Please use this identifier to cite or link to this item:
Advanced Continuous Ant Colony Optimization for Multi-Objective Fuzzy Control with Intelligent Robot Wall-Following Applications
advanced continuous ant colony optimization
multi-objective evolutionary algorithm
intelligent robot wall-following application
|引用:|| C. F. Juang, “Temporal problems solved by dynamic fuzzy network based on genetic algorithm with variable-length chromosomes,” Fuzzy Sets and Systems, vol. 142, no. 2, pp. 199-219, March 2004.  A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, “Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients,” IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 240-255, Jun. 2004.  C. F. Juang, “A hybrid of genetic algorithm and particle swarm optimization for recurrent network design,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 34, no. 2, pp. 997-1006, Apr. 2004.  F. J. Lin, L. T. Teng, J. W. Lin, and S. Y. Chen, “Recurrent functional-link-based fuzzy-neural-network-controlled induction-generator system using improved particle swarm optimization,” IEEE Trans. Ind. Electron., vol. 56, no. 5, pp. 1557-1577, May 2009.  M. E. Aboul-Ela, A.A. Sallam, J. D. McCalley, and A. A. Fouad, “Damping controller design for power system oscillations using global signals,” IEEE Trans. Power Sys., vol. 11, no. 2, pp. 767-773, May 1996.  M. Clerc and J. Kennedy, “The particle swarm - explosion, stability, and convergence in a multidimentional complex space,” IEEE Trans. Evol. Comput., vol. 6, no. 1, pp. 58-73, Feb. 2002.  C. F. Lu and C. F. Juang, “Evolutionary fuzzy control of flexible AC transmission system,” IEE Proc.-Gener. Transm. Distrib, vol. 152, no. 4, pp. 441-448, July 2005.  D. Parrott and X. Li, “Locating and tracking multiple dynamic optima by a particle swarm model using speciation,” IEEE Trans. Evol. Comput., vol. 10, no. 4, pp. 440-458, Aug. 2006.  C. F. Juang, C. M. Hsiao, and C. H. Hsu, “Hierarchical cluster-based multi-species particle swarm optimization for fuzzy system optimization,” IEEE Trans. Fuzzy Systems, vol. 18, no. 1, pp. 14-26, Feb. 2010.  C. F. Juang and Y. C. Chang, “Evolutionary group-based particle swarm-optimized fuzzy controller with application to mobile robot navigation in unknown environments,” IEEE Trans. Fuzzy Syst., vol. 19, no. 2, pp. 379-392, April 2011.  S. Tsutsui, M. Pelican and A. Ghosh, “Performance of aggregation pheromone system on unimodal and multimodal problems,” Proc. IEEE Cong. Evolutionary Computation, pp. 880-887, 2005.  K. Socha and M. Dorigo, “Any colony optimization for continuous domain,” European Journal of Operational Research, vol. 185, pp. 1155-1173, 2008.  C. F. Juang and P. H. Chang, “Designing fuzzy-rule-based systems using continuous ant-colony optimization ,” IEEE Trans. Fuzzy Syst., vol. 18, no. 1, pp. 138-149, Feb. 2010.  R. Storn and K. Price, “Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces,” J. Global Optim., vol. 11, no. 4, pp. 341-359, Dec. 1997.  K. Price, R. Storn, and J. Lampinen, Differential evolution - A Practical Approach to Global Optimization, Berlin, Germany, Springer, 2005.  L. dos S. Coelho and V. C. Mariani, “Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect,” IEEE Trans. Power Syst., vol. 21, no. 2, pp. 989-996, May 2006.  J. Chakraborty, A. Konar, U. K. Chakraborty, and L.C. Jain, “Distributed cooperative multi-robot path planning using differential evolution,” Proc. IEEE Cong. Evol. Comput., pp. 718–715, 2008.  C.H. Chen, C.J. Lin, and C.T. Lin, “Nonlinear system control using adaptive neural fuzzy networks based on a modified differential evolution,” IEEE Trans. Syst., Man, Cyber. C, Appl. Rev., vol. 39, no. 4, pp. 459-473, July 2009.  