A Unified Control Framework for Traction Machine Drive Using Linear Parameters Varying-Based Field-Oriented Control

2020 ◽  
Vol 142 (10) ◽  
Author(s):  
Athar Hanif ◽  
Qadeer Ahmed ◽  
Aamer Iqbal Bhatti ◽  
Giorgio Rizzoni
Keyword(s):  
Operating Conditions ◽  
Linear Matrix ◽  
Field Oriented Control ◽  
Optimization Approach ◽  
Direct Drive ◽  
Multi Objective ◽  
The Road ◽  
Control Framework ◽  
Conflicting Objectives ◽  
The Stability

Abstract The performance of an electrified powertrain in extreme operating conditions is greatly compromised. This is due to the fact that meeting the road loads, ensuring efficient powertrain operation, and minimizing the loss of lifetime (aging) of an electric machine are three essential but conflicting targets. In this paper, a unified multi-objective linear parameters varying (LPV)-based field-oriented control (FOC) framework is proposed to solve the problem of conflicting objectives mentioned above. The outer loop in a unified control framework generates the reference currents using linear matrix inequality (LMI)-based torque and flux controllers. Moreover, optimal flux is estimated using LPV observer to ensure efficient machine operations. In the inner loop of a unified control framework, the multi-objective controller (MOC) is synthesized by selecting optimal weighting functions using LMI-based convex optimization approach. The stability of the proposed unified control framework is also analyzed. The effectiveness of the proposed unified control framework is tested for a direct drive electrified powertrain of a three-wheeled vehicle commonly found in urban transportation for Asian countries. The urban driving schedule-based simulation results confirm that the lifetime of traction machine can be enhanced by appropriate control framework design without compromising its performance.

Multi-Objective Modeling of Leading-Edge Serrations Applied to Low-Pressure Axial Fans

2020 ◽  
Vol 142 (11) ◽  
Author(s):  
Till M. Biedermann ◽  
M. Reich ◽  
C. O. Paschereit
Keyword(s):  
Low Pressure ◽  
High Accuracy ◽  
Operating Conditions ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Latin Hypercube Design ◽  
Axial Fan ◽  
Multi Objective ◽  
Target Values ◽  
Leading Edges

Abstract A novel modeling strategy is proposed which allows high-accuracy predictions of aerodynamic and aeroacoustic target values for a low-pressure axial fan, equipped with serrated leading edges. Inspired by machine learning processes, the sampling of the experimental space is realized by use of a Latin hypercube design plus a factorial design, providing highly diverse information on the analyzed system. The effects of four influencing parameters (IP) are tested, characterizing the inflow conditions as well as the serration geometry. A total of 65 target values in the time and frequency domains are defined and can be approximated with high accuracy by individual artificial neural networks. Furthermore, the validation of the model against fully independent test points within the experimental space yields a remarkable fit, even for the spectral distribution in 1/3-octave bands, proving the ability of the model to generalize. A metaheuristic multi-objective optimization approach provides two-dimensional Pareto optimal solutions for selected pairs of target values. This is particularly important for reconciling opposing trends, such as the noise reduction capability and aerodynamic performance. The chosen optimization strategy also allows for a customized design of serrated leading edges, tailored to the specific operating conditions of the axial fan.

Genetic Algorithm Based Optimization of Cutting Parameters in Drilling of Composite Materials

2010 ◽  
Author(s):  
Hamidreza Namazi
Keyword(s):  
Genetic Algorithm ◽  
Composite Materials ◽  
Removal Rate ◽  
Optimization Procedure ◽  
Operating Conditions ◽  
Machining Process ◽  
Graphical Form ◽  
Optimization Approach ◽  
Multi Objective ◽  
Optimization Of Cutting Parameters

Composite materials provide distinctive advantages in manufacture of advanced products because of attractive features such as high strength and light weight, but on the other hand machining of composite materials is difficult to carry out due to the anisotropic and non-homogeneous structure of composites and to the high abrasiveness of their reinforcing constituents. This typically results in damage being introduced into the workpiece and in very rapid wear development in the cutting tool. Conventional machining process such as drilling can be applied to composite materials, provided proper tool design and operating conditions are adopted. In this paper, A genetic algorithm (GA) based optimization procedure has been developed to optimize two factors, material removal rate; and delamination factor, using multi-objective function model with a weighted approach for the productivity, and superficial quality. An a posteriori approach was used to obtain a set of optimal solutions. An application sample was developed and its results were analyzed for several different production conditions. Finally, the obtained outcomes were arranged in graphical form and analyzed to make the proper decision for different process preferences. This paper also remarks the advantages of multi-objective optimization approach over the single-objective one.

