Loop Approach Research Articles

Simultaneous design of parameters and controller of robotic manipulators: closed loop approach to practical implementation

In this paper, Simultaneous Design of Plant parameters and Controller (SDPC) of robotic manipulators is presented using the Closed Loop (CL) optimal control approach. Since a robot is inherently used to perform repetitive tasks, the SDPC problem for robotic manipulator systems is practically reformulated as the optimal balancing problem and a demonstration of practical implementation. The optimality conditions are derived using the CL-optimal control theory and then solved by the policy iteration method. The modified policy iteration is further used to design robot parameters. The results are compared with the Open Loop (OL) solution through the optimal and static balancing cases. As a final point, although OL is the exact solution, but it has higher control cost over the CL implementation (for the case implemented here the cost difference is 16.75%). Also CL-case has much shorter calculation time (51%) than that of the OL case.

Classical molecular dynamics simulation of seryl tRNA synthetase and threonyl tRNA synthetase bound with tRNA and aminoacyl adenylate

Lacunae of understanding exist concerning the active site organization during the charging step of the aminoacylation reaction. We present here a molecular dynamics simulation study of the dynamics of the active site organization during charging step of subclass IIa dimeric SerRS from Thermus thermophilus (ttSerRS) bound with tttRNASer and dimeric ThrRS from Escherichia coli (ecThrRS) bound with ectRNAThr. The interactions between the catalytically important loops and tRNA contribute to the change in dynamics of tRNA in free and bound states, respectively. These interactions help in the development of catalytically effective organization of the active site. The A76 end of the tttRNASer exhibits fast dynamics in free State, which is significantly slowed down within the active site bound with adenylate. The loops change their conformation via multimodal dynamics (a slow diffusive mode of nanosecond time scale and fast librational mode of dynamics in picosecond time scale). The active site residues of the motif 2 loop approach the proximal bases of tRNA and adenylate by slow diffusive motion (in nanosecond time scale) and make conformational changes of the respective side chains via ultrafast librational motion to develop precise hydrogen bond geometry. Presence of bound Mg2+ ions around tRNA and dynamically slow bound water are other common features of both aaRSs. The presence of dynamically rigid Zinc ion coordination sphere and bipartite mode of recognition of ectRNAThr are observed.

A two-phase approach based on sequential approximation for reliability-based design optimization

The original problem of reliability-based design optimization (RBDO) is mathematically a nested two-level structure that is computationally time consuming for real engineering problems. In order to overcome the computational difficulties, many formulations have been proposed in the literature. These include SORA (sequential optimization and reliability assessment) that decouples the nested problems. SLA (single loop approach) further improves efficiency in that reliability analysis becomes an integrated part of the optimization problem. However, even SLA method can become computationally challenging for real engineering problems involving many reliability constraints. This paper presents an enhanced version of SLA where the first phase is based on approximation at nominal design point. After convergence of first iterative phase is reached the process transitions to a second phase where approximations of reliability constraints are carried out at their respective minimum performance target point (MPTP). The solution is implemented in Altair OptiStruct, where adaptive approximation and constraint screening strategies are utilized in the RBDO process. Examples show that the proposed two-phase approach leads to reduction in finite element analyses while preserving equal solution quality.

Enhancement of clean-in-place procedures in powder production using ultraviolet-induced fluorescence

In powder manufacturing facilities, clean-in-place (CIP) is a technique utilised for cleaning internal surfaces of equipment without having to dismantle them. However, the process is commonly based an open loop approach with parameter settings dependent on estimation and assumptions, leading to increased energy consumption and decreased process availability. It has previously been demonstrated that ultraviolet-induced fluorescence of proteins can be used for the detection of liquid foodstuffs in order to reduce CIP cycle times. This research investigates the suitability of an RGB vision sensor for detecting ultraviolet induced fluorescence of proteins in a range of powders (foodstuffs and detergents) under a simulated cleaning process. An image processing procedure is described that enables the evaluation of remaining fouling within a sample. The sensitivity of the system is demonstrated by comparing the coverage (area) of fouling against the volume remaining (weight). The suitability of the technique for industrial application is discussed.

