4 research outputs found
A New Model-Free Method Combined with Neural Networks for MIMO Systems
In this brief, a model-free adaptive predictive control (MFAPC) is proposed.
It outperforms the current model-free adaptive control (MFAC) for not only
solving the time delay problem in multiple-input multiple-output (MIMO) systems
but also relaxing the current rigorous assumptions for sake of a wider
applicable range. The most attractive merit of the proposed controller is that
the controller design, performance analysis and applications are easy for
engineers to realize. Furthermore, the problem of how to choose the matrix
{\lambda} is finished by analyzing the function of the closed-loop poles rather
than the previous contraction mapping method. Additionally, in view of the
nonlinear modeling capability and adaptability of neural networks (NNs), we
combine these two classes of algorithms together. The feasibility and several
interesting results of the proposed method are shown in simulations
Distributed Event-triggered Bipartite Consensus for Multi-agent Systems Against Injection Attacks
This paper studies fully distributed data-driven problems for nonlinear discrete-time multi-agent systems (MASs) with fixed and switching topologies preventing injection attacks. We first develop an enhanced compact form dynamic linearization model by applying the designed distributed bipartite combined measurement error function of the MASs. Then, a fully distributed event-triggered bipartite consensus (DETBC) framework is designed, where the dynamics information of MASs is no longer needed. Meanwhile, the restriction of the topology of the proposed DETBC method is further relieved. To prevent the MASs from injection attacks, neural network-based detection and compensation schemes are developed. Rigorous convergence proof is presented that the bipartite consensus error is ultimately boundedness. Finally, the effectiveness of the designed method is verified through simulations and experiment