Multi-Objective Optimization in Multi-Attribute and Multi-Unit Combinatorial Reverse Auctions

2017 ◽  
Vol 26 (05) ◽  
pp. 1760016 ◽  
Author(s):  
Shubhashis Kumar Shil ◽  
Samira Sadaoui
Keyword(s):  
Quality Measures ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Solution Quality ◽  
Winner Determination ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Trading Constraints ◽  
Significant Superiority

This study introduces an advanced Combinatorial Reverse Auction (CRA), multi-units, multiattributes and multi-objective, which is subject to buyer and seller trading constraints. Conflicting objectives may occur since the buyer can maximize some attributes and minimize some others. To address the Winner Determination (WD) problem for this type of CRAs, we propose an optimization approach based on genetic algorithms that we integrate with our variants of diversity and elitism strategies to improve the solution quality. Moreover, by maximizing the buyer’s revenue, our approach is able to return the best solution for our complex WD problem. We conduct a case study as well as simulated testing to illustrate the importance of the diversity and elitism schemes. We also validate the proposed WD method through simulated experiments by generating large instances of our CRA problem. The experimental results demonstrate on one hand the performance of our WD method in terms of several quality measures, like solution quality, run-time complexity and trade-off between convergence and diversity, and on the other hand, it’s significant superiority to well-known heuristic and exact WD techniques that have been implemented for much simpler CRAs.

scholarly journals A comparative study of nonlinear circle criterion based observer and H∞ observer for induction motor drive

2019 ◽  
Vol 10 (3) ◽  
pp. 1229
Author(s):  
Farid Berrezzek ◽  
Wafa Bourbia ◽  
Bachir Bensaker
Keyword(s):  
Comparative Study ◽  
Induction Motor ◽  
Observer Design ◽  
Nonlinear Observer ◽  
Linear Matrix ◽  
Optimization Approach ◽  
Lmi Optimization ◽  
Circle Criterion ◽  
True State ◽  
The Stability

<span lang="EN-US">This paper deals with a comparative study of circle criterion based nonlinear observer<em> </em>and <em>H<sub>∞</sub></em> observer for induction motor (IM) drive. The  advantage of the circle criterion approach for nonlinear observer design is that it directly handles the nonlinearities of the system with less restriction  conditions in contrast of the other methods which attempt to eliminate them. However the <em>H<sub>∞</sub></em> observer guaranteed the stability taking into account disturbance and noise attenuation. Linear matrix inequality (LMI) optimization approach is used to compute the gains matrices for the two observers. The simulation results show the superiority of <em>H<sub>∞</sub></em> observer in the sense that it can achieve convergence to the true state, despite the nonlinearity of model and the presence of disturbance.</span>

scholarly journals Robust H∞ Output-Feedback Yaw Control for Vehicles with Differential Steering

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Paul Oke ◽  
Sing Kiong Nguang ◽  
Wentai Qu
Keyword(s):  
Output Feedback ◽  
External Disturbance ◽  
Longitudinal Velocity ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
The Road ◽  
Yaw Control ◽  
Output Constraints ◽  
Differential Steering ◽  
The Stability

This paper examines the problem of designing a robust H∞ output-feedback yaw controller with both input and output constraints for four-wheel independently driven in-wheel electric vehicles (EVs) with differential steering. Specifically, the controller aims are to ensure the stability and improve the performance of the EV despite variations in the road adhesion coefficient, longitudinal velocity, and external disturbance. Based on the linear matrix inequalities approach, sufficient conditions for the existence of an H∞ output-feedback controller for linear systems with polytopic uncertainties, and input and control output constraints, are derived. Then those sufficient conditions are utilized to design an H∞ output-feedback yaw controller that guarantees the robust performance and stability of an EV over a wider range of road conditions. Finally, the capability of the developed controller is simulated on a vehicle model with uncertain road conditions and longitudinal velocities.

