Journal ArticleDOI
Adaptive Fuzzy Control for Nonstrict-Feedback Systems With Input Saturation and Output Constraint
Qi Zhou,Lijie Wang,Chengwei Wu,Hongyi Li,Haiping Du +4 more
- Vol. 47, Iss: 1, pp 1-12
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TLDR
An adaptive fuzzy control approach for a category of uncertain nonstrict-feedback systems with input saturation and output constraint is presented, and the simulation results reveal the effectiveness of the proposed approach.Abstract:
This paper presents an adaptive fuzzy control approach for a category of uncertain nonstrict-feedback systems with input saturation and output constraint. A variable separation approach is introduced to overcome the difficulty arising from the nonstrict-feedback structure. The problem of input saturation is solved by introducing an auxiliary design system, and output constraint is handled by utilizing a barrier Lyapunov function. Combing fuzzy logic system with the adaptive backstepping technique, the semi-global boundedness of all variables in the closed-loop systems is guaranteed, and the tracking error is driven to the origin with a small neighborhood. The stability of the closed-loop systems is proved, and the simulation results reveal the effectiveness of the proposed approach.read more
Citations
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Journal ArticleDOI
Direct Neural Network Adaptive Tracking Control for Uncertain Non-Strict Feedback Systems With Nonsymmetric Dead-Zone
Rui Wang,Liyun Zhao,Fusheng Yu +2 more
TL;DR: In this paper, a direct adaptive alleviating tracking control algorithm is presented for a class of non-strict feedback uncertain nonlinear systems, where both nonlinear uncertainties and nonsymmetric dead-zone inputs are considered.
Journal ArticleDOI
Intelligent Retrieval Method of Approximate Painting in Digital Art Field
Jixin Wan,Yu Xiaobo +1 more
Journal ArticleDOI
Deterministic learning-based neural control for output-constrained strict-feedback nonlinear systems.
TL;DR: In this article , an adaptive neural control of output-constrained strict-feedback uncertain nonlinear systems is proposed to overcome the constraint restriction and achieve learning from the closed-loop control process.
Journal ArticleDOI
A Deep Motion Reliability Scheme for Robotic Operations
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Practical Tracking Control via Switching for Uncertain Nonlinear Systems With Both Dead-Zone Input and Output Constraint
TL;DR: In this article , a switching controller is designed with some pivotal controller parameters being tuned by a smart switching mechanism, which guarantees that all the states of the resulting closed-loop system are bounded while system output practically tracks the reference signal.
References
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Book
Adaptive Fuzzy Systems and Control: Design and Stability Analysis
TL;DR: This paper presents a meta-analysis of the design and stability analysis of fuzzy identifiers of nonlinear dynamic systems fuzzy adaptive filters of adaptive fuzzy controllers using input-output linearization concepts.
Journal ArticleDOI
Barrier Lyapunov Functions for the control of output-constrained nonlinear systems
TL;DR: This paper presents control designs for single-input single-output (SISO) nonlinear systems in strict feedback form with an output constraint, and explores the use of an Asymmetric Barrier Lyapunov Function as a generalized approach that relaxes the requirements on the initial conditions.
Journal ArticleDOI
Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form
TL;DR: A backstepping based control design for a class of nonlinear systems in strict-feedback form with arbitrary uncertainty is developed and is able to eliminate the problem of "explosion of complexity" inherent in the existing method.
Journal ArticleDOI
Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints
TL;DR: The auxiliary design system is introduced to analyze the effect of input constraints, and its states are used to adaptive tracking control design, and the closed-loop semi-global uniformly ultimate bounded stability is achieved via Lyapunov synthesis.
Journal ArticleDOI
Adaptive Neural Control for Output Feedback Nonlinear Systems Using a Barrier Lyapunov Function
TL;DR: A barrier Lyapunov function (BLF) is introduced to address two open and challenging problems in the neuro-control area: for any initial compact set, how to determine a priori the compact superset on which NN approximation is valid; and how to ensure that the arguments of the unknown functions remain within the specified compact supersets.
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