Title :
A neurodynamic approach to model predictive control of piecewise linear systems
Author :
Yan, Zheng ; Wang, Jun
Author_Institution :
Dept. of Mech. & Autom. Eng., Chinese Univ. of Hong Kong, Hong Kong, China
Abstract :
This paper presents a neurodynamic approach to model predictive control (MPC) of constrained piecewise linear systems. A novel procedure for estimating uncertain system parameters of piecewise linear systems is proposed. The model predictive control problem is then formulated as a quadratic optimization problem. To realize the real-time optimization in MPC, a one-layer recurrent neural network is employed for solving the quadratic optimization problem during each sampling interval. The overall MPC approach is of low computational complexity. Simulation results are included to substantiate the effectiveness and usefulness of the proposed approach.
Keywords :
computational complexity; linear systems; neurocontrollers; parameter estimation; piecewise linear techniques; predictive control; quadratic programming; recurrent neural nets; sampling methods; uncertain systems; MPC approach; computational complexity; constrained piecewise linear systems; model predictive control; neurodynamic approach; one-layer recurrent neural network; quadratic optimization problem; sampling interval; uncertain system parameter estimation; Biological neural networks; Linear systems; Neurodynamics; Optimization; Predictive control; Recurrent neural networks; Vectors;
Conference_Titel :
Intelligent Control and Information Processing (ICICIP), 2012 Third International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4577-2144-1
DOI :
10.1109/ICICIP.2012.6391405
