Adaptive multi-dimensional Taylor network control for nonlinear stochastic systems with time-delay

Zhu, Shuqian; Wang, Mingxin; Han, Yu‐Qun

doi:10.1177/09596518211040285

Cited by 4 publications

(4 citation statements)

References 51 publications

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“…[12][13][14] Among them, the MTN is taken as a new type of NN, which has received a growing number of attention, and many MTN-based adaptive tracking control schemes have been proposed. 15,16 However, the above research results only ensure that the tracking error bounded near the origin rather than asymptotic tracking. In addition, the resources limitation is another difficulty to be overcome when the network as most control systems transmission medium.…”

Section: Introductionmentioning

confidence: 93%

“…As some alternative methods, the approximation‐based adaptive control methods have been reported, such as neural networks (NNs) control, 6‐9 fuzzy logic systems (FLSs) control 10,11 and multi‐dimensional Taylor network (MTN) control 12‐14 . Among them, the MTN is taken as a new type of NN, which has received a growing number of attention, and many MTN‐based adaptive tracking control schemes have been proposed 15,16 . However, the above research results only ensure that the tracking error bounded near the origin rather than asymptotic tracking.…”

Section: Introductionmentioning

confidence: 99%

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Switching threshold‐based event‐triggered adaptive asymptotic tracking control for stochastic nonlinear systems with full‐state constraints

Zhu

Zhai

et al. 2023

Intl J Robust & Nonlinear

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In this article, the problem of event-triggered adaptive asymptotic tracking control (ATC) for stochastic nonlinear systems with unknown control directions (UCDs) and full state constraints is concerned. It must be said that the controller design and system analysis is more complex and difficult since the existence of stochastic disturbances, UCDs and full state constraints simultaneously. By introducing the lower bound of the UCDs into the barrier Lyapunov functions, an event-triggered adaptive MTN ATC scheme is proposed based on the boundary estimation method and a new event-triggered control (ETC) strategy, which can achieve satisfactory asymptotic tracking performance and control performance of the system, while reduce the occupation of network resources. The simulation results not only verify the effectiveness of the proposed control scheme, but also present different tracking performances between three ETC strategies for comparison, further confirming the superiority of the proposed ETC strategy in achieving asymptotic tracking performance.

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Switching threshold‐based event‐triggered adaptive asymptotic tracking control for stochastic nonlinear systems with full‐state constraints

Zhu

Zhai

et al. 2023

Intl J Robust & Nonlinear

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“…is the estimation error weight vector. Substitute Formula (31) into Formula (15) to obtain Assumption 2. The approximating error ε 2 is a constant which is really tiny.…”

Section: ˆˆˆ( ) [ ( ) ( ) ( )]mentioning

confidence: 99%

“…However, these control methods require a multidimensional Taylor network to take into account the requirements of stability, speed and error, and the internal parameter adjustment inevitably has a competitive relationship. In [31] and [32], the authors focus on the role of a multidimensional Taylor network for stochastic nonlinear systems, and in order to make the theory more rigorous, more stringent assumptions are usually required.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Robust Control of an Industrial Motor-Driven Stage with Disturbance Rejection Ability Based on Multidimensional Taylor Network

Sun,

Zhang,

et al. 2023

Applied Sciences

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Linear motors are widely used in practical engineering fields, but it is difficult to achieve accurate position control because of model uncertainty, external interference, input nonlinearity and other factors. In this paper, a multidimensional Taylor network (MTN) controller with flexible learning robustness is proposed to realize tracking control and make the motor have excellent antijamming ability. The controller is composed of a robust feedback part, a parameter adaptive part and a multidimensional Taylor network control part. This strategy has good track tracking performance and anti-interference ability. In this control strategy, the input of the multidimensional Taylor network is determined only by the referable trajectories, and no model information is required. In addition, the designed multidimensional Taylor network can accurately describe the relationship between state variables and any complex disturbance, which is the basis of disturbance suppression. Finally, the stability of the controller is proved by the Lyapunov theorem. The unknown interference comparison experiment and numerical simulation experiment are carried out on the industrial linear motor platform, respectively. Experimental results show that compared with NNPID, the proposed algorithm has smaller overshoot and better performance under various error statistical dimensions.

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Identification of MIMO Nonlinear Gaussian Time-Varying System Based on Multi-Dimensional Taylor Network Multi-Level Approximation

Li,

2024

IJDST

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Aiming at the problems of identification difficulties and low identification accuracy in modelling and identification of multiple-input multiple-output (MIMO) nonlinear Gaussian time-varying systems, this paper proposes an identification scheme based on the step-by-step approximation of multidimensional Taylor network (MTN). The aim of this paper is to improve the modelling of complex nonlinear systems so as to improve the prediction performance and control effect of the system. Different from the traditional multidimensional Taylor network identification method, this method adopts an order-by-order approximation strategy, which seeks its parameters sequentially from the lower order to the higher order, and continuously optimises the parameter weights during the parameter seeking process. Firstly, the nonlinear function model is approximated as a polynomial form by the order-by-order Taylor expansion, and then the weight parameters of each order of the Taylor expansion are calculated and updated step by step by using the algorithm based on the Variable Forgetting Factor Recursive Least Squares (VFF-RLS) method. Through iterative optimized of these parameters, dynamic weight assignment to each order of the Taylor expansion is achieved. A parameter-identified nonlinear function model is finally obtained, which can more accurately describe the dynamic behaviour and characteristics of the system. Finally, an arithmetic simulation is carried out through an example to verify the effectiveness of the proposed method.

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Adaptive multi-dimensional Taylor network control for nonlinear stochastic systems with time-delay

Cited by 4 publications

References 51 publications

Switching threshold‐based event‐triggered adaptive asymptotic tracking control for stochastic nonlinear systems with full‐state constraints

Switching threshold‐based event‐triggered adaptive asymptotic tracking control for stochastic nonlinear systems with full‐state constraints

Adaptive Robust Control of an Industrial Motor-Driven Stage with Disturbance Rejection Ability Based on Multidimensional Taylor Network

Identification of MIMO Nonlinear Gaussian Time-Varying System Based on Multi-Dimensional Taylor Network Multi-Level Approximation

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