Adaptive Multi-Dimensional Taylor Network Tracking Control for a Class of Stochastic Nonlinear Systems With Unknown Input Dead-Zone

Han, Yu‐Qun; Zhu, Shuqian; Yang, Shuguo

doi:10.1109/access.2018.2849511

Cited by 16 publications

(17 citation statements)

References 43 publications

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“…On the other hand, many approximation-based adaptive control schemes, such as multi-dimensional Taylor network (MTN) (Han, 2018a; Han et al, 2018; Han and Yan, 2018a; Kang and Yan, 2018; Yan and Kang, 2017), neural networks (NNs) (Han, 2018b; Han et al, 2019a; Wang and Huang, 2005; Wang et al, 2013b; Yin et al, 2017) and fuzzy logic systems (FLSs) (Chen et al, 2009; Zhou et al, 2011), have been found to be particularly useful for the control of nonlinear systems. Among the existing research results, MTN-backstepping-based adaptive control schemes have been found to be particularly suitable for solving the control problems of nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

“…Although MTN-based adaptive control approaches for nonlinear systems (Han, 2018b; Han et al, 2019b) and stochastic nonlinear systems (Han, 2018a; Han and Yan, 2018a, 2018b) have been developed, they only investigated the nonlinear systems without considering the case of input constraint. In the work of Han et al (2018), the authors investigated the problem of adaptive tracking control for a class of stochastic nonlinear systems with input dead-zone using MTN. However, the mentioned above control schemes cannot solve the tracking control problems of switched nonlinear systems with input nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive multi-dimensional Taylor network tracking control for a class of switched nonlinear systems with input nonlinearity

Zhu

Duan

Chu

et al. 2020

Transactions of the Institute of Measurement and Control

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In this paper, a multi-dimensional Taylor network (MTN)-based adaptive tracking control approach is proposed for a class of switched nonlinear systems with input nonlinearity. Firstly, the input nonlinearity is assumed to be bounded by a sector interval. Secondly, with the help of MTNs approximating the unknown nonlinear functions, a novel adaptive MTN control scheme has the advantages of low cost, simple structure and real time feature is developed via backstepping technique. It is shown that the tracking error finally converges to a small domain around the origin and all signals in the closed-loop system are bounded. Finally, two examples are given to demonstrate the effectiveness of the proposed control scheme.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive multi-dimensional Taylor network tracking control for a class of switched nonlinear systems with input nonlinearity

Zhu

Duan

Chu

et al. 2020

Transactions of the Institute of Measurement and Control

Self Cite

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show abstract

“…Then, an MTN tracking control scheme is proposed for a class of stochastic nonlinear systems with unknown input dead-zone [37]. And it is investigated the problem of adaptive MTN control for SISO uncertain stochastic non-linear systems [38].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Nonlinear Near-Optimal Regulation Based on Multi-Dimensional Taylor Network Dynamic Programming

et al. 2020

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Using the data-driven control formulation, an iterative dynamic programming approach which is based on a multi-dimensional Taylor network is established to design the near optimal regulation of discrete-time nonlinear systems. For discrete-time general nonlinear systems, the iterative adaptive dynamic programming algorithm is developed and proved to guarantee the property of convergence and optimality. Three networks are constructed, namely, the identification network, critic network and action network. Moreover, a globalized dual heuristic programming technique with detailed implementation is developed. The cost function and its derivative can be approximated by this novel architecture. Besides, without the consideration of the system dynamics, this technique can learn the near-optimal control law simultaneously and adaptively. In addition, this technique greatly improves the existing results of the iterative adaptive dynamic programming algorithm in terms of reducing the requirement of the control matrix. Furthermore, because of the approach that is based on the multi-dimensional Taylor network, the amount of calculation needed is also greatly reduced. The simulation experiment is described to illustrate the effectiveness of the data-driven optimal regulation method proposed in this paper. INDEX TERMS Adaptive dynamic programming, data-driven control, multi-dimensional Taylor network, nonlinear control.

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“…The proportional–integral–derivative (PID) controller is found to be a special case of the MTN controller (MTNC), and its parameters can be taken as the initial ones of the latter, as illustrated in Section 5. This model is commonly applied for model prediction [13], system identification [14], disaster prediction [15], motor control [16] and non‐linear control [17–22]. However, Yan et al [18] provided only a basic idea of the MTNC.…”

Section: Introductionmentioning

confidence: 99%

“…More importantly, the convergence and faster learning have not been guaranteed, the optimal learning rates have not been identified and the computational complexity has not been considered. In [22], the MTNs were used to approximate the non‐linearities, and then, an adaptive MTNC is constructed via backstepping technique. Wherein, only the approximation of MTN was discussed.…”

Section: Introductionmentioning

confidence: 99%

Identification and adaptive multi‐dimensional Taylor network control of single‐input single‐output non‐linear uncertain time‐varying systems with noise disturbances

Zhang

Yan

2019

IET Control Theory & Applications

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Adaptive Multi-Dimensional Taylor Network Tracking Control for a Class of Stochastic Nonlinear Systems With Unknown Input Dead-Zone

Cited by 16 publications

References 43 publications

Adaptive multi-dimensional Taylor network tracking control for a class of switched nonlinear systems with input nonlinearity

Adaptive multi-dimensional Taylor network tracking control for a class of switched nonlinear systems with input nonlinearity

Data-Driven Nonlinear Near-Optimal Regulation Based on Multi-Dimensional Taylor Network Dynamic Programming

Identification and adaptive multi‐dimensional Taylor network control of single‐input single‐output non‐linear uncertain time‐varying systems with noise disturbances

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