首页|Deterministic learning from neural control for a class of sampled-data nonlinear systems
Deterministic learning from neural control for a class of sampled-data nonlinear systems
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
This study investigates the deterministic learning and control issues for uncertain sampled-data nonlinear systems (SDNSs). The problem of how to acquire/learn knowledge from adaptive control for SDNSs with uncertain affine terms is studied. To be specific, an appropriate neural network-based (NNB) control strategy is first presented to ensure tracking performance. To further realize learning, the exponential stability (ES) of the integrated closed-loop system coupled with the estimation error of the NN weights is considered. As the uncertain affine term prevents learning from occurring, the integrated system is converted into a discrete linear time-varying (DLTV) perturbed system by employing the state conversion technique. Tracking convergence allows the persistent excitation condition (PEC) of the NNs to be established, which guarantees the ES of the integrated DLTV system. Thus, accurate modeling of closed-loop sampled dynamics is obtained. By reutilizing the experiential knowledge obtained, a knowledge-based controller is constructed for high-performance control. Finally, simulations are performed to verify the presented strategy. (C) 2022 Elsevier Inc. All rights reserved.
Deterministic learningAdaptive neural controlDiscrete-timeNeural networkSampled-data nonlinear systemDISCRETE-TIME-SYSTEMSADAPTIVE NN CONTROLNETWORK CONTROLIDENTIFICATIONCONVERGENCEPERSISTENCYEXCITATION
NSTL
Elsevier
