IET Control Theory & Applications
Volume 12, Issue 10, 03 July 2018
Volumes & issues:
Volume 12, Issue 10
03 July 2018
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- Author(s): Zhaoxu Yu ; Huaicheng Yan ; Shugang Li ; Yan Dong
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1367 –1375
- DOI: 10.1049/iet-cta.2017.1197
- Type: Article
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p.
1367
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The adaptive quantised tracking problem is addressed for a class of switched stochastic strict-feedback non-linear systems with asymmetrical input saturation in this study. With the aid of a Gaussian error function-based continuous differentiable switching model and some special techniques, the technical difficulties from dealing with the switching asymmetric saturation non-linearities and the sector-bounded quantisation errors are overcome. Then, by combining the common Lyapunov function method, backstepping technique and neural network approximation-based approach, a simple common adaptive tracking control scheme involving one adaptive parameter only is presented for such systems under arbitrary switching. The given quantised control scheme guarantees that all signals of the closed-loop system are semi-globally bounded in probability while the tracking error can converge to a small neighbourhood of the origin. Finally, simulation studies are provided to illustrate the effectiveness and applicability of the proposed control design.
- Author(s): Yu-Chi Chiang and Chih-Chiang Cheng
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1376 –1383
- DOI: 10.1049/iet-cta.2017.1014
- Type: Article
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1376
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Based on the Lyapunov stability theorem, an adaptive output feedback variable structure control (OFVSC) scheme is proposed in this study for a class of multi-input multi-output non-linear systems with matched and mismatched perturbations to solve regulation problems. First, a switching function is designed, and the system to be controlled is decomposed into two subsystems through a linear state transformation. Then the proposed OFVSC scheme is presented. A perturbation estimation algorithm is utilised in designing the proposed control scheme in order to overcome the problem of unmeasurable states. Adaptive mechanism is also employed so that the upper bounds of perturbations as well as perturbation estimation errors are not required to be known in advance. Furthermore, the resultant control system is capable of driving all the states into zero within a finite time and guaranteeing global stability. Finally, a numerical example is given for demonstrating the feasibility of the proposed control scheme.
- Author(s): Luca Cavanini ; Gionata Cimini ; Gianluca Ippoliti
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1384 –1392
- DOI: 10.1049/iet-cta.2017.1096
- Type: Article
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p.
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The use of linear parameter varying (LPV) prediction models has been proven to be an effective solution to develop model predictive control (MPC) algorithms for linear and non-linear systems. However, the computational effort is a crucial issue for LPV-MPC, which has severely limited its application especially in embedded control. Indeed, for dynamical systems of dimension commonly found in embedded applications, the time needed to form the quadratic programming (QP) problem at each time step, can be substantially higher than the average time to solve it, making the approach infeasible in many control boards. This study presents an algorithm that drastically reduces this computational complexity for a particular class of LPV systems. They show that when the input matrix is right-invertible, the rebuild phase of the QP problem can be accelerated by means of a coordinate transformation which approximates the original formulation. Then they introduce a variant of the algorithm, able to further reduce this time, at the cost of a slightly increased sub-optimality. The presented results on vehicle dynamics and electrical motor control confirm the effectiveness of the two novel methods, especially in those applications where computational load is a key indicator for success.
- Author(s): Jinwei Yu ; Jinchen Ji ; Zhonghua Miao ; Jin Zhou
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1393 –1401
- DOI: 10.1049/iet-cta.2017.1065
- Type: Article
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1393
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This study addresses the problem of formation control with collision avoidance for networked Lagrangian systems with uncertain parameters interacting on directed network communication topologies. Two adaptive formation control strategies with collision avoidance are proposed by making use of adaptive gain techniques for both cases of with and without a dynamic leader. The main objective of the proposed control strategies is to dispatch a group of agents to maintain a desired geometric pattern, while still guarantee collision avoidance at any time, and eventually to achieve velocity matching. A distinctive feature of the developed adaptive gain is to adapt itself duly based on both the network communication topology and collision avoidance constraints, so it is feasible to be implemented in practice. Some general criteria are derived to guarantee that the desired formation with collision avoidance for the networked Lagrangian systems can be achieved. Finally, numerical simulations are given to show the performance of the proposed control methodologies.
