Tube-Based Model Predictive Control Using Multidimensional Taylor Network for Nonlinear Time-Delay Systems

Yan, Hong‐Sen; Duan, Zheng-Yi

doi:10.1109/tac.2020.3005674

Cited by 46 publications

(19 citation statements)

References 41 publications

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“…Remark It should be mentioned that the structure of MTN has been proposed in [27, 32] Obviously, the MTN has a form similar to radial basis function NN (RBFNN), and the difference between them is the middle layer of network. Compared with RBFNN, MTN has a concise structure, and the hidden layer only contains addition and multiplication.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

“…It was first proposed in [23,24] to solve the nonlinear predictive control problem. Since then, this technique has been successfully generalized to nonlinear systems [25,26], nonlinear time-delay system [27][28][29], nonlinear systems with deadzone output [30], and switched nonlinear systems with input nonlinearity [31]. Recently, several MTN-based adaptive control schemes were proposed for SISO stochastic nonlinear systems [32,33], large-scale stochastic nonlinear systems [34,35] and stochastic nonlinear systems with input constraints [36,37].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive control of a class of stochastic nonlinear systems with full state constraints and input saturation using multi‐dimensional Taylor network

Han

2021

Asian Journal of Control

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In the paper, a new control algorithm to realize the tracking control for a class of stochastic nonlinear systems with both full state constraints and input saturation is put forward using multi-dimensional Taylor network (MTN) approach. By introducing a continuous function, the input saturation is converted into a linear model with bounded error. With the help of approximation properties of MTN, by proposing barrier Lyapunov functions (BLFs) and involving the idea of state-constrained control into backstepping technique, a simple and efficient control scheme is developed. The proposed control scheme can guarantee the system states fall in the given constrained bounds as well as keeping the resulting control system stable. The effectiveness of the proposed control method is demonstrated through two examples.

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Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive control of a class of stochastic nonlinear systems with full state constraints and input saturation using multi‐dimensional Taylor network

Han

2021

Asian Journal of Control

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“…MTN and MTN controllers have a much simpler structure than neural networks, its middle layer consisting of an array of polynomials that contains only addition and multiplication so that they have the characteristic of real-time performance owing to their low computation complexity. Meanwhile, they have been found to be particularly useful for the nonlinear modeling (Lin et al , 2014; Li and Yan, 2018) and control (Yan and Kang, 2017; Kang and Yan, 2018; Zhang and Yan, 2020; Yan and Duan, 2021). However, unknown time-delay has not been taken into account by the above research studies.…”

Section: Introductionmentioning

confidence: 99%

MTN-based recursive d-step-ahead predictive control of MIMO nonlinear systems with unknown input time-delay in industrial process

Yan

Li²

2022

Self Cite

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Purpose This paper aims to provide a precise tracking control scheme for multi-input multi-output “MIMO” nonlinear systems with unknown input time-delay in industrial process. Design/methodology/approach The predictive control scheme based on multi-dimensional Taylor network (MTN) model is proposed. First, for the unknown input time-delay, the cross-correlation function is used to identify the input time-delay through just the input and output data. And then, the scheme of predictive control is designed based on the MTN model. It goes as follows: a recursive d-step-ahead MTN predictive model is developed to compensate the influence of time-delay, and the extended Kalman filter (EKF) algorithm is applied for its learning; the multistep predictive objective function is designed, and the optimal controlled output is determined by iterative refinement; and the convergence of MTN predictive model and the stability of closed-loop system are proved. Findings Simulation results show that the proposed scheme is of desirable generality and capable of performing the tracking control for MIMO nonlinear systems with unknown input time-delay in industrial process effectively, such as the continuous stirred tank reactor (CSTR) process, which provides a considerably improved performance and effectiveness. The proposed scheme promises strong robustness, low complexity and easy implementation. Research limitations/implications For the limitations of proposed scheme, the time-invariant time-delay is only considered in time-delay identification and control schemes. And the CSTR process is only introduced to prove that the proposed scheme can adapt to practical industrial scenario. Originality/value The originality of the paper is that the proposed MTN control scheme has good tracking performance, which solves the influence of time-delay, coupling and nonlinearity and the real-time performance for MIMO nonlinear systems with unknown input time-delay.

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“…Lu et al proposed a robust self‐triggered MPC scheme for linear systems based on the tube‐MPC 20 . Some researchers also applied tube‐based MPC to the control problem of networks, for example, Reference 21.…”

Section: Introductionmentioning

confidence: 99%

Constrained control for systems on matrix Lie groups with uncertainties

Shi

et al. 2022

Intl J Robust & Nonlinear

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In this paper, the constrained control of systems evolving on matrix Lie groups with uncertainties is considered. The proposed methodology is composed of a nominal Model Predictive Control (MPC), and a feedback controller. The previous work on the control of systems on manifolds is applied to design the nominal MPC, which generates the nominal trajectory. In the nominal MPC, the state and input constraints on the Lie group are transformed into the constraints on the Euclidean space. While to deal with uncertainties, the feedback control used to track the nominal trajectory is designed directly on the Lie group.The tracking error in the feedback control is proved to be bounded in invariant sets, which are further used to revise the constraints in nominal MPC. We prove that the input-to-state stability of the entire system under the proposed control methodology with respect to the disturbances can be achieved. The proposed methodology is applied to the constrained attitude control of rigid bodies with uncertainties. In the application example, the detailed mathematical proof and the comparative numerical simulation are presented to demonstrate the feasibility of the proposed methodology.

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Tube-Based Model Predictive Control Using Multidimensional Taylor Network for Nonlinear Time-Delay Systems

Cited by 46 publications

References 41 publications

Adaptive control of a class of stochastic nonlinear systems with full state constraints and input saturation using multi‐dimensional Taylor network

Adaptive control of a class of stochastic nonlinear systems with full state constraints and input saturation using multi‐dimensional Taylor network

MTN-based recursive d-step-ahead predictive control of MIMO nonlinear systems with unknown input time-delay in industrial process

Constrained control for systems on matrix Lie groups with uncertainties

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