A new methodology to reduce uncertainty of global attributes in naturally fractured reservoirs

Accurately characterizing fractures is complex. Several studies have proposed reducing uncertainty by incorporating fracture characterization into simulations, using a probabilistic approach, to maintain the geological consistency, of a range of models instead of a single matched model. We propose a new methodology, based on one of the steps of a general history-matching workflow, to reduce uncertainty of reservoir attributes in naturally fractured reservoirs. This methodology maintains geological consistency and can treat many reservoir attributes. To guarantee geological consistency, the geostatistical attributes (e.g., fracture aperture, length, and orientation) are used as parameters in the history matching. This allows us to control Discrete Fracture Network attributes, and systematically modify fractures. The iterative sensitivity analysis allows the inclusion of many (30 or more) uncertain attributes that might occur in a practical case. At each uncertainty reduction step, we use a sensitivity analysis to identify the most influential attributes to treat in each step. Working from the general history-matching workflow of Avansi et al. (2016), we adapted steps for use with our methodology, integrating the history matching with geostatistical modeling of fractures and other properties in a big loop approach. We applied our methodology to a synthetic case study of a naturally fractured reservoir, based on a real semi-synthetic carbonate field, offshore Brazil, to demonstrate the applicability in practical and complex cases. From the initial 18 uncertain attributes, we worked with only 5 and reduced the overall variability of the Objective Functions. Although the focus is on naturally fractured reservoirs, the proposed methodology can be applied to any type of reservoir.

Bio-Composites to Tackle UK Built Environment Carbon Emissions: Comparative Analysis on Load-Bearing Capacity, Hygroscopic and Thermal Performance of Compressed Earth Blocks with Addition of Industrial Hemp Waste

Background:The built environment contributes up to 47% of the UK’s CO2emissions. Typically, building materials have an energy intensive life cycle. The UK construction industry generated 100 million tonnes of total waste in 2012. Only half of this was diverted from landfill. Facing this scenario, a mainstream transition towards biological materials in the building sector is encouraged to reduce embodied energy and carbon and to divert construction waste from landfill.Objective:This research aims to inform UK producers that there are available bio-composite materials for construction that can meet the mainstream requirements.Method:This paper analyses the effect of the addition of waste from industrial hemp agriculture to Earth Blocks on the performance of building products. To avoid potentially negative consequences of food displacement, an agricultural by-product from industrial hemp production was used. Hemp was also selected due to its known superior hygrothermal performance.In the study, fine hemp shiv was added to Compressed Earth Blocks’ mix following three different Earth/Hemp ratios (75/25; 62.5/37.5; 50/50) also a control series. The compressive strength, conductivity and moisture performance were tested, analysed and then compared with the mainstream values.Results:The results revealed that blocks’ load-bearing capacity reduced and thermal conductivity also reduced, hence increasing its insulation capacity. The moisture performance also reduced as hemp ratio increased. In comparison with the mainstream products, the results comply with the standard requirements. Thermal capacity overperformed mainstream products.Conclusion:A closed loop approach for the construction industry is possible. Bio-composite materials offer the industry an option for the reduction of embodied energy, carbon emissions and construction waste, while meeting industry standards and building regulations.

Enhanced single-loop method for efficient reliability-based design optimization with complex constraints

Reliability-based design optimization (RBDO) has been widely implemented for engineering design optimization when considering the uncertainty. The single loop approaches (SLA) are highly efficient but is prone to converge with inappropriate results for highly nonlinear probabilistic constraints. In this paper, a novel RBDO algorithm is proposed based on single loop approach and the enhanced chaos control method, named as enhanced single-loop method (ESM). The performance of SLA is enhanced using an adaptive inverse reliability method with limited number of iterations. The adaptive step size is computed based on a merit function which is computed using the results of the new and previous iterations. The iterations of the probabilistic constraints of RBDO models are manually controlled in the range from 1 to 10 in ESM. The efficiency and accuracy of the ESM are compared through four nonlinear RBDO problems with complex constraints, including a nonlinear mathematical problem, two engineering problems and a practical complex stiffened panel example with complex buckling constraint for aircraft design. Results illustrate that the proposed ESM is more efficient and robust than the performance measure approach and reliability index approach for RBDO problems.

Enhanced performance of PV power control using model predictive control

This paper focuses on the use of model predictive control (MPC) to control a DC/DC boost converter in order to regulate the PV power. When integrated with the grid, the PV system must deliver maximum power most of the time; however, if a voltage sag occurs, new grid codes demand that the control system should limit the PV power generated to avoid over current conditions and, consequently, a grid disconnection. Maximum and reduced power modes are implemented following the MPC strategy to achieve high–performance and stable operation in the system. First, the system is modeled in Matlab/Simulink and PLECS to understand its operation and to evaluate the effectiveness of the proposed algorithm. Secondly, experimental results are verified using the control hardware–in–the–loop (CHIL) approach on the Real Time Digital Simulator (RTDS).