H. Li, Q. Zhang, E. Tsang and J. A. Ford, “Hybrid estimation of distribution algorithm for multiobjective knapsack problem,” EvoCOP, pp. 145-154, 2004.  E. Zitzler, M. Laumnns, and L. Thiele, “SPEA2: improving the strength pareto evolutionary algorithm,” Computer. Eng. and Common. Network Lab, Swiss Feberal Inst. Techol., Zurich, Tech. Rep. 103, 2011.  K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGAII,” IEEE Trans. Evolutionary Computation, vol. 6, no. 2, April 2002.  C. A. Coello Coello, G. T. Pulido, and M. S. Lechuga, “Handling multiple objectives with particle swarm optimization,” IEEE Trans. Evolutionary Computation, vol. 8, no. 3, pp. 256-279, June 2004.  M. Daneshyari, and G. G. Yen, “Cultural-based multiobjective particle swarm optimization,” IEEE Trans. Syst. Man, and Cybernetics, vol. 41, no. 2, pp. 553-567, April 2011.  S. K. Goudos and J. N. Sahalos, “Pareto Optimal Microwave Filter Design Using Multiobjective Differential Evolution,” IEEE Transactions Antennas And Propagation, vol. 58, no. 1, pp. 132-144, January 2010.  Y. Wang and Z. Cai, “Combining Multiobjective Optimization With Differential Evolution to Solve Constrained Optimization Problems,” IEEE Transactions Evolutionary Computation, vol. 16, no. 1, pp. 117-134, Feburary 2012.  H. Ishibuchi, T. Murata, and I. B. Turksen, “Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems,” Fuzzy Sets Syst., vol. 89, no. 2, pp. 135-150, 1997.  H. Ishibuchi and T. Yamamoto, “Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining,” Fuzzy Sets and Systems, vol. 141, no. 1, pp. 59-88, Jan. 2004.  H. Wang, S. Kwong, Y. Jin, W. Wei, and K. F. Man, “Agent-based evolutionary approach for interpretable rule-based knowledge extraction,” IEEE Trans. Syst., Man, and Cyber. Part C: Applications and Reviews, vol. 35, no. 2, pp. 143-155, 2005.  H. Ishibuchi and Y. Nojima, “Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning,” Int. J. Approx. Reason., vol. 44, no. 1, pp. 4-31, 2007.  M. Cococcioni, P. Ducange, B. Lazzerini, and F. Marcelloni, “A Pareto based multi-objective evolutionary approach to the identification of Mamdani fuzzy systems,” Soft Comput., vol. 11, pp. 1013-1031, 2007.  M. Antonelli, P. Ducange, B. Lazzerini, and F. Marcelloni, “Multi¬objective evolutionary learning of granularity, membership function pa¬rameters and rules of Mamdani fuzzy systems,” Evol. Intell.,vol.2, no. 1/2, pp. 21-37, 2009.  R. Alcala, P. Ducange, F. Herrera, B. Lazzerini, and F. Marcelloni, “A multiobjective evolutionary approach to concurrently learn rule and data bases of linguistic fuzzy-rule-based systems,” IEEE Trans. Fuzzy Syst., vol. 17, no. 5, pp. 1106-1122, Oct. 2009.  P. Pulkkinen and H. Koivisto, “A dynamically constrained multiobjective genetic fuzzy system for regression problems,” IEEE Trans. Fuzzy Systems, vol. 18, no. 1, pp. 161-177, Feb. 2010.  M. Gacto, R. Alcala, and F. Herrera, “Integration of an index to preserve the semantic interpretability in the multi-objective evolutionary rule selection and tuning of linguistic fuzzy systems,” IEEE Trans. Fuzzy Syst., vol. 18, no. 3, pp. 515–531, Jun. 2010.  M. J. Gacto, R. Alcala, and F. Herrera, “Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures,” Information Sciences, vol. 181, pp. 4340-4360, 2011.  R. Alcala, M. J. Gacto, and F. Herrera, “A fast and scalable multiobjective genetic fuzzy system for linguistic fuzzy modeling in high-dimensional regression problems,” IEEE Trans. Fuzzy Syst., vol. 19, no. 4, pp. 666-681, Aug. 2011.  C. F. Hsu and C. F. Juang, “Evolutionary robot wall-following control using type-2 fuzzy controller with species-DE activated continuous ACO,” accepted to be published in IEEE Trans. Fuzzy Systems, 2012.  