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Design Boundary Layer Thickness and Switching Gain in SMC algorithm for AUV Motion Control

ROBOTICA 2019年第10期37卷 1785-1803页

作者： Taheri, Ehsan Ferdowsi, Mohamad Hossein Danesh, Mohammad Malek Ashtar Univ Technol Elect Engn Dept Control Grp Tehran 158751774 Iran Isfahan Univ Technol Dept Mech Engn Esfahan *** Iran

Designing the boundary layer thickness and switching gain in the nonlinear part of sliding mode controller (SMC) is one of the main subjects in SMC design that needs human experience, knowledge on the amplitude of disturbances, and information about the bounds of system uncertainties. In this paper, to reduce the trial-and-error effort by the designer(s) two different fitness functions in the horizontal and vertical planes are presented and a heuristic method is used for their optimization. The optimal switching gain in the proposed approach properly compensates the unmodeled dynamics, model uncertainty, and external disturbances. Chattering phenomenon in control signals and noise measurement effect are reduced by the optimal selection of boundary layer thickness. This proposed method is applied to an autonomous underwater vehicle (AUV) and evaluated through the real-time and cost-effective manner. The execution code is implemented on a single-board computer (SBC) through the xPC Target and is evaluated by the processor-in-the-loop (PIL) test. The results of the PIL test in the two different test cases indicate that the chattering phenomenon and amplitude of control signal applied to the actuators are reduced in comparison with the three conventional approaches in the AUV motion control.

关键词： Processor-in-the-loop test Sliding mode controller Autonomous underwater vehicle heuristic optimization algorithm

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Neighborhood information-based probabilistic algorithm for network disintegration

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 139卷 112853-000页

作者： Li, Qian Liu, San-Yang Yang, Xin-She Xidian Univ Sch Math & Stat Xian Shaanxi Peoples R China Middlesex Univ Sch Sci & Technol London NW4 4BT England

Many real-world applications can be modelled as complex networks, and such networks include the Internet, epidemic disease networks, transport networks, power grids, protein-folding structures and others. Network integrity and robustness are important to ensure that crucial networks are protected and undesired harmful networks can be dismantled. Network structure and integrity can be controlled by a set of key nodes, and to find the optimal combination of nodes in a network to ensure network structure and integrity can be an NP-complete problem. Despite extensive studies, existing methods have many limitations and there are still many unresolved problems. This paper presents a probabilistic approach based on neighborhood information and node importance, namely, neighborhood information-based probabilistic algorithm (NIPA). We also define a new centrality-based importance measure (IM), which combines the contribution ratios of the neighbor nodes of each target node and two-hop node information. Our proposed NIPA has been tested for different network benchmarks and compared with three other methods: optimal attack strategy (OAS), high betweenness first (HU) and high degree first (HDF). Experiments suggest that the proposed NIPA is most effective among all four methods. In general, NIPA can identify the most crucial node combination with higher effectiveness, and the set of optimal key nodes found by our proposed NIPA is much smaller than that by heuristic centrality prediction. In addition, many previously neglected weakly connected nodes are identified, which become a crucial part of the newly identified optimal nodes. Thus, revised strategies for protection are recommended to ensure the safeguard of network integrity. Further key issues and future research topics are also discussed. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Network disintegration Network robustness heuristic optimization algorithm Centrality measure Optimal attack strategy

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A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in a Smart Grid

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ENERGIES 2020年第9期13卷 2244页

作者： Hafeez, Ghulam Alimgeer, Khurram Saleem Wadud, Zahid Shafiq, Zeeshan Khan, Mohammad Usman Ali Khan, Imran Khan, Farrukh Aslam Derhab, Abdelouahid COMSATS Univ Islamabad Dept Elect & Comp Engn Islamabad Campus Islamabad 44000 Pakistan Univ Engn & Technol Dept Elect Engn Mardan 23200 Pakistan Univ Engn & Technol Peshawar Dept Comp Syst Engn Peshawar 25000 Pakistan Univ Engn & Technol Peshawar Dept Elect Engn Peshawar 25000 Pakistan King Saud Univ Ctr Excellence Informat Assurance Riyadh 11653 Saudi Arabia