Design of Optimal Operational Parameters for Steam-Alternating-Solvent Processes in Heterogeneous Reservoirs - A Multi-Objective Optimization Approach

2021 ◽  
Author(s):  
Israel Mayo-Molina ◽  
Juliana Y. Leung
Keyword(s):  
Sensitivity Analysis ◽  
Computational Time ◽  
Optimization Approach ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Operational Parameters ◽  
Multi Objective ◽  
Bottom Hole ◽  
Decision Variables ◽  
Conflicting Objectives

Abstract The Steam Alternating Solvent (SAS) process has been proposed and studied in recent years as a new auspicious alternative to the conventional thermal (steam-based) bitumen recovery process. The SAS process incorporates steam and solvent (e.g. propane) cycles injected alternatively using the same configuration as the Steam-Assisted Gravity-Drainage (SAGD) process. The SAS process offers many advantages, including lower capital and operational cost, as well as a reduction in water usage and lower Greenhouse Gas (GHG) Emissions. On the other hand, one of the main challenges of this relatively new process is the influence of uncertain reservoir heterogeneity distribution, such as shale barriers, on production behaviour. Many complex physical mechanisms, including heat transfer, fluid flows, and mass transfer, must be coupled. A proper design and selection of the operational parameters must consider several conflicting objectives. This work aims to develop a hybrid multi-objective optimization (MOO) framework for determining a set of Pareto-optimal SAS operational parameters under a variety of heterogeneity scenarios. First, a 2-D homogeneous reservoir model is constructed based on typical Cold lake reservoir properties in Alberta, Canada. The homogeneous model is used to establish a base scenario. Second, different shale barrier configurations with varying proportions, lengths, and locations are incorporated. Third, a detailed sensitivity analysis is performed to determine the most impactful parameters or decision variables. Based on the results of the sensitivity analysis, several objective functions are formulated (e.g., minimizing energy and solvent usage). Fourth, Response Surface Methodology (RSM) is applied to generate a set of proxy models to approximate the non-linear relationship between the decision variables and the objective functions and to reduce the overall computational time. Finally, three Multi-Objective Evolutionary Algorithms (MOEAs) are applied to search and compare the optimal sets of decision parameters. The study showed that the SAS process is sensitive to the shale barrier distribution, and that impact is strongly dependent on the location and length of a specific shale barrier. When a shale barrier is located near the injector well, pressure and temperature may build up in the near-well area, preventing additional steam and solvent be injected and, consequently, reducing the oil production. Operational constraints, such as bottom-hole pressure, steam trap criterion, and bottom-hole gas rate in the producer, are among various critical decision variables examined in this study. A key conclusion is that the optimal operating strategy should depend on the underlying heterogeneity. Although this notion has been alluded to in other previous steam- or solvent-based studies, this paper is the first to utilize a MOO framework for systematically determining a specific optimal strategy for each heterogeneity scenario. With the advancement of continuous downhole fibre-optic monitoring, the outcomes can potentially be integrated into other real-time reservoir characterization and optimization work-flows.

A Multi-Objective Model for Evaluation of Road-Region Ecosystem Stability

2011 ◽  
Vol 48-49 ◽  
pp. 60-63
Author(s):  
Zhi Bin Qin ◽  
Zhao Hui Liu ◽  
Yu Zhi Li
Keyword(s):  
Index System ◽  
Comprehensive Evaluation ◽  
Evaluation Index System ◽  
Multidimensional Space ◽  
Ecosystem Stability ◽  
Multi Objective ◽  
The Road ◽  
Objective Model ◽  
The Stability ◽  
The Relationship

The concept of the road-region ecosystem is described and its environmental features analyzed. In addition, the stability of the road-region ecosystem is summarized. A scientific and rational evaluation of road-region ecosystem stability is proposed to properly investigate the relationship between highway construction and protection of the ecological environment. This paper investigated a method for determining an index system to be used to evaluate road-region ecosystem stability. It put forward an index system for assessing road-region ecosystem stability as a reference. On the basis of detailed analysis of the multidimensional space of the road-region ecosystem, a new method and calculation formula for multi-objective comprehensive evaluation of road-region ecosystem stability are presented. Computation result is fit with the actual situation. The result indicates that the evaluation index system and the method are feasible.

A Multi-Objective Optimization Approach on Spiral Grooves for Gas Mechanical Seals

2018 ◽  
Vol 140 (4) ◽  
Author(s):  
Xiuying Wang ◽  
Liping Shi ◽  
Wei Huang ◽  
Xiaolei Wang
Keyword(s):  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Mechanical Seals ◽  
Independent Variables ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Conflicting Objectives ◽  
Collinearity Diagnostics ◽  
Film Stiffness ◽  
Spiral Grooves

Spiral groove is one of the most common types of structures on gas mechanical seals. Numerical research demonstrated that the grooves designed for improving gas film lift or film stiffness often lead to the leakage increase. Hence, a multi-objective optimization approach specially for conflicting objectives is utilized to optimize the spiral grooves for a specific sample in this study. First, the objectives and independent variables in multi-objective optimization are determined by single objective analysis. Then, a set of optimal parameters, i.e., Pareto-optimal set, is obtained. Each solution in this set can get the highest dimensionless gas film lift under a specific requirement of the dimensionless leakage rate. Finally, the collinearity diagnostics is performed to evaluate the importance of different independent variables in the optimization.

