AIMC Topic: Nonlinear Dynamics

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An Approximate Neuro-Optimal Solution of Discounted Guaranteed Cost Control Design.

IEEE transactions on cybernetics
The adaptive optimal feedback stabilization is investigated in this article for discounted guaranteed cost control of uncertain nonlinear dynamical systems. Via theoretical analysis, the guaranteed cost control problem involving a discounted utility ...

Parametric Neural Network-Based Model Free Adaptive Tracking Control Method and Its Application to AFS/DYC System.

Computational intelligence and neuroscience
This paper deals with adaptive nonlinear identification and trajectory tracking problem for model free nonlinear systems via parametric neural network (PNN). Firstly, a more effective PNN identifier is developed to obtain the unknown system dynamics,...

Observer-based adaptive neural tracking control for a class of nonlinear systems with prescribed performance and input dead-zone constraints.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the problem of output feedback neural network (NN) learning tracking control for nonlinear strict feedback systems subject to prescribed performance and input dead-zone constraints. First, an NN is utilized to approximate the ...

Adaptive Fuzzy Tracking Control for a Class of Uncertain Switched Nonlinear Systems With Full-State Constraints and Input Saturations.

IEEE transactions on cybernetics
In this article, an adaptive fuzzy tracking control scheme is developed for a class of uncertain switched nonlinear systems with input saturations and full-state constraints. First to surmount the design difficulty with respect to a saturation nonlin...

Command-filter-based adaptive neural tracking control for a class of nonlinear MIMO state-constrained systems with input delay and saturation.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the problem of adaptive tracking control for a class of nonlinear multi-input and multi-output (MIMO) state-constrained systems with input delay and saturation. During the process of the control scheme, neural network is emplo...

Robust Adaptive Self-Structuring Neural Network Bounded Target Tracking Control of Underactuated Surface Vessels.

Computational intelligence and neuroscience
This paper studies the target-tracking problem of underactuated surface vessels with model uncertainties and external unknown disturbances. A composite robust adaptive self-structuring neural-network-bounded controller is proposed to improve system p...

DSC-based RBF neural network control for nonlinear time-delay systems with time-varying full state constraints.

ISA transactions
The presented control scheme in this paper aims at stabilizing uncertain time-delayed systems requiring all states to change within the preset time-varying constraints. The controller design framework is based on the backstepping method, drastically ...

The New Simulation of Quasiperiodic Wave, Periodic Wave, and Soliton Solutions of the KdV-mKdV Equation via a Deep Learning Method.

Computational intelligence and neuroscience
How to solve the numerical solution of nonlinear partial differential equations efficiently and conveniently has always been a difficult and meaningful problem. In this paper, the data-driven quasiperiodic wave, periodic wave, and soliton solutions o...

A novel design of Gudermannian function as a neural network for the singular nonlinear delayed, prediction and pantograph differential models.

Mathematical biosciences and engineering : MBE
The present work is to solve the nonlinear singular models using the framework of the stochastic computing approaches. The purpose of these investigations is not only focused to solve the singular models, but the solution of these models will be pres...

Adaptive Critic Learning-Based Robust Control of Systems with Uncertain Dynamics.

Computational intelligence and neuroscience
Model uncertainties are usually unavoidable in the control systems, which are caused by imperfect system modeling, disturbances, and nonsmooth dynamics. This paper presents a novel method to address the robust control problem for uncertain systems. T...