Pan, Yunpeng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (p. 98-107). / Abstract also in Chinese. / Abstract --- p.i / p.iii / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.2 / Chapter 1.1 --- Model Predictive Control --- p.2 / Chapter 1.2 --- Neural Networks --- p.3 / Chapter 1.3 --- Existing studies --- p.6 / Chapter 1.4 --- Thesis structure --- p.7 / Chapter 2 --- Two Recurrent Neural Networks Approaches to Linear Model Predictive Control --- p.9 / Chapter 2.1 --- Problem Formulation --- p.9 / Chapter 2.1.1 --- Quadratic Programming Formulation --- p.10 / Chapter 2.1.2 --- Linear Programming Formulation --- p.13 / Chapter 2.2 --- Neural Network Approaches --- p.15 / Chapter 2.2.1 --- Neural Network Model 1 --- p.15 / Chapter 2.2.2 --- Neural Network Model 2 --- p.16 / Chapter 2.2.3 --- Control Scheme --- p.17 / Chapter 2.3 --- Simulation Results --- p.18 / Chapter 3 --- Model Predictive Control for Nonlinear Affine Systems Based on the Simplified Dual Neural Network --- p.22 / Chapter 3.1 --- Problem Formulation --- p.22 / Chapter 3.2 --- A Neural Network Approach --- p.25 / Chapter 3.2.1 --- The Simplified Dual Network --- p.26 / Chapter 3.2.2 --- RNN-based MPC Scheme --- p.28 / Chapter 3.3 --- Simulation Results --- p.28 / Chapter 3.3.1 --- Example 1 --- p.28 / Chapter 3.3.2 --- Example 2 --- p.29 / Chapter 3.3.3 --- Example 3 --- p.33 / Chapter 4 --- Nonlinear Model Predictive Control Using a Recurrent Neural Network --- p.36 / Chapter 4.1 --- Problem Formulation --- p.36 / Chapter 4.2 --- A Recurrent Neural Network Approach --- p.40 / Chapter 4.2.1 --- Neural Network Model --- p.40 / Chapter 4.2.2 --- Learning Algorithm --- p.41 / Chapter 4.2.3 --- Control Scheme --- p.41 / Chapter 4.3 --- Application to Mobile Robot Tracking --- p.42 / Chapter 4.3.1 --- Example 1 --- p.44 / Chapter 4.3/2 --- Example 2 --- p.44 / Chapter 4.3.3 --- Example 3 --- p.46 / Chapter 4.3.4 --- Example 4 --- p.48 / Chapter 5 --- Model Predictive Control of Unknown Nonlinear Dynamic Sys- tems Based on Recurrent Neural Networks --- p.50 / Chapter 5.1 --- MPC System Description --- p.51 / Chapter 5.1.1 --- Model Predictive Control --- p.51 / Chapter 5.1.2 --- Dynamical System Identification --- p.52 / Chapter 5.2 --- Problem Formulation --- p.54 / Chapter 5.3 --- Dynamic Optimization --- p.58 / Chapter 5.3.1 --- The Simplified Dual Neural Network --- p.59 / Chapter 5.3.2 --- A Recursive Learning Algorithm --- p.60 / Chapter 5.3.3 --- Convergence Analysis --- p.61 / Chapter 5.4 --- RNN-based MPC Scheme --- p.65 / Chapter 5.5 --- Simulation Results --- p.67 / Chapter 5.5.1 --- Example 1 --- p.67 / Chapter 5.5.2 --- Example 2 --- p.68 / Chapter 5.5.3 --- Example 3 --- p.76 / Chapter 6 --- Model Predictive Control for Systems With Bounded Uncertainties Using a Discrete-Time Recurrent Neural Network --- p.81 / Chapter 6.1 --- Problem Formulation --- p.82 / Chapter 6.1.1 --- Process Model --- p.82 / Chapter 6.1.2 --- Robust. MPC Design --- p.82 / Chapter 6.2 --- Recurrent Neural Network Approach --- p.86 / Chapter 6.2.1 --- Neural Network Model --- p.86 / Chapter 6.2.2 --- Convergence Analysis --- p.88 / Chapter 6.2.3 --- Control Scheme --- p.90 / Chapter 6.3 --- Simulation Results --- p.91 / Chapter 7 --- Summary and future works --- p.95 / Chapter 7.1 --- Summary --- p.95 / Chapter 7.2 --- Future works --- p.96 / Bibliography --- p.97
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_326833 |
| Date | January 2009 |
| Contributors | Pan, Yunpeng., Chinese University of Hong Kong Graduate School. Division of Mechanical and Automation Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, viii, 107 p. : ill. ; 30 cm. |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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