- Author(s): Debasish Biswas ; Kaushik Das Sharma ; Gautam Sarkar
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1402 –1413
- DOI: 10.1049/iet-cta.2017.0732
- Type: Article
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The present study proposes a new approach for designing stable adaptive fractional-order proportional–integral–derivative (FOPID) controllers, which employs non-sinusoidal orthogonal function (NSOF) domain-based design approach. The objective is to design a self-adaptive FOPID controller such that the designed controller can guarantee desired stability and simultaneously it can provide satisfactory transient performance. The proposed design methodology simplifies and eliminates the complexity of solving fractional-order system dynamics by converting it into the algebraic vector–matrix equation with the help of NSOF. The conventional FOPID, NSOF-based FOPID and NSOF-based adaptive FOPID controllers are implemented for benchmark simulation case studies and real-life experimentation and their results demonstrate the usefulness of the proposed approach.
- Author(s): Hsiu-Ming Wu and Reza Tafreshi
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1414 –1420
- DOI: 10.1049/iet-cta.2017.0063
- Type: Article
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Control of air–fuel ratio (AFR) plays a key role in the minimisation of the carbon dioxide and harmful pollutant emissions and maximisation of fuel economy. An inherent time-varying delay existing in lean-burn spark ignition (SI) engines is a major challenge for the AFR control. Herein an unstable internal dynamics with a parameter dependent system caused by time delay is established to represent a dominating feature of AFR. The proposed control scheme, LPV-based fuzzy control technique, combines the features of LPV and fuzzy control to deal with the unstable internal dynamics of an AFR system with external disturbances and a high level of uncertainty in system parameters. Based on the desired error dynamics, an LPV dynamic error consisting of the unstable state and the AFR tracking error is determined. Subsequently, the proposed fuzzy control algorithm through a look-up table is used to stabilise the LPV dynamic error. Then, the tracking error moves along the desired error dynamics towards zero. The system stability is assured via Lyapunov stability criteria. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed control scheme under different operating conditions. Also, compared with the baseline controller, i.e. proportional–integral controller with Smith predictor, demonstrates its superiority.
- Author(s): Laura Celentano and Michael Basin
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1421 –1430
- DOI: 10.1049/iet-cta.2018.0101
- Type: Article
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This paper provides theoretical preliminary results and develops a first methodology, that allows one to efficiently estimate the maximum time constant of a dynamic matrix of an uncertain system with rational multi-affine structure with respect to parameters, and a second one to find a majorant system of a multi-input multi-output (MIMO) system with rational multi-affine structure with respect to parameters. The developed methodologies are used to estimate the evolution of an uncertain linear time-invariant (LTI) system with additional bounded nonlinearities and/or additional bounded input signals. Moreover, the above results are also used to design a robust controller for an uncertain MIMO system with unmeasurable states and subject to a rate-bounded disturbance in order to track a rate-bounded reference signal. The obtained theoretical results are illustrated by three examples. The first two examples deal with the analysis of a LTI system with bounded disturbances and measurement noise, and additional bounded and not bounded nonlinearities, respectively; in the second example a new control law with saturation is also designed. In the last example, a robust controller for an uncertain electro-mechanical system with unmeasurable state is designed to track a rate-bounded reference signal in the presence of a disturbance with bounded derivative.
- Author(s): Boubekeur Targui ; Omar Hernández-González ; Carlos-Manuel Astorga-Zaragoza ; Maria Eusebia Guerrero-Sánchez
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1431 –1439
- DOI: 10.1049/iet-cta.2017.1138
- Type: Article
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The main contribution of this study is to present a chain observer for Lipschitz non-linear systems when the output measurements are affected by a long known and bounded time-varying delay. The proposed chain observer is composed by a set of observers with similar algorithms. Each observer estimates the state in a time horizon, while the first observer of the chain estimates the current state. The structure of each observer of the chain is based on the presence of a dynamical term which permits the compensation of the time-varying delay. The observer gain is computed by solving parameterised linear matrix inequalities (LMIs) which depend on the delay. The less conservative Lipschitz condition allows to manage less restrictive LMI stability conditions, which leads to a more general class of Lipschitz non-linear systems. A Lyapunov–Krasovskii functional is used to demonstrate that the estimation error converges asymptotically to zero. The performance of the proposed observer is evaluated through numerical examples. The observer exhibits good estimation of the system state, even in the presence of significant delayed measurements.
- Author(s): Xin Huang ; Ding Zhai ; Jiuxiang Dong
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1440 –1447
- DOI: 10.1049/iet-cta.2017.1278
- Type: Article
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This study is concerned with the reliable and optimal control problems of data-driven cyber-physical systems (CPSs) against a class of actuator attacks. Consider an unknown continuous-time linear physical system with the external disturbance, and it is assumed that control input signals transmitted via network layers are vulnerable to cyber attacks. By introducing a new integral sliding-mode function and utilising the available data acquired by an off-policy reinforcement learning algorithm, a novel data-based adaptive integral sliding-mode control strategy is presented. Different from the existing control policies, the novel one uses a data-driven sliding-mode compensator to eliminate the effect of the actuator attacks such that the stability and a nearly optimal performance of the CPSs can be guaranteed. Finally, the effectiveness of the proposed control strategy is verified by a numerical example.
