Robust model predictive control based on recurrent multi‐dimensional Taylor network for discrete‐time non‐linear time‐delay systems

Duan, Zheng-Yi; Yan, Hong‐Sen; Zheng, Xiao-Yi

doi:10.1049/iet-cta.2019.1286

Cited by 9 publications

(5 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is designed to draw the states at the end of the finite prediction horizon to a neighborhood of the origin [28]. The construction process of this terminal constraint set X is described in detail in the following subsection.…”

Section: Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

Learning-based nonlinear model predictive control with accurate uncertainty compensation

2021

View full text Add to dashboard Cite

A learning-based nonlinear model predictive control (LBNMPC) method is proposed in this paper for general nonlinear systems under system uncertainties and subject to state and input constraints. The proposed LBNMPC strategy decouples the robustness and performance requirements by employing an additional learned model and introducing it into the MPC framework along with the nominal model. The nominal model helps to ensure the closed-loop system’s safety and stability, and the learned model aims to improve the tracking behaviors. As a core of the learned model construction, an online parameter estimator is designed to deal with system uncertainties. This estimation process effectively evaluates both the current and historical effects of uncertainties, leading to superior estimating performance compared with conventional methods. By constructing an invariant terminal constraint set, we prove that the LBNMPC is recursively feasible and robustly asymptotically stable. Numerical verifications for a two-link manipulator are conducted to validate the effectiveness and robustness of the proposed control scheme.

show abstract

Section: Problem Statementmentioning

confidence: 99%

“…where K is the feedback gain (K is a positive-definite matrix). Then, the terminal penalty matrix P and the matrix K can be determined by solving the following equation [28]…”

Section: Construction Of Terminal Constraint Setmentioning

confidence: 99%

Learning-based nonlinear model predictive control with accurate uncertainty compensation

2021

View full text Add to dashboard Cite

show abstract

“…And the fuzzy control rules are easily affected by the parameter changes. To solve this problem, the multidimensional Taylor network (MTN), proposed by Yan in 2010, which has found wide application in the domains of model prediction, system identification, and nonlinear control in recent years, can approximate any unknown nonlinear function with arbitrarily high precision and efficiency because of a polynomial network topology with the only product and sum operations [47][48][49][50][51]. With this advantage, the MTN-based adaptive control scheme for stochastic nonlinear systems with unknown nonlinear functions, combined with the classical adaptive technique and backstepping method, has been designed and attracted wide attention [31,33,50,52].…”

Section: Introductionmentioning

confidence: 99%

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

Zheng

2023

Asian Journal of Control

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Within delayindependent strategies, information regarding the exact value or the range of time-delay is not considered in the design of controllers [19,20]. Researchers have studied delay-independent RMPC for linear [21,22] and nonlinear [23] time-delay systems. For instance, an RMPC strategy has been addressed towards designing a controller for state-delay systems to minimise a given performance index [24,25].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the RMPC problem for uncertain time-delay systems has been developed in refs. [23,26]; however, the fragility of the controller has not been addressed in this study. Hakimzadeh et al [48] proposed a non-fragile RMPC algorithm, but for delay-free systems.…”

Section: Introductionmentioning

confidence: 99%

Designing of a non‐fragile robust predictive controller for uncertain systems with time‐varying delay: A delay‐range‐dependent approach

Baneshi

Ghaffari

2022

IET Control Theory & Appl

View full text Add to dashboard Cite

This study addresses a new delay‐dependent non‐fragile robust model predictive controller for a class of uncertain continuous‐time systems with time‐varying delay subjected to constrained input and perturbed controller gain. The uncertainties in system parameters and controller gain are considered as general additive forms. The proposed controller can provide a suitable linear feedback control law depending on delay such that robust performance is achieved to the possible existence of uncertainties. To this end, an optimisation problem is formulated to minimise the upper‐bound of the cost function involving some conditions in terms of linear matrix inequalities, utilising the information of lower‐and upper‐bounds of time‐varying delay. By including information regarding the delay range in the quadratic function, the ability to have less conservative results compared to the related works in literature is provided. The effectiveness of the proposed technique is illustrated through a comparison with recent works.

show abstract

Robust model predictive control based on recurrent multi‐dimensional Taylor network for discrete‐time non‐linear time‐delay systems

Cited by 9 publications

References 42 publications

Learning-based nonlinear model predictive control with accurate uncertainty compensation

Learning-based nonlinear model predictive control with accurate uncertainty compensation

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

Designing of a non‐fragile robust predictive controller for uncertain systems with time‐varying delay: A delay‐range‐dependent approach

Contact Info

Product

Resources

About