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Pareto design of an adaptive robust hybrid of PID and sliding control for a biped robot via genetic algorithm optimization

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NONLINEAR DYNAMICS 2015年第1期79卷 251-263页

作者： Taherkhorsandi, M. Mahmoodabadi, M. J. Talebipour, M. Castillo-Villar, K. K. Univ Texas San Antonio Dept Mech Engn San Antonio TX 78249 USA Sirjan Univ Technol Dept Mech Engn Sirjan Iran

In case of helping elder people and doing complicated jobs, biped robots have greater capabilities in comparison to other mobile robots, such as wheeled robots. However, owing to their extremely nonlinear dynamics and natural instability, their motion planning and control become a more challenging and crucial task. Thus, this paper tends to present an adaptive robust hybrid of PID and sliding control optimized by multi-objective genetic algorithm optimization to control a biped robot walking in the lateral plane on slope. More precisely, proportional, integral, and derivative (PID) control is a reliable and stable controller and sliding mode control (SMC) is a robust controller having an appropriate tracking function. To utilize these unique features of each controller, optimal SMC is employed as a supervisory controller to enhance the performance of optimal adaptive PID control and provide sufficient control input. An adaptation mechanism is used to update the proportional, integral, and derivative gains of PID control online. To eliminate the tedious trial-and-error process of determining the control coefficients, multi-objective genetic algorithm optimization is utilized to design and choose the con-trol parameters by an optimal approach. Three points of the Pareto front of the genetic algorithm optimization are selected to design the controller. While the dynamic equations of the biped robot walking in the lateral plane are immensely nonlinear, the control method can operate effectively and the results demonstrate the proper performance of the controller in two aspects of low tracking error and optimal control input.

关键词： Optimal control Adaptive control PID control Sliding mode control Biped robot multi-objective optimization algorithm Genetic algorithm

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The Research on Vector Data Compression Based on multi-objective optimization algorithm

The Research on Vector Data Compression Based on Multi-objec...

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The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

作者： Nihong Wang Changwei Wu Chang Wang Yanrong Zhang College of Information and Computer Engineering Northeast Forestry University Harbin China College of Mechanical and Electrical Engineering Northeast Forestry University Harbin China Department of Mathematics University of Munich Germany College of Engineering Technology Northeast Forestry University Harbin China

ISBN: (纸本)9781424472352

This paper proposes a multi-objective optimization algorithm of vector data compression in the basis of the traditional Douglas-Peucker algorithm and multi-objective optimization algorithm, which gets the optimal matching program of multi-objective optimization through the Pareto optimal set This algorithm also solves the deficiencies of traditional Douglas-Peucker algorithm and makes a good compression effect.

关键词： vector data compression Douglas-Peucker algorithm multi-objective optimization algorithm pareto optimal set

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A Three-phase Large Scale Skyline Service Selection Framework in Clouds

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2016年第4期9卷 223-232页

作者： Li, Jinzhong Zeng, Jintao Peng, Lei Luo, Wenlang Jinggangshan Univ Dept Comp Sci & Technol Jian 343009 Jiangxi Peoples R China Jinggangshan Univ NASG Key Lab Watershed Ecol & Geog Environm Monitoring Jian 343009 Jiangxi Peoples R China Tongji Univ Dept Comp Sci & Technol Shanghai 201804 Peoples R China Tongji Univ Dept Surveying & Geoinformat Shanghai 200092 Peoples R China

For the large scale services with high-dimensional QoS attributes and distributed environment, traditional service selection approaches are faced with unprecedented challenges in terms of efficiency and performance of QoS. To address these challenges, we propose a three-phase large scale Skyline service selection framework for service composition in clouds. This framework adopts distributed parallel Skyline computation with MapReduce to prune redundant candidate services, and employs parallel multi objective optimization algorithm based on MapReduce to select Skyline services from the tremendous amount of Skyline services warehouse for composing single service into a set of more powerful Skyline composite services, then applies Top-k query processing technology or multiple attribute decision making support method to select k Skyline composite services from the set of Skyline composite services. Through theoretical analysis, the framework can efficiently solve the service selection problem with large scale services, high-dimensional QoS in cloud computing environment, and quickly generate better composite services with the global optimal QoS.