A framework for tolerance design considering systematic and random uncertainties due to operating conditions

2019 ◽  
Vol 39 (5) ◽  
pp. 854-871
Author(s):  
S. Khodaygan
Keyword(s):  
Operating Conditions ◽  
Tolerance Allocation ◽  
Manufacturing Cost ◽  
Tolerance Design ◽  
Element Analysis ◽  
Content Type ◽  
Multi Objective ◽  
Mechanical Assemblies ◽  
Conflicting Objectives ◽  
Random Uncertainties

Purpose The purpose of this paper is to present a novel Kriging meta-model assisted method for multi-objective optimal tolerance design of the mechanical assemblies based on the operating conditions under both systematic and random uncertainties. Design/methodology/approach In the proposed method, the performance, the quality loss and the manufacturing cost issues are formulated as the main criteria in terms of systematic and random uncertainties. To investigate the mechanical assembly under the operating conditions, the behavior of the assembly can be simulated based on the finite element analysis (FEA). The objective functions in terms of uncertainties at the operating conditions can be modeled through the Kriging-based metamodeling based on the obtained results from the FEA simulations. Then, the optimal tolerance allocation procedure is formulated as a multi-objective optimization framework. For solving the multi conflicting objectives optimization problem, the multi-objective particle swarm optimization method is used. Then, a Shannon’s entropy-based TOPSIS is used for selection of the best tolerances from the optimal Pareto solutions. Findings The proposed method can be used for optimal tolerance design of mechanical assemblies in the operating conditions with including both random and systematic uncertainties. To reach an accurate model of the design function at the operating conditions, the Kriging meta-modeling is used. The efficiency of the proposed method by considering a case study is illustrated and the method is verified by comparison to a conventional tolerance allocation method. The obtained results show that using the proposed method can lead to the product with a more robust efficiency in the performance and a higher quality in comparing to the conventional results. Research limitations/implications The proposed method is limited to the dimensional tolerances of components with the normal distribution. Practical implications The proposed method is practically easy to be automated for computer-aided tolerance design in industrial applications. Originality/value In conventional approaches, regardless of systematic and random uncertainties due to operating conditions, tolerances are allocated based on the assembly conditions. As uncertainties can significantly affect the system’s performance at operating conditions, tolerance allocation without including these effects may be inefficient. This paper aims to fill this gap in the literature by considering both systematic and random uncertainties for multi-objective optimal tolerance design of mechanical assemblies under operating conditions.

Pareto-Based Multi-Objective Optimization of Recuperated S-CO2 Brayton Cycles

2014 ◽  
Author(s):  
Mahmood Mohagheghi ◽  
Jayanta Kapat ◽  
Narasimha Nagaiah
Keyword(s):  
System Optimization ◽  
Superior Performance ◽  
Specific Power ◽  
Pareto Optimal ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Cycle Efficiency ◽  
Pareto Optimal Fronts

In this paper, two configurations of the S-CO2 Brayton cycles (i.e., the single-recuperated and recompression cycles) are thermodynamically modeled and optimized through a multi-objective approach. Two semi-conflicting objectives, i.e., cycle efficiency (ηc) and cycle specific power (Φsp) are maximized simultaneously to achieve Pareto optimal fronts. The objective of maximum cycle efficiency is to have a smaller and less expensive solar field, and a lower fuel cost in case of a hybrid scheme. On the other hand, the objective of maximum specific power provides a smaller power block, and a lower capital cost associated with recuperators and coolers. The multi-objective optimization is carried out by means of a genetic algorithm which is a robust method for multidimensional, nonlinear system optimization. The optimization process is comprehensive, i.e., all the decision variables including the inlet temperatures and pressures of turbines and compressors, the pinch point temperature differences, and the mass flow fraction of the main compressor are optimized simultaneously. The presented Pareto optimal fronts provide two optimum trade-off curves enabling decision makers to choose their desired compromise between the objectives, and to avoid naive solution points obtained from a single-objective optimization approach. Moreover, the comparison of the Pareto optimal fronts associated with the studied configurations reveals the optimum operational region of the recompression configuration where it presents superior performance over the single-recuperated cycle.

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