Nonlinear observers based on the well-known concept of minimum energy estimation are discussed. The approach relies on an output injection operator determined by a Hamilton–Jacobi–Bellman equation whose solut
An adaptive finite-time prescribed performance control (FTPPC) strategy is considered based on the time-delay neural network (NN) observer for the uncertain nonlinear system with unknown time-delay. Unlike previous works, a time-delay NN state observer based on the existing NN state observer is ...
An adaptive observer for a class of single-input single-output (SISO) nonlinear systems is proposed using a generalized dynamic recurrent neural network (DRNN). The neural-network (NN) weights are tuned on-line, with no off-line learning required. No exact knowledge of non-linearities in the...
We therefore build a nonlinear classifier aiming to correctly identify the label of the histograms resulting from the acquisition of pulsed laser light backscattered from three different people in seven different positions. We use a supervised approach where we pair the temporal histogram as input to ...
In Reference 21, the authors proposed a single-hidden-layer feedforward network aided fault-tolerant control scheme for a class of nonlinear systems subject to actuator faults. In addition, a sliding mode control approach is presented to allow for prompt corrective reactions, with explicit ...
Zheng, X., Ding, M., Liu, L.et al.Recurrent neural network robust curvature tracking control of tendon-driven continuum manipulators with simultaneous joint stiffness regulation.Nonlinear Dyn112, 11067–11084 (2024). https://doi.org/10.1007/s11071-024-09585-w ...
Deep nonlinear network experiments To measure the sensitivity of pretrained networks, we first downloaded pretrained ResNet18 and VGG16 (with batch normalization) networks from the Torchvision python package (v0.11) distributed with Pytorch. For the vision transformer, we used a distribution in Python...
Due to the uncertainty of wind and because wind energy conversion systems (WECSs) have strong nonlinear characteristics, accurate model of the WECS is difficult to be built. To solve this problem, data-driven control technology is selected and data-driven controller for the WECS is designed base...
Artificial neural networks are black-box models that can be used to model nonlinear dynamical systems. This article presents a synthesis method for full dynamic state feedback controllers and state and output observers that have guaranteed properties for systems approximated by dynamic artificial neural ...
This paper illustrates the benefits of a nonlinear model based predictive control (NMPC) strategy for setpoint tracking control of an industrial crystallization process. A neural networks model is used as internal model to predict process outputs. An optimization problem is solved to compute future co...