Journal ArticleDOI
Asymptotic tracking and dynamic regulation of SISO non-linear system based on discrete multi-dimensional Taylor network
Hong-Sen Yan,An-Ming Kang +1 more
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TLDR
This study deals with the problem of asymptotic tracking and dynamic regulation of single-input single output (SISO) non-linear systems via output feedbacks by the discrete multi-dimensional Taylor network (MTN) controller, a novel controller with fixed structure and sampled-data control mechanism.Abstract:
For non-linear control, it is important to secure a generally structured controller that promises wide application and desirable performance. This study deals with the problem of asymptotic tracking and dynamic regulation of single-input single output (SISO) non-linear systems via output feedbacks by the discrete multi-dimensional Taylor network (MTN) controller, a novel controller with fixed structure and sampled-data control mechanism. For verification of its validity, differential geometry and polynomial approximation are adopted. Using the emulation technique and regional pole assignment, the asymptotic tracking and dynamic regulation without online optimisation of the system by discrete MTN controller is tested. With the dynamic change of error signals, the dynamic regulation by given index is realised. As a convex optimisation problem, the controller parameters can be acquired by parametric learning. Based on the delta operator model, the procedure of the controller design is given in detail. Simulation results confirm the feasibility and effectiveness of the proposed approach.read more
Citations
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Journal ArticleDOI
A Novel Neural-Network-Based Adaptive Control Scheme for Output-Constrained Stochastic Switched Nonlinear Systems
TL;DR: A novel neural-network (NN)-based adaptive tracking controller design method is presented for the single-input/single-output nonlinear stochastic switched systems in lower triangular structures with an output constraint and it is proved that both controllers can assure all the signals in the closed-loop remain bounded in probability.
Journal ArticleDOI
Adaptive multi-dimensional Taylor network tracking control for SISO uncertain stochastic non-linear systems
Yu-Qun Han,Hong-Sen Yan +1 more
TL;DR: It is proved that the proposed controller can guarantee that all signals of the closed-loop system remain bounded in probability, and the tracking error converges to an arbitrarily small neighbourhood around the origin.
Journal ArticleDOI
Observer-based multi-dimensional Taylor network decentralised adaptive tracking control of large-scale stochastic nonlinear systems
Yu-Qun Han,Hong-Sen Yan +1 more
TL;DR: It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded in probability, and the tracking errors converge to an arbitrarily small neighbourhood around the origin in the sense of mean quartic value.
Journal ArticleDOI
Stability analysis and dynamic regulation of multi-dimensional Taylor network controller for SISO nonlinear systems with time-varying delay.
An-Ming Kang,Hong-Sen Yan +1 more
TL;DR: Feedback linearization, Lyapunov-Razumikhin theorem and polynomial approximation theorem are employed here to verify that the multi-dimensional Taylor network (MTN) controller can stabilize the single input single output (SISO) nonlinear time-varying delay systems through dynamic regulation of the system output with no need for on-line optimization.
Journal ArticleDOI
Tube-Based Model Predictive Control Using Multidimensional Taylor Network for Nonlinear Time-Delay Systems
Hong-Sen Yan,Zheng-Yi Duan +1 more
TL;DR: A tube-based MPC consisting of MPC and control contraction metric (CCM) controller is proposed and a variational formulation multidimensional Taylor network (MTN) is constructed as the basis function to search for the minimal geodesic.
References
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Barrier Lyapunov Functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints
Yan-Jun Liu,Shaocheng Tong +1 more
TL;DR: An adaptive control technique is developed for a class of uncertain nonlinear parametric systems and it is proved that all the signals in the closed-loop system are global uniformly bounded and the tracking error is remained in a bounded compact set.
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Sufficient conditions for stabilization of sampled-data nonlinear systems via discrete-time approximations
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Induction of fuzzy rules and membership functions from training examples
Tzung-Pei Hong,Chai-Ying Lee +1 more
TL;DR: This paper proposes a general learning method as a framework for automatically deriving membership functions and fuzzy if-then rules from a set of given training examples to rapidly build a prototype fuzzy expert system.
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Fuzzy Approximation-Based Adaptive Backstepping Optimal Control for a Class of Nonlinear Discrete-Time Systems With Dead-Zone
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