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Adaptive Fuzzy Control Applied to Seven-Link Biped Robot Using ant colony optimization algorithm

IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2019年第4期43卷 797-811页

作者： Aldair, Ammar A. Rashid, Abdulmuttalib T. Rashid, Mofeed T. Alsaedee, Eman Badee Univ Basrah Elect Engn Dept Basrah Iraq Univ Dhi Qar Dept Elect & Elect Engn Nasiriyah Iraq

Referring to the fact that the nth links bipedal walking robot has high nonlinearity and uncertainty parameters;therefore robust controllers for walking robot should be properly designed. This paper proposes a new robust control scheme based on fuzzy system and ant colony optimization algorithm. Adaptive fuzzy controllers (AFCs) based on ant colony optimization (ACO) algorithm are proposed to eliminate the chattering phenomenon that occurs when the walking robot moves on rough surfaces. Six rotational joints are used to connect the seven links of the bipedal walking robot. Those joints are assumed frictionless and actuated by six independent servomotors. Therefore, six adaptive fuzzy controllers are designed in this work (one for each joint). To design robust fuzzy controllers, the ant colony optimization algorithm is utilized to tune and find the best parameters of the output membership function of the fuzzy controller. For comparison purposes, optimal PID controllers (OPIDCs) are designed with optimal parameters that are chosen by utilizing the ant colony algorithm. The performances of the two proposed controllers (AFCs and OPIDCs) are tested under significant disturbances situations such as carrying different weighted things by the walking biped robot. In addition, the stability of the adaptive fuzzy controller is studied and proved by applying Lyapunov theory.

关键词： Seven-link biped robot Adaptive fuzzy control Optimal PID control ant colony optimization algorithm Nonlinear system

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An ant colony optimization algorithm for Knight's Tour Problem on the chessboard with holes

An Ant Colony Optimization Algorithm for Knight's Tour Probl...

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1st International Workshop on Education Technology and Computer Science

作者： Jiang Delei Bai Sen Dong Wenming Chongqing Commun Inst Image Commuicat Lab Chongqing Chongqing 400035 Peoples R China

ISBN: (纸本)9780769535579

Knight's tour problem (KTP) on the chessboard with holes is an especial but interest tour problem. We improve the basic ant colony optimization,and introduce more better pheromone resource update and sharing mechanism,, and find its tour path solutions on the chessboard with holes with the help of colonies sharing the information resource by their intercommunion. We have used the algorithm to encumerate the knight's tours on different chessboard with holes, and made the particular analysis for produce. Experiment results show that the algorithm not only produce the path solutions quickly on the chessboard with holes, but also is easy to control the path starting square and path style.

关键词： KTP chessboard with holes ant colony optimization algorithm muti-ant-colony optimization algorithm

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An ant colony optimization algorithm based on Scheduling Preference for Maximizing Working Time of Wireless Sensor Networks 16

An Ant Colony Optimization Algorithm based on Scheduling Pre...

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17th Genetic and Evolutionary Computation Conference (GECCO)

作者： Liu, Yu Chen, Wei-Neng Hu, Xiao-Min Zhang, Jun Sun Yat Sen Univ Sch Adv Comp Guangzhou 510275 Guangdong Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China Collaborat Innovat Ctr High Performance Comp Beijing Peoples R China Minist Educ Engn Res Ctr Supercomp Engn Software Beijing Peoples R China Sun Yat Sen Univ Sch Publ Hlth Guangzhou 510275 Guangdong Peoples R China

ISBN: (纸本)9781450334723

With the proliferation of wireless sensor networks (WSN), the issues about how to schedule all the sensors in order to maximize the system's working time have been in the spotlight. Inspired by the promising performance of ant colony optimization (ACO) in solving combinational optimization problem, we attempt to apply it in prolonging the life time of WSN. In this paper, we propose an improved version of ACO algorithm to get solutions about selecting exact sensors to accomplish the covering task in a reasonable way to preserve more energy to maintain longer active time. The methodology is based on maximizing the disjoint subsets of sensors, in other words, in every time interval, choosing which sensor to sustain active state must be rational in certain extent. With the aid of pheromone and heuristic information, a better solution can be constructed in which pheromone denotes the previous scheduling experience, while heuristic information reflects the desirable device assignment. Orderly sensor selection is designed to construct an advisable subset for coverage task. The proposed method has been successfully applied in solving limited energy assignment problem no matter in homogenous or heterogeneous WSNs. Simulation experiments have shown it has a good performance in addressing relevant issues.

关键词： ant colony optimization algorithm Wireless Sensors Network (WSN) maximize working time schedule

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Research of Path Planning for Mobile Robot based on Improved ant colony optimization algorithm

Research of Path Planning for Mobile Robot based on Improved...