Energy consumption forecasting is of prime importance for the restructured environment of energy management in the electricity market. Accurate energy consumption forecasting is essential for efficient energy management in the smart grid (SG);however, the energy consumption pattern is non-linear with a high level of uncertainty and volatility. Forecasting such complex patterns requires accurate and fast forecasting models. In this paper, a novel hybrid electrical energy consumption forecasting model is proposed based on a deep learning model known as factored conditional restricted Boltzmann machine (FCRBM). The deep learning-based FCRBM model uses a rectified linear unit (ReLU) activation function and a multivariate autoregressive technique for the network training. The proposed model predicts future electrical energy consumption for efficient energy management in the SG. The proposed model is a novel hybrid model comprising four modules: (i) data processing and features selection module, (ii) deep learning-based FCRBM forecasting module, (iii) genetic wind driven optimization (GWDO) algorithm-based optimization module, and (iv) utilization module. The proposed hybrid model, called FS-FCRBM-GWDO, is tested and evaluated on real power grid data of USA in terms of four performance metrics: mean absolute percentage deviation (MAPD), variance, correlation coefficient, and convergence rate. Simulation results validate that the proposed hybrid FS-FCRBM-GWDO model has superior performance than existing models such as accurate fast converging short-term load forecasting (AFC-STLF) model, mutual information-modified enhanced differential evolution algorithm-artificial neural network (MI-mEDE-ANN)-based model, features selection-ANN (FS-ANN)-based model, and Bi-level model, in terms of forecast accuracy and convergence rate.

关键词： smart grid electric energy consumption forecasting deep learning factored conditional restricted Boltzmann machine rectified linear unit modified feature selection technique heuristic optimization algorithm

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Design optimization of air distribution systems in non-residential buildings

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ENERGY AND BUILDINGS 2018年 175卷 48-56页

作者： Jorens, Sandy Verhaert, Ivan Sorensen, Kenneth Univ Antwerp EMIB HVAC Campus GroenenborgerBldg ZGroenenborgerlaan 171 B-2020 Antwerp Belgium Univ Antwerp Operat Res Grp ANT OR City CampusPrinsstr 13 B-2000 Antwerp Belgium

Centralized air distribution systems in non-residential buildings are characterized by an extensive air distribution network, that has to be built in a building environment with finite degrees of freedom. The ductwork layout, i.e., the network structure of the ducts, as well as the number and location of the fans, has a large impact on the total cost and performance of the air distribution system. Nevertheless, existing air distribution system design methods are limited to the sizing of each duct (and fan) in the network. The layout itself is considered predetermined, and thus not explicitly taken into account for optimization. In this paper, we meet this shortcoming by presenting the air distribution network design optimization method, that is able to calculate the optimal air distribution system configuration, i.e., the optimal layout and duct and fan sizes, while minimizing the total cost of the air distribution system. A multi-start local search algorithm is developed, consisting of a constructive and a local search phase. In the first phase, multiple air distribution system configurations are generated, and evaluated for feasibility. In the local search phase, all feasible solutions are further optimized in terms of material costs by decreasing and increasing the duct diameters following the steepest descent/mildest ascent approach. An application of the algorithm on a realistic test case demonstrates its usefulness in practice. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Air distribution system design Ductwork layout Duct and fan sizing heuristic optimization algorithm Multi-start local search Test case

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heuristic optimization algorithm for Automated Control Design of Bioleaching of Chalcopyrite

Heuristic Optimization Algorithm for Automated Control Desig...

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4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE)

作者： Li, Wei Yang, Aili Liu, Lei North China Elect Power Univ Energy & Environm Res Ctr Beijing Peoples R China Dalhousie Univ Dept Civil Engn Halifax NS Canada

ISBN: (纸本)9781424447138

Microbially assisted recovery of copper from low- grade chalcopyrite has been reported to be a very difficult process, conventional hydrometallurgical methods were limited by many parameters. This study focus on the design and the training of a Multi- Layer Perceptron classifier for the optimized preparation conditions for bioleaching of chalcopyrite. The proposed approach uses the heuristic Backpropagation Neural Network generation and training (BPNN) algorithm to generate the neural network system. The optimization conditions for bioleaching of chalcopyrite in the system were discussed based on the artificial neural network model, in which the input conditions were selected as stirring speed, volume of inoculum, and process pH. The highest Cu dissolution of bioleaching was regarded as the optimization aim, along with constraints of each factor's bounds. The highest copper recovery of 32.1% is obtained at pH 1.5, stirring speed of 140r/min, and 13% (v/v) inoculum concentration. The BP model based on heuristic optimization algorithm for bioleaching of chalcopyrite has practicability.

关键词： heuristic optimization algorithm Bioleaching of chalcopyrite Process control BP neural network

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Improved Node Localization for WSN using heuristic optimization Approaches

Improved Node Localization for WSN using Heuristic Optimizat...

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International Conference on Networking and Network Applications (NaNA)

作者： Shieh, Chin-Shiuh Van-Oanh Sai Lin, Yuh-Chung Lee, Tsair-Fwu Trong-The Nguyen Quang-Duy Le Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 80778 Taiwan Tajen Univ Dept Informat Applicat & Management Pingtung Taiwan

ISBN: (纸本)9781467398039

In Wireless Sensor Network, the localization of sensor nodes is an important problem in many applications. Normally in localization problem, the unknown position nodes will be determined their location through information of three or more anchors. In first part, some popular heuristic optimization methods like Genetic algorithm (GA), Particle Swarm optimization (PSO) will be compared with some recent optimization methods like Grey Wolf Optimizer (GWO), Firefly algorithm (FA), and Brain Storm optimization (BSO) algorithms in estimating the location of sensor nodes about accuracy. In second part, the improvement in localization algorithm is also proposed to enhance the number of nodes that can be localized. The results of our proposed improvement will be compared with original algorithm in both number of nodes that can be localized and the execute time with different deployment of networks.

