Echo state network based predictive control with particle swarm optimization for pneumatic muscle actuator

DOI10.1016/j.jfranklin.2016.05.004zbMath1347.93126OpenAlexW2400328397MaRDI QIDQ326156

Songhyok Ri, Yongji Wang, Jin Qian, Lei Liu, Jian Huang, Cai Hua Xiong

Publication date: 12 October 2016

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2016.05.004

zbMATH Keywords

predictive controlparticle swarm optimizationecho state neural network (ESNN)learning convergence theorempneumatic muscle actuatorsingle-layer neural network (SNN)trajectory tracking control

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Neural networks for/in biological studies, artificial life and related topics (92B20) Application models in control theory (93C95) Least squares and related methods for stochastic control systems (93E24)

Related Items

Nonlinear model-predictive control with disturbance rejection property using adaptive neural networks, Broad echo state network for multivariate time series prediction, Legendre cooperative PSO strategies for trajectory optimization

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