%0 Journal Article %1 Akbarzadeh-T:2000:CEE %A Akbarzadeh-T., M.-R. %A Kumbla, K. %A Tunstel, E. %A Jamshidi, M. %D 2000 %J Computers and Electrical Engineering %K Articial Fuzzy Neural Robotic Soft algorithms, computing, control, genetic intelligence logic, networks, programming, %N 1 %P 5--32 %T Soft computing for autonomous robotic systems %U http://citeseer.ist.psu.edu/373353.html %V 26 %X Neural networks (NN), genetic algorithms (GA), and genetic programming (GP) are augmented with fuzzy logic-based schemes to enhance artificial intelligence of automated systems. Such hybrid combinations exhibit added reasoning, adaptation, and learning ability. In this expository article, three dominant hybrid approaches to intelligent control are experimentally applied to address various robotic control issues which are currently under investigation at the NASA Center for Autonomous Control Engineering. The hybrid controllers consist of a hierarchical NN-fuzzy controller applied to a direct drive motor, a GA-fuzzy hierarchical controller applied to position control of a flexible robot link, and a GP-fuzzy behavior based controller applied to a mobile robot navigation task. Various strong characteristics of each of these hybrid combinations are discussed and used in these control architectures. The NN-fuzzy architecture takes advantage of NN for handling complex data patterns, the GA-fuzzy architecture uses the ability of GA to optimize parameters of membership functions for improved system response, and the GP-fuzzy architecture uses the symbolic manipulation capability of GP to evolve fuzzy rule-sets.

@article{Akbarzadeh-T:2000:CEE, abstract = {Neural networks (NN), genetic algorithms (GA), and genetic programming (GP) are augmented with fuzzy logic-based schemes to enhance artificial intelligence of automated systems. Such hybrid combinations exhibit added reasoning, adaptation, and learning ability. In this expository article, three dominant hybrid approaches to intelligent control are experimentally applied to address various robotic control issues which are currently under investigation at the NASA Center for Autonomous Control Engineering. The hybrid controllers consist of a hierarchical NN-fuzzy controller applied to a direct drive motor, a GA-fuzzy hierarchical controller applied to position control of a flexible robot link, and a GP-fuzzy behavior based controller applied to a mobile robot navigation task. Various strong characteristics of each of these hybrid combinations are discussed and used in these control architectures. The NN-fuzzy architecture takes advantage of NN for handling complex data patterns, the GA-fuzzy architecture uses the ability of GA to optimize parameters of membership functions for improved system response, and the GP-fuzzy architecture uses the symbolic manipulation capability of GP to evolve fuzzy rule-sets.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Akbarzadeh-T., M.-R. and Kumbla, K. and Tunstel, E. and Jamshidi, M.}, biburl = {https://www.bibsonomy.org/bibtex/2a15f5f76b6ad24688883cfd7b46d6e92/brazovayeye}, interhash = {7962eff4aa396edb19dd26b7fa784986}, intrahash = {a15f5f76b6ad24688883cfd7b46d6e92}, journal = {Computers and Electrical Engineering}, keywords = {Articial Fuzzy Neural Robotic Soft algorithms, computing, control, genetic intelligence logic, networks, programming,}, notes = {citeseer 373353 version not identical to published version}, number = 1, pages = {5--32}, size = {28 pages}, timestamp = {2008-06-19T17:35:23.000+0200}, title = {Soft computing for autonomous robotic systems}, url = {http://citeseer.ist.psu.edu/373353.html}, volume = 26, year = 2000 }