C. F. Lu, C. H. Hsu, and C. F. Juang, “Coordinated control of flexible AC transmission system devices using an evolutionary fuzzy lead-lag controller with advanced continuous ant colony optimization,” accepted to be published in IEEE Trans. Power Systems, 2012.  K. Socha and M. Dorigo, “Ant colony optimization for continuous domain, ” European Journal of Operational Research, vol. 185, pp. 1155-1173, 2008.  M. Mucientes and J. Casillas, “Quick design of fuzzy controller with good interpretability in mobiles robotics,” IEEE Trans. Fuzzy Syst., vol. 15, no. 4, pp. 636-651, Aug. 2007.  Y. Zhou and M. J. Er, “An evolutionary approach toward dynamic self-generated fuzzy inference systems,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 38, no. 4, pp. 963-969, Aug. 2008.  C. F. Juang and C. H. Hsu, “Reinforcement ant optimized fuzzy controller for mobile-robot wall-following control,” IEEE Trans. Ind. Electron., vol. 56, no. 10, pp. 3931-3940, Oct. 2009.  G. Antonelli, S. Chiaverini, and G. Fusco, “A fuzzy-logic-based approach for mobile robot path tracking,” IEEE Trans. Fuzzy Syst., vol. 15, no. 2, pp. 211-221, Apr. 2007.  C. Y. Tsai and K. T. Song, “Visual tracking control of a wheeled mobile robot with system model and velocity quantization robustness,” IEEE Trans. Cont. Syst. Tech., vol. 17, no. 3, pp. 520-527, May 2009.  A. Zhu and S. X. Yang, “Neurofuzzy-based approach to mobile robot navigation in unknown environments,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 37, no. 4, pp. 610-621, July 2007.  C. Luo and S. X. Yang, “A bioinspired neural network for real-time concurrent map building and complete coverage robot navigation in unknown environments,” IEEE Trans. Neural Networks, vol. 19, no. 7, pp. 1279-1298, July 2008.  H. Hagras, “A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots,” IEEE Trans. Fuzzy Syst., vol. 12, no. 4, pp. 524-539, Aug. 2004.  F. Cupertino, V. Giordano, D. Naso, and L. Delfine, “Fuzzy control of a mobile robot,” IEEE Robot Auto. Mag., vol. 13, no. 4, pp. 74-81, Dec. 2006.  W. Tsui, M. S. Masmoudi, F. Karray, I. Song, and M. Masmoudi, “Soft-computing-based embedded design of an intelligent wall/lane-following vehicle,” IEEE/ASME Trans. on Mech., vol. 13, no. 1, pp. 125-135, Feb. 2008.  M. Mucientes, R. Alcalá, J. Alcalá-Fdez, and J. Casillas, “Learning weighted linguistic rules to control an autonomous robot,” International Journal of Intelligent Systems, vol. 24, no. 3, pp. 226-251, Mar. 2009.  M. J. Er and C. Deng, “Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning,” IEEE Trans. Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 34, no. 3, pp. 1478-1489, June 2004.  C. S. Lee, M. H. Wang, H. Hagras, “A type-2 fuzzy ontology and its application to personal diabetic-diet recommendation,” IEEE Trans. Fuzzy Systems, vol. 18, no. 2, pp. 374-395, April 2010.  C. F. Juang, R. B. Huang, and W. Y. Cheng, “An interval type-2 fuzzy neural network with support vector regression for noisy regression problems,” IEEE Trans. Fuzzy Systems, vol. 18, no. 4, pp. 686-699, Aug. 2010.  S. Barkat, A. Tlemçani, and H. Nouri, “Noninteracting adaptive control of PMSM using interval type-2 fuzzy logic systems,” IEEE Trans. Fuzzy Systems, vol. 19, no. 5, pp. 925-936, Oct. 2011.  M. Dorigo and T. Stützle, Ant colony optimization, MIT, July 2004.  K. Clark, B. Fradanesh, and R. Adapa, “Thyristor-controlled series compensation application study - control interaction considerations,” IEEE Transactions Power Delivery, vol. 10, no. 2, pp. 1031-1037, April 1995.  J. J. Sanchez-Gasca, “Coordinated control of two FACTS devices for damping interarea oscillations,” IEEE Trans. on Power Sys., vol. 13, no. 2, pp.428-434, 1997.  