关键词： Service Selection MapReduce Skyline multi-objective optimization algorithm Top-k cloud computing

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multi-objective optimal electric power planning in the power system using Gaussian bare-bones imperialist competitive algorithm

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INFORMATION SCIENCES 2015年 294卷 286-304页

作者： Ghasemi, Mojtaba Ghavidel, Sahand Ghanbarian, Mohammad Mehdi Gitizadeh, Mohsen Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Islamic Azad Univ Kazerun Branch Kazerun Iran

In this paper, a Gaussian Bare-bones multi-objective Imperialist Competitive algorithm (GBICA) and its Modified version (MGBICA) are presented for the optimal electric power planning in the electric power system. Two sub-problems of multi-objective optimal electric power planning namely Optimal Power Flow (OPF) and Optimal Reactive Power Dispatch (ORPD) problems are considered. The OPF and ORPD problems are formulated as a nonlinear constrained multi-objective optimization problem with competing objectives. The performance of multi-objective algorithms are studied and evaluated on the standard IEEE 30-bus and IEEE 57-bus test systems. The proposed algorithm provides better results compared with the other algorithms as demonstrated by simulation results. (C) 2014 Elsevier Inc. All rights reserved.

关键词： multi-objective optimal electric power planning Gaussian bare-bones imperialist competitive algorithm multi-objective optimization algorithm

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LFM Hybrid-Coding Waveform Design of MIMO SAR Based on NSGA-II

LFM Hybrid-Coding Waveform Design of MIMO SAR Based on NSGA-...

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7th IEEE International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Liu, Feng Mu, Shanxiang Lv, Wanghan Li, Wenliang Ge, Ting Li, Li Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781467376877

The transmit signal with good performance is the precondition of the excellent multiple-input and multiple-output (MIMO) synthetic aperture radar (SAR). In order to obtain waveforms of MIMO SAR based on linear frequency modulation with higher tolerance of receiver noise, the improved non dominated sorting genetic algorithm II (NSGA-II) with elite strategy is proposed. Application of orthogonal linear frequency modulation hybrid coding (LFMHC) signal to MIMO SAR is analyzed and waveform design is hence converted into multi objective optimization problem. objective function takes into account the signal-to-noise ratio (SNR) of the receiving signal after matched filtering. Orthogonal LFMHC waveform sets are designed. Comprehensive numerical simulation examples are performed. It is shown that the signal waveforms have more flexibility in encoding, with lower autocorrelation side-lobes and cross-correlation interference. They are desirable in separating targets and suppressing noise. The proposed method verifies the algorithm and enables MIMO SAR to operate efficiently in practical applications.

关键词： multiple input and multiple output synthetic aperture radar (MIMO SAR) linear frequency modulation hybrid-coding (LFMHC) non-dominated sorting genetic algorithm II (NSGA-II) multi-objective optimization algorithm

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Mean-variance model for power system economic dispatch with wind power integrated

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ENERGY 2014年 72卷 510-520页

作者： Li, Y. Z. Wu, Q. H. Li, M. S. Zhan, J. P. South China Univ Technol Sch Elect Power Engn Guangzhou 510641 Guangdong Peoples R China Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China

This paper presents the mean-variance (MV) model to solve the power system economic dispatch with wind power integrated, based on the optimal power flow problem. The MV model considers the profit and risk simultaneously under the environment of uncertain wind power, which is formulated as a multi-objective optimization problem. The MGSOMP (multiple-group search optimizer with multiple producers) is proposed to solve the MV model to find Pareto solutions, based on GSOMP (group search optimizer with multiple producers). Then the preference ranking organization method is used for decision making to determine the final dispatch solution. The MV model and MGSOMP are tested on the modified IEEE 30-bus and 118-bus power systems, respectively. Simulation results show that the MV model is well applicable to solve power system dispatch considering wind power integrated, and MGSOMP can obtain more convergent and better diversified Pareto solutions, compared with GSOMP. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Mean-variance model Economic dispatch Wind power multi-objective optimization algorithm Decision making

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Analysis of the Optimal Transmission Capacity and Bundle Proportion of Wind Power Combined with Thermal Power Based on multi-objective optimization Decision-making

Analysis of the Optimal Transmission Capacity and Bundle Pro...

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International Conference on Power System Technology (PowerCon)

作者： Jin Weigang Li Yong Wang Wenda Guo Xiaoyun Liu Huijin State Grid Corp China Cent China Grid Co Ltd Cent China Branch Wuhan 430077 Peoples R China Wuhan Univ Sch Elect Engn Wuhan 430072 Peoples R China

ISBN: (纸本)9781479950324

This paper presents a planning method for thermal power that is combined with wind power to get the optimal transmission capacity and bundle proportion of wind power and thermal power. At first, a comprehensive index system is established which takes into account the power demand, level of safety and stability, economy of the receiving system, transmission price and etc. Then these factors are defined as the objectives of the mathematical model with multi-objective function. The non-dominated sorting genetic algorithm-II (NSGA-II) is applied to solve this multi-objective model. After obtaining the non-inferior solutions of the model, the optimal compromise solution is determined by the fuzzy membership function. The proposed scheme can obtain the optimal transmission capacity and also can guarantee the stability of the receiving power system. The simulation is conducted on the IEEE-9 system and the results verify the validation of the method.

