AIMC Topic: Nonlinear Dynamics

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Deep learning for centre manifold reduction and stability analysis in nonlinear systems.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Bifurcations cause large qualitative and quantitative changes in the dynamics of nonlinear systems with slowly varying parameters. These changes most often are due to modifications that occur in a low-dimensional subspace of the overall system dynami...

A Novel Mean-Field-Game-Type Optimal Control for Very Large-Scale Multiagent Systems.

IEEE transactions on cybernetics
In this article, a decentralized adaptive optimal controller based on the emerging mean-field game (MFG) and self-organizing neural networks (NNs) has been developed for multiagent systems (MASs) with a large population and uncertain dynamics. This d...

Resilient Adaptive Neural Control for Uncertain Nonlinear Systems With Infinite Number of Time-Varying Actuator Failures.

IEEE transactions on cybernetics
Existing studies on adaptive fault-tolerant control for uncertain nonlinear systems with actuator failures are restricted to a common result that only system stability is established. Such a result of not being asymptotically stable is a tradeoff pai...

A Bound Estimation Approach for Adaptive Fuzzy Asymptotic Tracking of Uncertain Stochastic Nonlinear Systems.

IEEE transactions on cybernetics
The adaptive fuzzy tracking control problems for a class of uncertain stochastic nonlinear systems are investigated in this article using the backstepping control approach. Different from the existing research, the crucial but highly restrictive hypo...

Design of Cooperative Output Regulators for Heterogeneous Uncertain Nonlinear Multiagent Systems.

IEEE transactions on cybernetics
Cooperative output regulation (COR) of multiagent systems having heterogeneous uncertain nonlinear dynamics is often challenging because of the complex system dynamics and the coupling among agents. This article develops an adaptive internal model-ba...

Disturbance Observer-Based Minimum Entropy Control for a Class of Disturbed Non-Gaussian Stochastic Systems.

IEEE transactions on cybernetics
In this article, a novel control algorithm is developed for a class of nonlinear stochastic systems subject to multiple disturbances, including exogenous dynamic disturbance and general non-Gaussian noise. An observer is designed to estimate the exog...

Dam deformation forecasting using SVM-DEGWO algorithm based on phase space reconstruction.

PloS one
A hybrid model integrating chaos theory, support vector machine (SVM) and the difference evolution grey wolf optimization (DEGWO) algorithm is developed to analyze and predict dam deformation. Firstly, the chaotic characteristics of the dam deformati...

Robust adaptive fault detection and diagnosis observer design for a class of nonlinear systems with uncertainty and unknown time-varying internal delay.

ISA transactions
This paper introduces a novel robust adaptive fault detection and diagnosis (FDD) observer design approach for a class of nonlinear systems with parametric uncertainty, unknown system fault and time-varying internal delays. The conditions for the exi...

Adaptive 2-bits-triggered neural control for uncertain nonlinear multi-agent systems with full state constraints.

Neural networks : the official journal of the International Neural Network Society
This paper investigates an adaptive 2-bits-triggered neural control for a class of uncertain nonlinear multi-agent systems (MASs) with full state constraints. Considering the limitations of practical physical devices and operating conditions, MASs ma...

Integrating nonlinear analysis and machine learning for human induced pluripotent stem cell-based drug cardiotoxicity testing.

Journal of tissue engineering and regenerative medicine
Utilizing recent advances in human induced pluripotent stem cell (hiPSC) technology, nonlinear analysis and machine learning we can create novel tools to evaluate drug-induced cardiotoxicity on human cardiomyocytes. With cardiovascular disease remain...