AIMC Topic: Nonlinear Dynamics
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Comparison of Deep Learning and Deterministic Algorithms for Control Modeling.
Sensors (Basel, Switzerland)
Aug 24, 2022
Controlling nonlinear dynamics arises in various engineering fields. We present efforts to model the forced van der Pol system control using physics-informed neural networks (PINN) compared to benchmark methods, including idealized nonlinear feedforw...
Synergetic learning structure-based neuro-optimal fault tolerant control for unknown nonlinear systems.
Neural networks : the official journal of the International Neural Network Society
Aug 18, 2022
In this paper, a synergetic learning structure-based neuro-optimal fault tolerant control (SLSNOFTC) method is proposed for unknown nonlinear continuous-time systems with actuator failures. Under the framework of the synergetic learning structure (SL...
Reinforcement-Learning-Based Disturbance Rejection Control for Uncertain Nonlinear Systems.
IEEE transactions on cybernetics
Aug 18, 2022
This article investigates the reinforcement-learning (RL)-based disturbance rejection control for uncertain nonlinear systems having nonsimple nominal models. An extended state observer (ESO) is first designed to estimate the system state and the tot...
Runtime Safety Monitoring of Neural-Network-Enabled Dynamical Systems.
IEEE transactions on cybernetics
Aug 18, 2022
Complex dynamical systems rely on the correct deployment and operation of numerous components, with state-of-the-art methods relying on learning-enabled components in various stages of modeling, sensing, and control at both offline and online levels....
Fuzzy Multiple Hidden Layer Recurrent Neural Control of Nonlinear System Using Terminal Sliding-Mode Controller.
IEEE transactions on cybernetics
Aug 18, 2022
This study designs a fuzzy double hidden layer recurrent neural network (FDHLRNN) controller for a class of nonlinear systems using a terminal sliding-mode control (TSMC). The proposed FDHLRNN is a fully regulated network, which can be simply conside...
Event-Triggered Adaptive Neural Control for Fractional-Order Nonlinear Systems Based on Finite-Time Scheme.
IEEE transactions on cybernetics
Aug 18, 2022
This article addresses the finite-time event-triggered adaptive neural control for fractional-order nonlinear systems. Based on the backstepping technique, a novel adaptive event-triggered control scheme is proposed, and finite-time stability criteri...
Event-Triggered ADP for Tracking Control of Partially Unknown Constrained Uncertain Systems.
IEEE transactions on cybernetics
Aug 18, 2022
An event-triggered adaptive dynamic programming (ADP) algorithm is developed in this article to solve the tracking control problem for partially unknown constrained uncertain systems. First, an augmented system is constructed, and the solution of the...
Dissipativity-Based Intermittent Fault Detection and Tolerant Control for Multiple Delayed Uncertain Switched Fuzzy Stochastic Systems With Unmeasurable Premise Variables.
IEEE transactions on cybernetics
Aug 18, 2022
This study focuses on dissipativity-based fault detection for multiple delayed uncertain switched Takagi-Sugeno fuzzy stochastic systems with intermittent faults and unmeasurable premise variables. Nonlinear dynamics, exogenous disturbances, and meas...
Deep Learning for Robust Adaptive Inverse Control of Nonlinear Dynamic Systems: Improved Settling Time with an Autoencoder.
Sensors (Basel, Switzerland)
Aug 9, 2022
An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converg...
Remote State Estimation of Nonlinear Systems Over Fading Channels via Recurrent Neural Networks.
IEEE transactions on neural networks and learning systems
Aug 3, 2022
In this article, we consider the remote state estimation for nonlinear dynamic systems with known linear dynamics and unknown nonlinear perturbations. The nonlinear dynamic plant is monitored by multiple distributed sensors over a random access wirel...
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