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2nd IEEE International Conference on Advanced Computer Control

作者： Zhao Juan-ping Liu Jin-gang Univ Chem Technol Coll Informat Engn Shenyang Shenyang Peoples R China Northeastern Univ Coll Informat Sci & engn Shenyang Peoples R China Shenyang Xinghua Aero Elect Appliances Co Ltd Lia Shenyang Peoples R China

ISBN: (纸本)9781424458479

The paper proposes an improved ant colony optimization algorithm. This method first designs two fuzzy controllers to optimize three parameters alpha, beta, rho. Then it establishes a dynamic searching window for ants and chaos information are added when near-neighbour city table is constituted in order to increase research speed in initial stages of algorithm. In addition, the concept of active degree of city node is presented as future information to supervise ants to construct solution and update pheromone. Finally a new evaluation criterion is produced to distinguish where paths are excellent or not. So the strategy not only conquers the weakness of easily running into local optimization while making route optimization, but also enhances efficient convergence of ant colony optimization algorithm. Results of large numbers of computer simulations demonstrate that this novel algorithm can plan optimal path rapidly in intricate three dimension (3-D) environment.

关键词： Mobile Robot Path Planning ant colony optimization algorithm Fuzzy Control Dynamic Window Parameters optimization

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Passenger Capacity Prediction Based on Least Squares Support Vector Regression with Continuous ant colony optimization algorithm

Passenger Capacity Prediction Based on Least Squares Support...

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International Conference on Information System and Artificial Intelligence (ISAI)

作者： Li, Dong-Ping Kunming Univ Inst Informat Sci & Technol Kunming 650214 Peoples R China

ISBN: (纸本)9781509015856

In order to solve the shortcoming of support vector regression, passenger capacity prediction based on least squares support vector regression with ant colony optimization algorithm is proposed in this paper. ant colony optimization algorithm is used to select the parameters of least squares support vector regression. Highway passenger capacity data of Anhui province from 2000 to 2011 are applied to testify the passenger capacity forecasting performance of ACO-LSSVR, among which the passenger capacity data from 2000 to 2009 are used to create the training samples and the passenger capacity from 2010 to 2011 are used as the testing samples. The testing results show that the forecasting ability for highway passenger capacity of ACO-LSSVR is better than those of LSSVR and SVR.

关键词： Highway passenger capacity Prediction technology ant colony optimization algorithm Least squares support vector regression

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Implementation of Robotic Path Planning using ant colony optimization algorithm

Implementation of Robotic Path Planning using Ant Colony Opt...

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IEEE International Conference on Inventive Computation Technologies (ICICT)

作者： Joshy, Parvathy Supriya, P. Amrita Univ Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Elect & Elect Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781509012855

Mobile robot path planning is critical in the present day of automation. Several situations may occur for humans, like the environment may be dirty, hazardous, might cause death, or injury as in case of mining, detecting leakage in pipe, cleaning of pipe etc. where robots can be successfully employed. The idea of this paper is to develop a mobile robot that finds the shortest route from source to destination by using ant colony optimization algorithm with a single robot. The hardware used is iRobot Create interfaced to NXP LPC1768 Cortex M3 controller. The same is simulated using MATLAB. The output of the hardware is also made visible in Teraterm. It is observed that the mobile robot is enhanced with considerable skills to trace a path of optimum distance from source to destination without any collision in most of the situations, barring a few.

关键词： ant colony optimization algorithm Pheromones

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Wireless Networked Control Systems with neural networks and ant colony optimization algorithm 37

Wireless Networked Control Systems with neural networks and ...

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37th Chinese Control Conference (CCC)

作者： Xu, Qiang Yang, Jia Chongqing Technol & Business Univ Coll Comp Sci & Informat Engn Chongqing 400067 Peoples R China Chongqing Univ Technol Sch Elect & Elect Engn Chongqing 400054 Peoples R China

ISBN: (纸本)9789881563958

Due to Wireless network delay and large delay time of the controlled plant, the performance of the wireless networked control systems (WNCS) is deteriorates. In order to effectively eliminate the influence of network delay and large delay time, a novel structure of the WNCS is proposed that new Smith predictor combined with neural network prediction and identification model. Nonlinear system's delay time must be well identified. The abrupt mutation result from the training error sum square of the real output and the expected output of the neural network was used to identify the delay time. The input sample period of the neural network was changed so that it could discriminate the delay time of the nonlinear model. The discriminated large time-delay was applied to neural network prediction model. The errors between input and prediction model output were used to search PID controller parameters based on ant colony optimization algorithm. The method was applied to control boiler combustion system. The simulation results show that this scheme has much better advantage of celerity and robustness. This is the paper style requirement for the Chinese Control Conference. The writers of papers should and must provide normalized electronic documents in order for readers to search and read papers conveniently.

