Neurodynamic approaches to model predictive control.

Pan, Yunpeng.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (p. 98-107).Abstract also in Chinese.Abstract --- p.ip.iiiAcknowledgement --- p.ivChapter 1 --- Introduction --- p.2Chapter 1.1 --- Model Predictive Control --- p.2Chapter 1.2 --- Neural Networks --- p.3Chapter 1.3 --- Existing studies --- p.6Chapter 1.4 --- Thesis structure --- p.7Chapter 2 --- Two Recurrent Neural Networks Approaches to Linear Model Predictive Control --- p.9Chapter 2.1 --- Problem Formulation --- p.9Chapter 2.1.1 --- Quadratic Programming Formulation --- p.10Chapter 2.1.2 --- Linear Programming Formulation --- p.13Chapter 2.2 --- Neural Network Approaches --- p.15Chapter 2.2.1 --- Neural Network Model 1 --- p.15Chapter 2.2.2 --- Neural Network Model 2 --- p.16Chapter 2.2.3 --- Control Scheme --- p.17Chapter 2.3 --- Simulation Results --- p.18Chapter 3 --- Model Predictive Control for Nonlinear Affine Systems Based on the Simplified Dual Neural Network --- p.22Chapter 3.1 --- Problem Formulation --- p.22Chapter 3.2 --- A Neural Network Approach --- p.25Chapter 3.2.1 --- The Simplified Dual Network --- p.26Chapter 3.2.2 --- RNN-based MPC Scheme --- p.28Chapter 3.3 --- Simulation Results --- p.28Chapter 3.3.1 --- Example 1 --- p.28Chapter 3.3.2 --- Example 2 --- p.29Chapter 3.3.3 --- Example 3 --- p.33Chapter 4 --- Nonlinear Model Predictive Control Using a Recurrent Neural Network --- p.36Chapter 4.1 --- Problem Formulation --- p.36Chapter 4.2 --- A Recurrent Neural Network Approach --- p.40Chapter 4.2.1 --- Neural Network Model --- p.40Chapter 4.2.2 --- Learning Algorithm --- p.41Chapter 4.2.3 --- Control Scheme --- p.41Chapter 4.3 --- Application to Mobile Robot Tracking --- p.42Chapter 4.3.1 --- Example 1 --- p.44Chapter 4.3/2 --- Example 2 --- p.44Chapter 4.3.3 --- Example 3 --- p.46Chapter 4.3.4 --- Example 4 --- p.48Chapter 5 --- Model Predictive Control of Unknown Nonlinear Dynamic Sys- tems Based on Recurrent Neural Networks --- p.50Chapter 5.1 --- MPC System Description --- p.51Chapter 5.1.1 --- Model Predictive Control --- p.51Chapter 5.1.2 --- Dynamical System Identification --- p.52Chapter 5.2 --- Problem Formulation --- p.54Chapter 5.3 --- Dynamic Optimization --- p.58Chapter 5.3.1 --- The Simplified Dual Neural Network --- p.59Chapter 5.3.2 --- A Recursive Learning Algorithm --- p.60Chapter 5.3.3 --- Convergence Analysis --- p.61Chapter 5.4 --- RNN-based MPC Scheme --- p.65Chapter 5.5 --- Simulation Results --- p.67Chapter 5.5.1 --- Example 1 --- p.67Chapter 5.5.2 --- Example 2 --- p.68Chapter 5.5.3 --- Example 3 --- p.76Chapter 6 --- Model Predictive Control for Systems With Bounded Uncertainties Using a Discrete-Time Recurrent Neural Network --- p.81Chapter 6.1 --- Problem Formulation --- p.82Chapter 6.1.1 --- Process Model --- p.82Chapter 6.1.2 --- Robust. MPC Design --- p.82Chapter 6.2 --- Recurrent Neural Network Approach --- p.86Chapter 6.2.1 --- Neural Network Model --- p.86Chapter 6.2.2 --- Convergence Analysis --- p.88Chapter 6.2.3 --- Control Scheme --- p.90Chapter 6.3 --- Simulation Results --- p.91Chapter 7 --- Summary and future works --- p.95Chapter 7.1 --- Summary --- p.95Chapter 7.2 --- Future works --- p.96Bibliography --- p.9

Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation

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Autonomous Autorotation of a Tilt-Rotor Aircraft Using Model Predictive Control

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Design of an adaptive neural predictive nonlinear controller for nonholonomic mobile robot system based on posture identifier in the presence of disturbance

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