Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/91644
Title: | Particle swarm optimization for nonlinear model predictive control |
Authors: | Mercieca, Julian Fabri, Simon G. |
Keywords: | Neural networks (Computer science) Adaptive control systems Nonlinear systems Swarm intelligence Computational intelligence Nonlinear control theory Predictive control Artificial intelligence |
Issue Date: | 2011 |
Publisher: | IARIA |
Citation: | Mercieca, J., & Fabri, S. G. (2011). Particle swarm optimization for nonlinear model predictive control. Proc. ADVCOMP, 88-93. |
Abstract: | The paper proposes two Nonlinear Model Predictive Control schemes that uncover a synergistic relationship between on-line receding horizon style computation and Particle Swarm Optimization, thus benefiting from both the performance advantages of on-line computation and the desirable properties of Particle Swarm Optimization. After developing these techniques for the unconstrained nonlinear optimal control problem, the entire design methodology is illustrated by a simulated inverted pendulum on a cart, and compared with a particular numerical linearization technique exploiting conventional convex optimization methods. This is then extended to input constrained nonlinear systems, offering a promising new paradigm for nonlinear optimal control design. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/91644 |
ISBN: | 9781612081724 |
Appears in Collections: | Scholarly Works - FacEngSCE |
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