Results 231 to 240 of about 6,421,352 (290)

Model-free network control

Physica D: Nonlinear Phenomena, 2020
Abstract There is an abundance of instances when the state of a complex network needs to be altered to a pre-specified target state, one potential example being the need to alter a disease-induced tissue to a healthy state. Unfortunately, in many such cases, accurate models of the underlying network are unavailable.
Jason Shulman   +2 more
openaire   +1 more source

Event-Triggered Model-Free Adaptive Control

IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021
This paper investigates an event-triggered model-free adaptive control for nonaffined nonlinear systems under a data-driven design framework. By introducing a compact form dynamic linearization (CFDL) scheme, a linear data model of the nonlinear nonaffine system is derived.
Na Lin, Ronghu Chi, Biao Huang
openaire   +1 more source

Neuron model-free PID control

SPIE Proceedings, 2001
Based on the neuron model and learning strategy, the neuron intelligent PID control system is set up in this paper. The neuron model-free PID control method is posed. The simulation tests with an example of a hydraulic turbine generator unit are made. The result show that god control performances are obtained.
Ning Wang, Li Zhang, Shuqing Wang
openaire   +1 more source

Cooperative Adaptive Model-Free Control With Model-Free Estimation and Online Gain Tuning

IEEE Transactions on Cybernetics, 2022
In this article, a distributed adaptive model-free control algorithm is proposed for consensus and formation-tracking problems in a network of agents with completely unknown nonlinear dynamic systems. The specification of the communication graph in the network is incorporated in the adaptive laws for estimation of the unknown linear and nonlinear terms,
openaire   +2 more sources

Model‐free recursive LQ controller design (learning LQ control)

International Journal of Adaptive Control and Signal Processing, 2004
AbstractA new data‐based iterative self‐optimizing approach to practical design (learning/adaptive process) of the infinite‐horizon LQ regulator is proposed. Optimality is given by a certain orthogonality condition of response signals, and the global convergence of feedback gain is proved for MIMO systems by an expansion of the Riccati equation.
Kawamura, Yoshiaki   +2 more
openaire   +2 more sources

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