering system. After that, we present control of instruments of metering system of fluid with more detailed discussions to the reference standards.

Keywords: communication, metering, computer, sensor

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Authors: Alia Alghosoun, Nabil El Moçayd, Mohammed Seaid

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Modeling dam-break flows over non-flat beds requires an accurate representation of the topography which is the main source of uncertainty in the model. Therefore, developing robust and accurate techniques for reconstructing topography in this class of problems would reduce the uncertainty in the flow system. In many hydraulic applications, experimental techniques have been widely used to measure the bed topography. In practice, experimental work in hydraulics may be very demanding in both time and cost. Meanwhile, computational hydraulics have served as an alternative for laboratory and field experiments. Unlike the forward problem, the inverse problem is used to identify the bed parameters from the given experimental data. In this case, the shallow water equations used for modeling the hydraulics need to be rearranged in a way that the model parameters can be evaluated from measured data. However, this approach is not always possible and it suffers from stability restrictions. In the present work, we propose an adaptive optimal control technique to numerically identify the underlying bed topography from a given set of free-surface observation data. In this approach, a minimization function is defined to iteratively determine the model parameters. The proposed technique can be interpreted as a fractional-stage scheme. In the first stage, the forward problem is solved to determine the measurable parameters from known data. In the second stage, the adaptive control Ensemble Kalman Filter is implemented to combine the optimality of observation data in order to obtain the accurate estimation of the topography. The main features of this method are on one hand, the ability to solve for different complex geometries with no need for any rearrangements in the original model to rewrite it in an explicit form. On the other hand, its achievement of strong stability for simulations of flows in different regimes containing shocks or discontinuities over any geometry. Numerical results are presented for a dam-break flow problem over non-flat bed using different solvers for the shallow water equations. The robustness of the proposed method is investigated using different numbers of loops, sensitivity parameters, initial samples and location of observations. The obtained results demonstrate high reliability and accuracy of the proposed techniques.

Keywords: erodible beds, finite element method, finite volume method, nonlinear elasticity, shallow water equations, stresses in soil

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Authors: Haowen Xu, Xiaobing Liu, Jin Dong, Jianming Lian

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Emerging smart buildings often employ cyberinfrastructure, cyber-physical systems, and Internet of Things (IoT) technologies to increase the automation and responsiveness of building operations for better energy efficiency and lower carbon emission. These operations include the control of Heating, Ventilation, and Air Conditioning (HVAC) and lighting systems, which are often considered a major source of energy consumption in both commercial and residential buildings. Developing energy-saving control models for optimizing HVAC operations usually requires the collection of high-quality instrumental data from iterations of in-situ building experiments, which can be time-consuming and labor-intensive. This abstract describes a digital twin approach to automate building energy experiments for optimizing HVAC operations through the design and development of an adaptive web-based platform. The platform is created to enable (a) automated data acquisition from a variety of IoT-connected HVAC instruments, (b) real-time situational awareness through domain-based visualizations, (c) adaption of HVAC optimization algorithms based on experimental data, (d) sharing of experimental data and model predictive controls through web services, and (e) cyber-physical control of individual instruments in the HVAC system using outputs from different optimization algorithms. Through the digital twin approach, we aim to replicate a real-world building and its HVAC systems in an online computing environment to automate the development of building-specific model predictive controls and collaborative experiments in buildings located in different climate zones in the United States. We present two case studies to demonstrate our platform’s capability for real-time situational awareness and cyber-physical control of the HVAC in the flexible research platforms within the Oak Ridge National Laboratory (ORNL) main campus. Our platform is developed using adaptive and flexible architecture design, rendering the platform generalizable and extendable to support HVAC optimization experiments in different types of buildings across the nation.

Keywords: energy-saving buildings, digital twins, HVAC, cyber-physical system, BIM

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