Robust Model Predictive Control of Nonlinear Systems With Unmodeled Dynamics and Bounded Uncertainties Based on Neural Networks

• Published in: IEEE transaction on neural networks and learning systems Vol. 25; no. 3; pp. 457 - 469
• Main Authors: Yan, Zheng, Wang, Jun
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2014; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptative systems; Algorithms; Applied sciences; Artificial Intelligence; Artificial neural networks; Computer science; control theory; systems; Computer Simulation; Control theory. Systems; Dynamical systems; Exact sciences and technology; Extreme learning machine (ELM); Humans; Learning; Learning and adaptive systems; Mathematical models; Modelling and identification; Models, Neurological; Neural networks; Neural Networks (Computer); Nonlinear Dynamics; Nonlinear systems; Nonlinearity; Optimal control; Optimization; real-time optimization; recurrent neural networks (RNNs); robust model predictive control (MPC); Robustness; Time Factors; Uncertainty; unmodeled dynamics; Vectors; Extreme learning machine; recurrent neural networks (RNNs); Non linear control; Dynamical system; Jacobi matrix; Modeling; Convex programming; Optimization; Model predictive control; Minimum time; Uncertain system; Feedforward neural nets; Unmodeled dynamics; Efficiency; Minimax problem; System identification; Incomplete information; Dynamic model; Learning algorithm; Linearization; Mathematical programming; Extreme learning machine (ELM); Recurrent neural nets; real-time optimization; Neural network; Non linear system; Real time; Neurophysiology; Robust control; Supervised learning; Residue; Non linear model; Minimax method; Discrete time; Artificial intelligence; Usability; robust model predictive control (MPC)

This paper presents a neural network approach to robust model predictive control (MPC) for constrained discrete-time nonlinear systems with unmodeled dynamics affected by bounded uncertainties. The exact nonlinear model of underlying process is not precisely known, but a partially known nominal mode...