R. Mohan. Mathur, Rajiv K. Varma, Thyristor-Based FACTS Controller For Electrical Transmission System, IEEE Press, Wiley Interscience, 2002.  P. Kundur, J. Paserba, V. Ajjarapu, G. Andersson, A. Bose, C. Canizares, N. Hatziargyiou, D. Hill, A. Stankovic, C. Taylor, T. Cutsem, and V. Vittal, “Definition and classification of power system stability,” IEEE Trans. Power Sys. vol. 19, no. 2, pp. 1387-1401, May 2004.  D. P. He, C. Y. Chung, and Y. Xue, “An eigenstructure-based performance index and its application to control design for damping inter-area oscillations in power systems”, IEEE Trans. on Power Sys., vol. 26, no. 4, pp. 2371-2380, Nov. 2011.  X. Tan, N, Zhang, L. Tong, and Z. Wang, “Fuzzy control of thyristor-controllered series compensator in power system transients,” Fuzzy Sets and Systems, vol. 110, pp.429-436, 2000.  X. Lei, E. N. Lerch, and D. Povh, “Optimization and coordination of damping controls for improving system dynamic performance,” IEEE Trans. Power Sys. vol. 16, no. 3, pp. 473-480, August 2001.  Y. C. Chang, R. F. Chang, T. Y. Hsiao, and C. N. Lu, “Transmission ststem loacability enhancement study by ordinal optimization method,” IEEE Trans. Power Sys. vol. 26, no. 1, pp.451-459, Feb. 2011.  N. Mithulananthan, C. A. Canizares, J. Reeve, and G. J. Rogers, “Comparison of PSS, SVC, and STATCOM controllers for damping power system oscillations, “ IEEE Trans. Power Sys., vol. 18, no. 2, pp. 786-792, May 2003.  U. P. Mhaskar and A. M. Kulkarni, “Power oscillation damping using FACTS devices: model controllabiliby, observability in loacl signals, and location of transfer function zeros,” IEEE Trans. Power Sys., vol. 21, no. 1, pp. 285-294, Feb. 2006.  A. M. Simões, D. C. Savelli, P. C. Pellanda, N. Martins, and P. Apkarian, “Robust design of a TCSC oscillation damping controller in a week 200-kv interconnection considering multiple power flow scenarios and external disturbances,” IEEE Trans. Power Sys., vol. 24, no. 1, February, 2009.  B. Chaudhuri, S. Ray, and R. Majumder, “Robust low-order controller design for multi-modal power oscillation damping using flexible AC transmission system devices,” IET Gener. Transm. Distrib., vol. 3, no. 5, pp. 448-459, 2009.  J. Miguel González, C. A. Cañizares, and J. M. Ramoírez, “Stability modeling and comparative study of series vectorial compensators,” IEEE Trans. Power Delivery, vol. 25, no. 2, pp. 1093-1103, April 2010.  P. K. Dash, S. Morris, and S. Mishra, “Design of a nonlinear variable-gain fuzzy controller for FACTS devices,” IEEE Trans. Control Sys. Technology, vol. 12, no. 3, pp. 428-438, May 2004.  Y. S. Lee, “Decentralized suboptimal control of power systems with superconducting magnetic energy storage units,” International Journal of Power and Energy Systems, vol. 21, no. 2, pp. 87-96, 2001.  J. H. Chow, R. Galarza, P. Accari, and W. W. Price, “Inertial and slow coherency aggregation algorithms for power system dynamic model reduction,” IEEE Trans. Power Sys., vol. 10. no.2, pp. 680-685, May 1995.  I. Kamwa, R. Grondin, and Y. Hebert, “Wide-area measurement based stabilizing control of large power systems - A decentralized/hierarchical approach,” IEEE Trans. Power Syst., vol. 16, pp. 136-153, Feb. 2001.  Y. Zhang and A. Bose, “Design of wide area damping controllers for inter-area oscillations,” IEEE Trans. Power Syst., vol. 23, no.3, pp. 1136-1143, Aug. 2008.  S. Ray and G.K. Venayagamoorthy, “Real-time implementation of a measurement-based adaptive wide area control system considering communication delays,” IET Proc.-Gener. Transm. Distrib, vol. 2, no. 1, pp. 62-70, 2008.  H. Wu, K. S. Tsakalis, and G. T. Heydt, “Evaluation of time delays to wide-are power system stabilizer design” IEEE Trans. Power Syst., vol. 19, no. 4, pp. 1935-1941, Nov. 2004.  