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Ant colony optimization algorithm and its application to neuro-fuzzy controller design

Journal of Systems Engineering and Electronics 2007年第3期18卷 603-610页

作者： Zhao Baojiang Li Shiyong Dept. of Control Science and Engineering Harbin Inst. of Technology Harbin 150001 P.R.China Dept. of Mathematics Mudanjiang Teachers Coll. Mudanjiang 157012 P.R. China

An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information. The algorithm can keep good balance between accelerating convergence and averting precocity and stagnation. The results of function optimization show that the algorithm has good searching ability and high convergence speed. The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum. In order to avoid the combinatorial explosion of fuzzy rules due tσ multivariable inputs, a state variable synthesis scheme is employed to reduce the number of fuzzy rules greatly. The simulation results show that the designed controller can control the inverted pendulum successfully.

关键词： neuro-fuzzy controller ant colony algorithm function optimization genetic algorithm inverted pen-dulum system.

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Optimal Design of neuro-fuzzy controller Based on Ant Colony Algorithm

Optimal Design of Neuro-Fuzzy Controller Based on Ant Colony...

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第二十九届中国控制会议

作者： ZHAO Baojiang Department of Mathematics,Mudanjing Teachers College,Mudanjing Heilongjiang 157012,P.R.China

An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of the trail information *** algorithm can keep good balance between accelerating convergence and averting precocity and *** results of function optimization show that the algorithm has good searching ability and high convergence *** algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted *** order to avoid the combinatorial explosion of fuzzy rules due to multivariable inputs,state variable synthesis scheme is employed to reduce the number of fuzzy rules *** simulation results show that the designed controller can control the inverted pendulum successfully.

关键词： neuro-fuzzy controller Ant Colony Algorithm Function Optimization Genetic Algorithm Inverted Pendulum System.

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neuro-fuzzy and model-based motion control for mobile manipulator among dynamic obstacles

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Science in China(Series F) 2003年第1期46卷 14-30页

作者：魏武 Jean Bosco Mbede 黄心汉张毅 Department of Automation Tsinghua University Departement des Genies Industrie Universit de Yaound I Ecole Nationals Superieure Polytechnique Department of Control Science and Engineering Huazhong University of Science and Technology

This paper focuses on autonomous motion control of a nonholonomic platform with a robotic arm, which is called mobile manipulator. It serves in transportation of loads in imperfectly known industrial environments with unknown dynamic obstacles. A union of both procedures is used to solve the general problems of collision-free motion. The problem of collision-free motion for mobile manipulators has been approached from two directions, Planning and Reactive Control. The dynamic path planning can be used to solve the problem of locomotion of mobile platform, and reactive approaches can be employed to solve the motion planning of the arm. The execution can generate the commands for the servo-systems of the robot so as to follow a given nominal trajectory while reacting in real-time to unexpected events. The execution can be designed as an Adaptive fuzzy Neural controller. In real world systems, sensor-based motion control becomes essential to deal with model uncertainties and unexpected obstacles.

关键词： autonomous mobile manipulators dynamic obstacle avoidance dynamic path planning model and sensor-based control neuro-fuzzy controller nonholonomic and redundant systems.

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