In-flight tracking and vibration control using the DLR’s multiple Swashplate system

This paper discusses the design, integration, and test of a higher harmonic control algorithm capable of both vibration control and in-flight blade tracking in conjunction with DLR’s multiple swashplate control system (META), while honoring predefined limits in usable control authority. The design of the control algorithm is described in detail and the results of coupled numerical investigations with both a general purpose multibody code by Politecnico di Milano and DLR’s comprehensive rotor code to determine the algorithm’s performance are presented. The integration of the control algorithm into the real-time control software is shown for the META system, where, for safety reasons, a semi-open loop approach was implemented. First tests of the controllers in-flight tracking mode to reduce 1/rev loads during hover yielded an almost complete reduction in 1/rev vibratory loads while maintaining constant rotor thrust. Following the experiments at DLR’s own facility, extensive wind-tunnel tests were performed in 2016 with META and a 5-bladed rotor system at the large low-speed facility of the German Dutch Wind Tunnels. The control algorithm was adapted to the 5-bladed rotor and successfully applied for in-flight blade tracking as well as the reduction of 5/rev hub loads using multi-harmonic pitch inputs with frequencies from 4/rev to 6/rev in cruise and high-speed flight condition. In both cases, the controller showed excellent performance and yielded satisfactory reductions of 1/rev rotor imbalances as well as a reduction of 5/rev hub vibrations by more than $$80\%$$ , while, at the same time, adhering to user set limits for the higher harmonic control amplitudes.

E-learning and blended learning in textile engineering education: a closed feedback loop approach

E-learning has gained a significant role in typical education and in professional training, thanks to the flexibility it offers to the time and location parameters of the education event framework. Purely e-learning scenarios are mostly limited either to Open University-type higher education institutions or to graduate level or professional degrees; blended learning scenarios are progressively becoming popular thanks to their balanced approach. The aim of the present work is to propose approaches that exploit the e-learning and the blended-learning scenarios for Textile Engineering education programmes, especially for multi-institutional ones. The “E-Team” European MSc degree programme organized by AUTEX is used as a case study. The proposed solution is based on (i) a free and open-source e-learning platform (moodle) and (ii) blended learning educational scenarios. Educational challenges addressed include student engagement, student error / failure handling, as well as collaborative learning promotion and support.

Convergence control of single loop approach for reliability-based design optimization

For solution of reliability-based design optimization (RBDO) problems, single loop approach (SLA) shows high efficiency. Thus SLA is extensively used in RBDO. However, the iteration solution procedure by SLA is often oscillatory and non-convergent for RBDO with nonlinear performance function. This prevents the application of SLA to engineering design problems. In this paper, the chaotic single loop approach (CLSA) is proposed to achieve the convergence control of original iterative algorithm in SLA. The modification involves automated selection of the chaos control factor by solving a novel one-dimensional optimization model. Additionally, a new oscillation-checking method is constructed to detect the oscillation of iterative process of design variables. The computational capability of CLSA is demonstrated through five benchmark examples and one stiffened shell application. The comparison of numerical results indicates that CSLA is more efficient than the double loop approach and the decoupled approach. CSLA also solves the RBDO problems with highly nonlinear performance function and non-normal random variables stably.

Quality of Experience-based museum touring: a human in the loop approach

This paper introduces a human in the loop approach toward proposing a physical, personal and interest-aware museum touring in order to maximize visitor’s perceived Quality of Experience (QoE). The most influential parameters on visitors QoE are identified and more importantly quantified via performing an empirical study based on a questionnaire answered by experts in the field of arts and museums. Individual QoE functions with respect to each of the identified parameters are formulated for different museum visitors’ styles, toward capturing visitors’ perceived utility in a formal manner and providing them a customized and personalized experience. A social recommendation and personalization approach is designed toward creating visitors’ profiles, exploiting common characteristics and interests among them and assisting in recommending a set of exhibits to be visited, through a ranking system according to visitor’s interests. A Museum Visitor QoE Routing problem is formulated as an optimization problem considering the various QoE-related characteristics. The latter is solved via a graph-based approach determining both an optimal and a heuristic but less complex solution. Detailed numerical results are provided toward illustrating the applicability of the proposed framework under different scenarios and topologies.