- Author(s): Linghuan Kong ; Wei He ; Chenguang Yang ; Guang Li ; Zhengqiang Zhang
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1448 –1455
- DOI: 10.1049/iet-cta.2017.0757
- Type: Article
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1448
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An adaptive fuzzy neural network (FNN) control scheme is proposed for a marine vessel with time-varying constraints, guaranteed transient response and unknown dynamics. A series of continuous constraint functions are introduced to shape the motion of a marine vessel. To deal with the constraint problems and transient response problems, an asymmetric time-varying barrier Lyapunov function is designed to ensure that the system states are upper bounded by the considered constraint functions. FNNs are constructed to identify the unknown dynamics. Considering existing approximation errors when FNNs approximating the unknown dynamics, an adaptive term is designed to compensate the approximation errors in order to obtain accurate control. Via Lyapunov stability theory, it has been proved that all the states in the closed-loop system are uniformly bounded ultimately without violating the corresponding prescribed constraint region. Two comparative simulations are carried out to verify the effectiveness of the proposed control.
- Author(s): Raj Deshmukh ; Omanshu Thapliyal ; Cheolhyeon Kwon ; Inseok Hwang
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1456 –1464
- DOI: 10.1049/iet-cta.2017.1208
- Type: Article
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1456
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In this study, the authors consider the distributed state estimation problem of a stochastic linear hybrid system (SLHS) observed over a sensor network. The SLHS is a dynamical system with interacting continuous state dynamics described by stochastic linear difference equations and discrete state (or mode) transitions governed by a Markovian process with a constant transition matrix. Most existing hybrid estimation algorithms are based on a centralised architecture which is not suitable for distributed sensor network applications. Further, the existing distributed hybrid estimation algorithms are restrictive in sensor network topology, or approximate the consensus process among connected sensor agents. This study proposes a distributed hybrid state estimation algorithm based on the multiple model based approach augmented with the optimal consensus estimation algorithm which can locally process the state estimation and share the estimation information with the neighbourhood of each sensor agent. This shared information comprises local mode-conditioned state estimates and edge-error covariances, and is used to bring about an agreement or a consensus across the network. The proposed distributed hybrid state estimation algorithm is demonstrated with an illustrative aircraft tracking example.
- Author(s): Xianqiang Yang ; Xin Liu ; Boxuan Han
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1465 –1473
- DOI: 10.1049/iet-cta.2017.1176
- Type: Article
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This study focuses on identifying the linear parameter varying (LPV) system with an unknown scheduling variable in the presence of missing measurements and the system output data contaminated with outliers. The parameter interpolated LPV autoregressive exogenous (ARX) model with an unknown scheduling variable is considered and the scheduling variable dynamic is described by a non-linear state-space model. The outliers treatment and unknown scheduling variable estimation with missing observations are both taken into consideration. The robust LPV model is established based on the Student's t-distribution in order to handle the outliers and the particle smoother is adopted to estimate the true scheduling variable from incomplete data set. The formulations of the proposed algorithm are finally derived in the expectation–maximisation algorithm scheme and the formulas to estimate the unknown parameters of LPV ARX model and scheduling variable dynamic model are derived simultaneously. A numerical example and a chemical process are used to present the efficacy of the proposed approach.
- Author(s): Sapna Gupta ; Rajeev Gupta ; Subhransu Padhee
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1474 –1482
- DOI: 10.1049/iet-cta.2017.1128
- Type: Article
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This study provides a step-by-step analysis of a parametric system identification procedure which provides a single-input single-output, linear time invariant time-delay model of a liquid–liquid heat exchanger system. Different linear and block-oriented models are used to develop a control relevant identification model. The prediction error method is used to estimate the model parameters. From the simulation results, it can be found that the output error model (linear model) and Hammerstain–Wiener model (block-oriented model) provide the best model validation for a heat exchanger system at 72.23% and 81.5 goodness-of-FIT, respectively. To achieve the control objectives of the system, the classical proportional–integral–derivative (PID) controller is used. For controller design, the linear model is considered. An industrial process is prone to encounter different uncertainties. Considering the time delay of the estimated linear model as the uncertainty, a criterion is used to find out the robust parameters of the PID controller. The developed controller satisfies the sensitivity constraints. Stability boundary locus of , and of the PID controller have been plotted to illustrate the robust parameters of the PID controller.
