Abstract
As optimization of parameters affects prediction accuracy and generalization ability of support vector regression (SVR) greatly and the predictive model often mismatches nonlinear system model predictive control, a multi-step model predictive control based on online SVR (OSVR) optimized by multi-agent particle swarm optimization algorithm (MAPSO) is put forward. By integrating the online learning ability of OSVR, the predictive model can self-correct and adapt to the dynamic changes in nonlinear process well.
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Foundation item: the National Natural Science Foundation of China (No. 60905066), and the Natural Science Foundation of Chongqing (No. cstc2018jcyjA0667)
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Tang, X., Liu, N., Wan, Y. et al. Multi-Step Model Predictive Control Based on Online Support Vector Regression Optimized by Multi-Agent Particle Swarm Optimization Algorithm. J. Shanghai Jiaotong Univ. (Sci.) 23, 607–612 (2018). https://doi.org/10.1007/s12204-018-1990-1
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DOI: https://doi.org/10.1007/s12204-018-1990-1
Key words
- online support vector regression (OSVR)
- model predictive controller (MPC)
- multi-agent particle swarm optimization (MAPSO)
- nonlinear systems