Model-based design validation for advanced energy management strategies for electrified hybrid power trains using innovative vehicle hardware in the loop (VHIL) approach

Hybridization of automotive powertrains by using more than one type of energy converter is considered as an important step towards reducing fuel consumption and air pollutants. Specifically, the development of energy efficient, highly complex, alternative drive-train systems, in which the interactions of different energy converters play an important role, requires new design methods and processes. This paper discusses the inclusion of an alternative hybrid power train into an existing vehicle platform for maximum energy efficiency. The new proposed integrated Vehicle Hardware In-the-loop (VHiL) and Model Based Design (MBD) approach is utilized to evaluate the energy efficiency of electrified powertrain. In VHiL, a complete chassis system becomes an integrated part of the vehicle test bed. A complete conventional Internal Combustion Engine (ICE) powered vehicle is tested in roller bench test for the integration of energy efficient hybrid electric power train modules in closed-loop, real-time, feedback configuration. A model that is a replica of the test vehicle is executed – in real-time- where all hybrid power train modules are included. While the VHiL platform is controlling the signal exchange between the test bed automation software and the vehicle on-board controller, the road load exerted on the driving wheels is manipulated in closed –loop real-time manner in order to reflect all hybrid driving modes including: All Electric Range (AER), Electric Power Assist (EPA) and blended Modes (BM). Upon successful implementation of VHiL, a comparative study between Rule Based (RB) energy management strategy (EMS) and Equivalent Consumption Minimization Strategy (ECMS) to Control Parallel Through-The-Road Hybrid Electric Vehicle (PTTR-HEV) is performed. The study shows that the actual fuel efficiency of the tested vehicle under both control strategies can be used in order to evaluate the effectiveness of energy conversion efficiency of the powertrain system. The fuel consumption of hybridized powertrain is compared with the conventional powertrain equipped in an actual vehicle to help comprehend the degree of efficiency attained by the hybridization. This process is developed in order to enable effective tuning/validation of advanced energy management strategies utilized in hybrid electric powertrain through an evaluation of a complete real chassis system subject to electric hybridization. The VHiL is considered as new evolution for the utilization of vehicle test bed as a predictive mechatronic platform for the development of energy efficient electrified propulsion systems and thus reduce cost and time.

Global positioning system processing methods for GPS passive coherent location

This study addresses processing methods for the use of the global positioning system (GPS) as a signal of opportunity in a multistatic passive coherent location (PCL) system. The signal properties of the GPS signal are designed for position estimation, which makes it particularly suited for GPS PCL applications. The signal specifications are examined and the potential limitations of a proposed system are explored. Conventional GPS processing techniques are implemented in the framework of multistatic GPS PCL. GPS specific methods are developed for target position estimation of a multistatic PCL system in a three-dimensional plane. The PCL system has comparable performance metrics to conventional GPS positioning. Two methods are developed utilising measurements available in a conventional GPS system. The first involves a short time scale, which is in the order of milliseconds. The second involves a long time scale method, which is on the order of seconds. Simulation and analysis are performed to verify the methods. Experimental validation was also conducted using a hardware in the loop approach, as well as, an active radar calibrator.

Multi-Objective Decision Making

Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can often increase the efficacy of the treatment, but at the cost of more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. To illustrate this, we employ the popular problem classes of multi-objective Markov decision processes (MOMDPs) and multi-objective coordination graphs (MO-CoGs). First, we discuss different use cases for multi-objective decision making, and why they often necessitate explicitly multi-objective algorithms. We advocate a utility-based approach to multi-objective decision making, i.e., that what constitutes an optimal solution to a multi-objective decision problem should be derived from the available information about user utility. We show how different assumptions about user utility and what types of policies are allowed lead to different solution concepts, which we outline in a taxonomy of multi-objective decision problems. Second, we show how to create new methods for multi-objective decision making using existing single-objective methods as a basis. Focusing on planning, we describe two ways to creating multi-objective algorithms: in the inner loop approach, the inner workings of a single-objective method are adapted to work with multi-objective solution concepts; in the outer loop approach, a wrapper is created around a single-objective method that solves the multi-objective problem as a series of single-objective problems. After discussing the creation of such methods for the planning setting, we discuss how these approaches apply to the learning setting. Next, we discuss three promising application domains for multi-objective decision making algorithms: energy, health, and infrastructure and transportation. Finally, we conclude by outlining important open problems and promising future directions.