关键词： Improved algorithm node localization wireless sensor network heuristic optimization algorithm comparative study

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Conjunctive use of surface and ground water resources using the ant system optimization

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AGRICULTURAL WATER MANAGEMENT 2016年 173卷 23-34页

作者： Safavi, Hamid R. Enteshari, Sajad Isfahan Univ Technol Dept Civil Engn Esfahan Iran

Conjunctive use of surface and ground water resources forms an important part of integrated water resource planning and management in a basin-wide scale. This paper presents a simulation/optimization model based on artificial neural networks (ANNs) and an ant system optimization (AS) for solving the monthly conjunctive supply of irrigation water. The study area is Najafabad Plain located in the semi-arid west central Iran. The water resources in the region are under increasing pressure to meet the growing irrigation water demand despite the dire decline in its surface water resources due to climate changes. The main objective of the conjunctive use model developed in this study is to minimize the water deficit in the three irrigation zones subject to constraints on groundwater levels and cumulative drawdown for each zone. The results indicate that the simulation model is capable of predicting the behavior of the study aquifer and that it may be used as a decision support system. Moreover, both the simulation and the optimization models are found capable of determining water extraction quantities required so that not only will the present water deficit decline but aquifer conditions will improve as well. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Artificial neural network Simulation Scenario Ant system heuristic optimization algorithm

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A Novel Hybrid Method for Planning and Allocation of DGs in Medium Voltage Networks considering Voltage Regulation and Relay Coordination

A Novel Hybrid Method for Planning and Allocation of DGs in ...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Kumar, Dhivya Sampath Srinivasan, Dipti Reindl, Thomas Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore Natl Univ Singapore Solar Energy Res Inst Singapore Singapore Singapore

ISBN: (纸本)9781509006229

The penetration of distribution generation (DGs) sources in the distribution grid is largely limited by various factors that involves feeder losses, power quality, stability and security of the grid. Different types of DGs have different impacts on the grid upon integration. The penetration of inverter-based DGs (IBDG) in the distribution networks are mainly limited due to their heavy impact on the system harmonic levels. Whereas, synchronous based DGs (SBDG) are mainly limited due to their high impact on fault current levels that affect the over-current relay coordination. Placement of existing power system components such as flexible alternating current transmission system (FACTS) devices that are generally used for voltage regulation and reactive power compensation can be suggested as an efficient solution for maximizing the penetration of DGs. This paper suggests a hybrid framework with a heuristic optimization algorithm for planning and allocation of DGs and FACTS devices considering collective grid constraints such as voltage harmonics constraints, power balance constraints, voltage regulation constraints and over-current relay constraints. The proposed approach is tested on the power distribution network of IEEE 30-bus system. Comparative analyses have been conducted to highlight the efficacy of the proposed optimization approach under various test case scenarios.

关键词： Distributed generation FACTS devices heuristic optimization algorithm over-current relay coordination system harmonics

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A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems

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Journal of Modern Power Systems and Clean Energy 2014年第4期2卷 289-297页

作者： Ming NIU Can WAN Zhao XU The Hong Kong Polytechnic University Hong KongChina

Optimal power flow(OPF)is one of the key tools for optimal operation and planning of modern power *** to the high complexity with continuous and discrete control variables,modern heuristic optimization algorithms(HOAs)have been widely employed for the solution of *** paper provides an overview of the latest applications of advanced HOAs in OPF *** most frequently applied HOAs for solving the OPF problem in recent years are covered and briefly introduced,including genetic algorithm(GA),differential evolution(DE),particle swarm optimization(PSO),and evolutionary programming(EP),etc.

关键词： heuristic optimization algorithm Optimal power flow Multi-objective optimization Constraint optimization

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heuristics for the Cost-Effective Management of a Temperature Controlled Environment

Heuristics for the Cost-Effective Management of a Temperatur...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA 2015)

作者： Arikiez, Mohamed Grasso, Floriana Zito, Michele Univ Liverpool Dept Comp Sci Liverpool L69 3BX Merseyside England

ISBN: (纸本)9781509012374

This study investigates the use of linear programming based heuristics for solving particular energy allocation problems. The main objective is to minimize the cost of using a collection of air conditioning units in a residential or commercial building also, keeping the inside temperature within preset comfort levels. Optimal methods do not scale well when the number of appliances or the system time granularity grows past a certain threshold. We find that heuristics based on relaxation and rounding offer a good trade-off between cost and computation time is needed.

关键词： Demand-side management heuristic optimization algorithm home automation and power management mixed integer linear programming smart grid

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