关键词： wind power combined with thermal power transmission capacity comprehensive index system multi-objective optimization algorithm entropy method

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Evolving Hard and Easy Traveling Salesman Problem Instances: A multi-objective Approach

Evolving Hard and Easy Traveling Salesman Problem Instances:...

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10th International Conference on Simulated Evolution and Learning (SEAL)

作者： Jiang, He Sun, Wencheng Ren, Zhilei Lai, Xiaochen Piao, Yong Dalian Univ Technol Sch Software Dalian Peoples R China

ISBN: (纸本)9783319135632;9783319135625

It becomes a great challenge in the research area of metaheuristics to predict the hardness of combinatorial optimization problem instances for a given algorithm. In this study, we focus on the hardness of the traveling salesman problem (TSP) for 2-opt. In the existing literature, two approaches are available to measure the hardness of TSP instances for 2-opt based on the single objective: the efficiency or the effectiveness of 2-opt. However, these two objectives may conflict with each other. To address this issue, we combine both objectives to evaluate the hardness of TSP instances, and evolve instances by a multi-objective optimization algorithm. Experiments demonstrate that the multi-objective approach discovers new relationships between features and hardness of the instances. Meanwhile, this new approach facilitates us to predict the distribution of instances in the objective space.

关键词： TSP 2-opt multi-objective optimization algorithm random forest

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ADRC Decoupling Controller Design and Parameters optimization for Circulating Fluidized Bed Boiler Combustion System

ADRC Decoupling Controller Design and Parameters Optimizatio...

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32nd Chinese Control Conference (CCC)

作者： Chen Shihe Zhang Yuwen Li Donghai Lao Dazhong Guangdong Elect Power Res Inst Guangzhou 510080 Guangdong Peoples R China Beijing Inst Technol Sch Aerosp Engn Beijing 100081 Peoples R China Tsinghua Univ Dept Thermal Engn State Key Lab Power Syst Beijing 100084 Peoples R China

ISBN: (纸本)9789881563835

In this paper, the control of Circulating Fluidized Bed(CFB) combustion system is studied. It is typically a nonlinear, large time delay and coupling system, and is difficult to establish accurate mathematical model, therefore, using conventional control method to solve this control problem becomes very difficult. Since the anti-rejection controller(ADRC) has simple structure, easy to industrial field applications, does not rely on accurate model of plant, it has broad application prospects. In this paper, a decoupling controller is designed for a domestic 75t/h CFB. multi-objective optimization algorithm(Non-dominated sorting Fruit Fly optimization algorithm) is designed for this controller and ITAE, response time as well as maximum of control are selected to be optimized. Comparison between Partially Decentralized Control(PDC) and ADRC shows the ADRC controller has the advantage of simple structure, anti-channel coupling ability, and meanwhile similar controller effect can be obtained.

关键词： Circulating Fluidized Bed Combustion System Active Disturbance Rejection Control(ADRC) Decoupling Control multi-objective optimization algorithm Non-dominated Sorting Fruit Fly optimization algorithm

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multi-objective optimization algorithm analysis on supply chain resources integration decision in 4PL

Multi-objective optimization algorithm analysis on supply ch...

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IEEE International Conference on Automation and Logistics

作者： Liu, Liwen Yao, Hanming Tsinghua Univ Sch Econ & Management Beijing 100084 Peoples R China

ISBN: (纸本)9781424415304

At present, the successful operations of the fourth party logistics (4PL) in practice gradually demonstrate that it is an effective mode to integrate the supply chain's complicated resources rationally, efficiently and flexibly. In order to thoroughly analyze the supply chain resources integration problem in 4PL from a quantitative perspective and to guide the integration practice, based on the previous study results, this paper takes the multi-objective optimization action of the integration decision as an analysis pivot, builds a improved ant algorithm to resolve the integration decision optimization process. Finally, the reasonability and feasibility of the algorithm are validated through the simulation of a calculation case.

关键词： the fourth party logistics supply chain resources integration multi-objective optimization algorithm

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