Modelling end of life phase of the complex products: the case of end of life aircraft

There are different approaches to modelling the products at the End of Life (EOL), including closed loop approach, the ladder of lansink and material flow. The sustainability principles and the complexity of the products may affect the choice of modelling type. The life cycle costs, several subsystems and the interaction between design features, construction, technology, suppliers and legislation affect the degree of complexity of the products. Hence, developing appropriate strategies are required for modelling EOL phase of the complex products. This article presents strategies for modelling EOL phase of complex products considering sustainability tools, EOL phase models and the characteristics of these products. An ingenerated approach is proposed to address the four essential aspects of the complex product at the EOL: operational, tactical, strategic and sustainability. The case of aircraft at the EOL is presented to highlight the application of the proposed approach.

Centralized Protection Strategy for Medium Voltage DC Microgrids

This paper presents a centralized protection strategy for medium voltage dc microgrids. The proposed strategy consists of a communication-assisted fault detection method with a centralized protection coordinator and a fault isolation technique that provides an economic, fast, and selective protection by using the minimum number of dc circuit breakers. The proposed method is also supported by a backup protection that is activated if communication fails. This paper also introduces a centralized self-healing strategy that guarantees successful operation of zones that are separated from the main grid after the operation of the protection devices. Furthermore, to provide a more reliable protection, thresholds of the protection devices are adapted according to the operational modes of the microgrid and the status of distributed generators. The effectiveness of the proposed protection strategy is validated through real-time simulation studies based on the hardware in the loop approach.

Analytical Modeling of Human Choice Complexity in a Mixed Model Assembly Line Using Machine Learning-Based Human in the Loop Simulation

Despite the recent advances in manufacturing automation, the role of human involvement in manufacturing systems is still regarded as a key factor in maintaining higher adaptability and flexibility. In general, however, modeling of human operators in manufacturing system design still considers human as a physical resource represented in statistical terms. In this paper, we propose a human in the loop (HIL) approach to investigate the operator’s choice complexity in a mixed model assembly line. The HIL simulation allows humans to become a core component of the simulation, therefore influencing the outcome in a way that is often impossible to reproduce via traditional simulation methods. At the initial stage, we identify the significant features affecting the choice complexity. The selected features are in turn used to build a regression model, in which human reaction time with regard to different degree of choice complexity serves as a response variable used to train and test the model. The proposed method, along with an illustrative case study, not only serves as a tool to quantitatively assess and predict the impact of choice complexity on operator’s effectiveness, but also provides an insight into how complexity can be mitigated without affecting the overall manufacturing throughput.

A New Open Loop Approach for Identifying the Initial Rotor Position of a Permanent Magnet Synchronous Motor

The precision of initial rotor position detection is critical for the start and running performance of permanent magnet synchronous motor (PMSM). This work describes a new open loop approach for identifying the initial position of a PMSM with an incremental encoder, even when a constant load torque is being applied. By giving a testing current with high frequency to the stator winding, the initial rotor position of a PMSM can be detected with reasonable accuracy. The rotor almost does not move during the process of identification. The FFT algorithms are used to remove the phase bias effects in identification. Our approach is quicker and simpler than the conventional approaches.

MobisenseCar: A Mobile Crowd-Based Architecture for Data Acquisition and Processing in Vehicle-Based Sensing

The use of wireless technology via smartphone allows designing smartphone applications based on OBD-II for increasing environment sensing. However, uploading of vehicle’s diagnostics data via car driver’s tethered smart phone attests a long Internet latency when a large number of concurrent users use the remote mobile crowdsensing server application simultaneously, which increases the communication cost. The large volume of data would also challenge the traditional data processing framework. This paper studies design functionalities of mobile crowdsensing architecture applied to vehicle-based sensing for handling a huge amount of sensor data collected by those vehicle-based sensors equipped with a smart device connected to the OBD-II interface. The proposed MobiSenseCar uses Node.js, a web server architecture based on single-thread event loop approach and Apache Hive platform responsible for analyzing vehicle’s engine data. The Node.JS is 40% faster than the traditional web server side features thread-based approach. Experiment results show that MapReduce algorithm is highly scalable and optimized for distributed computing. With this mobile crowdsensing architecture it was possible to monitor information of car’s diagnostic system condition in real time, improving driving ability and protect the environment by reducing vehicle emissions.