|Appears in Collections:||Computing Science and Mathematics eTheses|
|Title:||Intelligent methods for complex systems control engineering|
|Author(s):||Abdullah, Rudwan Ali Abolgasim|
Neural networks modelling
Fuzzy logic supervisor
Minimum variance control
|Publisher:||University of Stirling|
|Citation:||A. Zayed, A. Hussain and R. Abdullah, A Novel Multiple-Controller Incorporating a Radial Basis Function Neural Network based Generalized Learning Model, Neurocomputing (Elsevier Science), 69 (16), 1868-1881, 2006.|
R. Abdullah, A. Hussain and M. Polycarpou, Fuzzy Logic based Switching and Tuning Supervisor for a Multivariable Multiple-Controller, IEEE International conference on Fuzzy Systems (FUZZ-IEEE 2007), 1644-1649, Imperial College, London, UK, 23-26 July, 2007.
R. Abdullah, A. Hussain, K. Warwick and A. Zayed, Autonomous Intelligent Vehicle Control using a Novel Multiple-Controller Framework Incorporating Fuzzy-Logic based Switching and Tuning, Neurocomputing (Elsevier Science), 70, in press, 2007.
|Abstract:||This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions.|
|Type:||Thesis or Dissertation|
|Affiliation:||School of Natural Sciences|
Computing Science and Mathematics
|RudwanAbdullah_PhD_Thesis_2007.pdf||2.18 MB||Adobe PDF||View/Open|
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