J. M. Mendel, Uncertain Rule-based Fuzzy Logic System: Introduction And New Directions, Upper Saddle River, NJ: Prentice-Hall, 2001.  C. F. Juang and Y. W. Tsao, “A self-evolving interval type-2 fuzzy neural network with on-line structure and parameter learning,” IEEE Trans. Fuzzy Syst., vol. 16, no. 6, pp. 1411-1424, Dec. 2008.  E. Zitzler, “Evolutionary algorithms for multiobjective optimization methods and applications, ” Ph. D. dissertation, Swiss Federal Inst. Technol., Zurich, Switzerland, 1999.  C. F. Juang, “A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms, ” IEEE Trans. Fuzzy Syst., vol. 10, no. 2, pp. 155-170, April 2002.  C. F. Juang and C. Lo, “Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence,” Fuzzy Sets Syst., vol. 159, no. 21, pp. 2910-2926, Nov. 2008.  C. F. Juang and C. Y. Wang, “A self-generating fuzzy system with ant and particle swarm cooperative optimization, ” Expert Systems with Applications, vol. 36, no. 3P1, pp. 5362-5370, April 2009.  S. Wu and M. J. Er, “Dynamic fuzzy neural networks - a novel approach to function aproximation, ” IEEE Trans. Syst., Man and Cyber., - Part B: Cybernetics, vol. 30. 358-364, 2000.  D. Kukolj, “Design of adaptive Takagi-Sugeno-Kang fuzzy models,” Applied Soft. Computing vol. 2, no. 2, pp. 89-103, 2002.|
|摘要:||本篇論文提出三種使用不同連續型螞蟻群聚最佳化(CACO)演算法的進化型模糊系統，以避免耗時的專家設計規則法與全面的監督式輸入輸出訓練資料之收集。在提出的CACO演算法中，解的描述和產生是透過包含節點和路徑的圖形來說明。本篇論文第一個提出的演算法為先進型CACO (ACACO)演算法，它使用新穎的螞蟻路徑挑選方案作為新解產生和提高學習性能。ACACO被應用在多機電力系統中彈性交流輸電系統(FACTS)裝置的多目標模糊領先落後控制器設計；其中，多目標函數是透過線性化組合轉成單目標函數來求解。第二個提出的是物種差異進化(SDE)活化CACO (SDE-CACO)演算法，此法將物種差異進化突變運算引入CACO演算法裡，以便改善其性能。論文並將基於SDE-CACO之多目標第二類型模糊控制法應用在實際機器人執行沿牆走任務。論文提出了兩階段學習控制架構以解決多目標機器人控制問題。第三個提出的是多目標規則編碼先進型CACO (MO-RACACO)演算法。不同於上面兩種改良式CACO演算法只找出多目標最佳化問題中的單一解，MO-RACACO可找出Pareto最佳解集合。MO-RACACO同樣地被應用在解決包含多個控制目標的實際機器人沿牆走控制問題上。論文並透過和各式基於群體最佳化演算法之比較來驗證這三種多目標CACO最佳化演算法的性能。|
This dissertation proposes three evolutionary fuzzy systems using different continuous ant colony optimization (CACO) algorithms, which avoids the time-consuming task of rule design by human experts and exhaustive collection of supervised input-output training pairs. Representations and generations of solutions in the proposed CACO algorithms are graphically explained in terms of nodes and path segments. The first algorithm is advanced CACO (ACACO), which uses a novel ant-path selection scheme for new solution generation and learning performance improvement. The ACACO is applied to multi-objective fuzzy lead-lag control of flexible AC transmission system (FACTS) devices in a multi-machine power system (PS), where multi-objective functions are linearly combined into a single objective function. The second one is a Species-Differential-Evolution (SDE) activated CACO (SDE-CACO) algorithm, which introduces a SDE mutation operation into a CACO algorithm for optimization performance improvement. The SDE-CACO is applied to multi-objective type-2 fuzzy control of a real robot performing a wall-following task. A two-stage learning control configuration is proposed to address the multi-objective robot control problem. The third one is a Multi-Objective Rule-Coded Advanced CACO (MO-RACACO) algorithm. Unlike the above two modified CACO algorithms that find a single solution in a multi-objective optimization problem, the MO-RACACO finds Pareto-optimal solutions. The MO-RACACO is also applied to the real robot wall-following control problem with multiple control objectives. Performances of these three optimization algorithms are verified through comparisons with various population-based optimization algorithms.
|Appears in Collections:||電機工程學系所|
Show full item record
TAIR Related Article
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.