Asymptotic Tracking and Dynamic Regulation of MIMO Nonaffine Nonlinear System With Actuator Saturation via Multidimensional Taylor Network Controller

Kang, An-Ming; Yan, Hong‐Sen

doi:10.1109/tsmc.2021.3106314

Cited by 13 publications

(6 citation statements)

References 47 publications

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“…Theorem 4. Consider the stochastic nonlinear discrete-time system (1), along with the adaptive embedding cubature Kalman filter (AECKF) in Algorithm 1, the actor networks (37) and (38), the critic networks (41) and (42). The updating laws of corresponding weights are set as (40) and (46), respectively.…”

Section: Stability Analysismentioning

confidence: 99%

“…Compared with the traditional neural network, MTN is essentially a weighted polynomial of network structure, featuring its convenient adjustment of network structure and good real‐time performance. Its better approximation ability than NN was verified via strict mathematical derivation 40,42,46‐48 …”

Section: Introductionmentioning

confidence: 98%

“…For this sake, we introduce a neoteric network structure called multidimensional Taylor network (MTN). [38][39][40][41][42][43][44][45] The topological structure of MTN is shown in Figure 2. The middle layer of MTN consists of an array of polynomials, which contains only addition and multiplication, and its output layer is the linear weighted combination.…”

Section: Introductionmentioning

confidence: 99%

“…Its better approximation ability than NN was verified via strict mathematical derivation. 40,42,[46][47][48] The present study aims to develop a tractable scheme for effective tracking control of nonlinear discrete-time systems with stochastic disturbance and time-varying delays. An adaptive filter is designed to realize reliable state estimation under stochastic uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…So it is vital to develop a control strategy with desirable real‐time performance and strong robustness for engineering practices. For this sake, we introduce a neoteric network structure called multidimensional Taylor network (MTN) 38‐45 . The topological structure of MTN is shown in Figure 2.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive control for stochastic nonlinear systems with time‐varying delays via multidimensional Taylor network

Yan

Wang

2023

Intl J Robust & Nonlinear

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Control of stochastic nonlinear systems turns out to be notoriously difficult when stochastic uncertainties and time‐varying delays occur simultaneously. This article presents a tractable adaptive control scheme for the stochastic nonlinear system with time‐varying delays. To mitigate the effects of stochastic uncertainties, an adaptive embedded cubature Kalman filter is developed to realize the robust estimation of the state. Unlike the conventional cubature Kalman filter with fixed construction, a semi‐definite programming is designed to adjust the weights of cubature points dynamically. Such programming guarantees the positive definiteness of the error covariance matrix, which enhances the reliability of the filtering procedure. Based on more accurate state estimations, the multidimensional Taylor network (MTN) is utilized to evaluate the dynamic performance under time‐varying delays and approximate the optimal policy in the deterministic policy gradient framework. Adaptive tracking control with high computational efficiency is achieved due to the concise topological structure of MTN. The exponential convergence of the state estimation error and the semi‐globally uniform ultimate boundness of the tracking error are verified theoretically. The effectiveness of the proposed method is confirmed by a numerical simulation based on a practical electric industrial system.

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Section: Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive control for stochastic nonlinear systems with time‐varying delays via multidimensional Taylor network

Yan

Wang

2023

Intl J Robust & Nonlinear

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Adaptive tracking control for a class of stochastic nonlinear systems with input delay using multidimensional Taylor network

Zheng

2023

Asian Journal of Control

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In this paper, an adaptive tracking control strategy based on multidimensional Taylor network (MTN) is designed for a class of stochastic nonlinear systems with input delay, in which MTN is mainly introduced to approximate uncertain nonlinear functions. Moreover, the effect of input delay is compensated by exploiting an appropriate auxiliary system with the same order as the considered system. By utilizing the backstepping technique and classical adaptive approach, an MTN‐based adaptive tracking control method is proposed, which guarantees that all the signals in the closed‐loop system remain bounded in probability. Furthermore, unlike most existing approaches, the tracking error can be bounded by an explicit expression of design parameters in the mean quartic sense. Finally, two examples are given to verify the validity of the presented scheme.

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Composite anti-disturbance predictive control of unmanned systems with time-delay using multi-dimensional Taylor network

LI,

ZHANG

2024

Chinese Journal of Aeronautics

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Asymptotic Tracking and Dynamic Regulation of MIMO Nonaffine Nonlinear System With Actuator Saturation via Multidimensional Taylor Network Controller

Cited by 13 publications

References 47 publications

Adaptive control for stochastic nonlinear systems with time‐varying delays via multidimensional Taylor network

Adaptive control for stochastic nonlinear systems with time‐varying delays via multidimensional Taylor network

Adaptive tracking control for a class of stochastic nonlinear systems with input delay using multidimensional Taylor network

Composite anti-disturbance predictive control of unmanned systems with time-delay using multi-dimensional Taylor network

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