关键词： Large delay time Neural network prediction model ant colony optimization algorithm Wireless networked control systems (WNCS) Network delay Smith predictor

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A Logistic Distribution Routes Solving Strategy Based on the Physarum Network and ant colony optimization algorithm 17

A Logistic Distribution Routes Solving Strategy Based on the...

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2015 IEEE 17th International Conference on High Performance Computing and Communications (HPCC)

作者： Chen, Qianguo Qian, Tao Liu, Kui Chongqing Technol & Business Univ Ctr Teaching & Learning Dev Chongqing Peoples R China Bank Chongqing Software Dev Ctr Chongqing Peoples R China Southwest Univ Expt Teaching Ctr Int Studies Chongqing Peoples R China

ISBN: (纸本)9781479989379

The Physarum Network with single inlet and multi outlet model (SMPN) exhibits a unique feature that the critical pipelines are reserved with the evolution of network. In addition, ant colony optimization algorithm is a classic optimization algorithm of simulated evolutionary algorithms, which has been used to solve optimal scheduling problems. In this paper, drawing on this feature, an optimized ant colony optimization (ACO) algorithm denoted as SMPNACO algorithm is proposed based on the Physarum Network and ant colony optimization algorithm (ACO) to solve the Vehicle Routing Problem (VRP). Throughout the algorithm, the amount of pheromone flowed in network are related to the customers' requirement. When the pheromone matrix is updated, the SMPNACO algorithm updates both the pheromone released by ants and the flowing pheromone in the Physarum Network. By adding extra pheromones in the Physarum Network improves the convergence performance of ant colony optimization algorithm. The simulative experiments show that the SMPNACO algorithm is less affected by the initial total pheromone, this algorithm is feasible in solving the small scale VRP, and can effectively solve the VRP.

关键词： Physarum Network model ant colony optimization algorithm Logistics distribution routes optimization VRP

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A Task Scheduling algorithm Based on Genetic algorithm and ant colony optimization algorithm with Multi-QoS Constraints in Cloud Computing 7

A Task Scheduling Algorithm Based on Genetic Algorithm and A...

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7th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Dai, Yangyang Lou, Yuansheng Lu, Xin Hohai Univ Coll Comp & Informat Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781479986460

Task scheduling problem in cloud computing environment is NP-hard problem, which is difficult to obtain exact optimal solution and is suitable for using intelligent optimization algorithms to approximate the optimal solution. Meanwhile, quality of service (QoS) is an important indicator to measure the performance of task scheduling. In this paper, a novel task scheduling algorithm MQoS-GAAC with multi-QoS constraints is proposed, considering the time-consuming, expenditure, security and reliability in the scheduling process. The algorithm integrates ant colony optimization algorithm (ACO) with genetic algorithm (GA). To generate the initial pheromone efficiently for ACO, GA is invoked. With the designed fitness function, 4-dimensional QoS objectives are evaluated. Then, ACO is utilized to seek out the optimum resource. The experiment indicates that the proposed algorithm has preferable performance both in balancing resources and guaranteeing QoS.

关键词： task scheduling cloud computing genetic algorithm ant colony optimization algorithm QoS

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Research on Routing algorithm for Time-Sensitive Networks Based on ant colony optimization algorithm 6

Research on Routing Algorithm for Time-Sensitive Networks Ba...

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6th International Conference on Natural Language Processing (ICNLP)

作者： Yang, Yanzhu Yang, Wujun Chang, Zhixian Xian Univ Posts & Telecommun Dept Sch Commun & Informat Engn Xian Peoples R China

ISBN: (纸本)9798350349122;9798350349115

Time-Sensitive Network (TSN) is a type of deterministic Ethernet that can satisfy the need for deterministic transmission of data in many industries. TSN provides bounded delay for Time Triggered (TT) communication by providing global scheduling of transmission messages. Therefore, to address the problem of delay optimization for path selection of TT flows in industrial TSN, this paper puts forward a kind of oriented industrial TT flows routing multipath time delay difference of perception of transport mechanism. A multipath forwarding metric is designed based on the sending delay and processing delay, and then the multipath forwarding problem is transformed into a 0-1 Integer Linear Programming (ILP) problem. Finally, based on the ant colony optimization (ACO) algorithm, the refined algorithm (RACO) offers reliable deterministic end-to-end delay guarantees for TT flows in the industrial Internet. Through a large number of simulation experiments and analysis of the experimental results, this scheme realizes the low-latency parallel transmission of flows and achieves lower end-to-end delay, with faster convergence speed and stronger global optimization capability, which can more effectively avoid the possibility of falling into a stagnant state.

关键词： time-sensitive network time-triggered flow path selection strategy ant colony optimization algorithm

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