- Author(s): Bo Pang and Qingling Zhang
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1483 –1490
- DOI: 10.1049/iet-cta.2017.1227
- Type: Article
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In this study, problems of sliding mode control (SMC) for polynomial fuzzy singular systems with time delay are concerned. A sliding surface function is constructed as integral type and the resultant system which is restricted to the sliding surface is analysed. In terms of sum-of-squares approach, stability criterions are derived, which lead to stability with disturbance rejection level property. Then, the SMC law is proposed, which can confine the state trajectories to the sliding surface. Finally, an example is performed to verify the feasibility and merits of the theoretical findings.
Adaptive quantised control of switched stochastic strict-feedback non-linear systems with asymmetric input saturation
Terminal adaptive output feedback variable structure control
Computationally efficient model predictive control for a class of linear parameter-varying systems
Formation control with collision avoidance for uncertain networked Lagrangian systems via adaptive gain techniques
Stable adaptive NSOF domain FOPID controller for a class of non-linear systems
Air–fuel ratio control of lean-burn SI engines using the LPV-based fuzzy technique
New results on robust stability analysis and synthesis for MIMO uncertain systems
Chain observer for Lipschitz non-linear systems with long time-varying delayed measurements
Adaptive integral sliding-mode control strategy of data-driven cyber-physical systems against a class of actuator attacks
Adaptive fuzzy control for a marine vessel with time-varying constraints
Distributed state estimation for a stochastic linear hybrid system over a sensor network
LPV model identification with an unknown scheduling variable in the presence of missing observations – a robust global approach
Parametric system identification and robust controller design for liquid–liquid heat exchanger system
Sliding mode control for polynomial fuzzy singular systems with time delay
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- Author(s): Sanbo Ding ; Zhanshan Wang ; Huaguang Zhang
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1491 –1496
- DOI: 10.1049/iet-cta.2017.0965
- Type: Article
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p.
1491
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This brief investigates the event-triggered control for a class of non-linear systems by proposing an exponential approximation method. The core idea is that some time-varying exponential terms which consist of two sign functions and a tunable scalar are developed to approximate the continuous states/outputs of closed-loop system. These exponential terms are utilised to design the state/output feedback controllers and the associated event-triggered mechanisms, respectively. The numerical examples demonstrate that the proposed method can essentially reduce the amount of samplings compared to the ones without the exponential terms.
- Author(s): Qiao Zhu ; Jun-Jun Ding ; Ming-Liang Yang
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1497 –1504
- DOI: 10.1049/iet-cta.2017.0529
- Type: Article
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This study focuses on the ride quality and hunting stability of high-speed train by employing the lateral active secondary and primary suspensions. First, the full-scale railway vehicle dynamics with 17-degree-of-freedom are introduced, where the actuator saturation, suspension deflection, and random and periodic track irregularities are considered. The system characteristic and control ability are discussed by analysing the spectrum of the open-loop system. Here, it is found that improving ride quality and hunting stability simultaneously is difficult by only using the secondary suspension. In consequence, both the active secondary and primary suspensions are employed to improve the ride quality and hunting stability simultaneously by utilising the linear-quadratic-Gaussian (LQG) control theory. Finally, the random and periodic track irregularities are given to illustrate the efficiency of the proposed LQG-based active secondary and primary suspensions.
- Author(s): Kongwei Zhu ; Dan Ma ; Jun Zhao
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1505 –1514
- DOI: 10.1049/iet-cta.2017.0895
- Type: Article
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p.
1505
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The event triggered control for a kind of switched linear parameter varying (LPV) systems is studied. The authors present two event triggering schemes for such systems, depending on whether the controllers are pre-designed or to be designed. In the cases of pre-designed controllers, they present the state and parameters dependent event triggering scheme. In the case of controllers to be designed, two event triggering schemes are given, depending on the state and parameters, respectively. The feasible linear matrix inequality (LMI) conditions are given for designing controllers and event triggering thresholds. The minimum of trigger time intervals is obtained as a positive number. Under the average dwell time switching mechanism, the switched parameter-varying system is exponentially stable. Finally, the validity of designed approaches is verified through a simulation example for an aircraft engine model.
- Author(s): Aijuan Wang ; Xiaofeng Liao ; Tao Dong
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1515 –1519
- DOI: 10.1049/iet-cta.2017.0328
- Type: Article
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p.
1515
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This study focuses on the event-triggered gradient-based algorithm for a distributed optimisation problem of multi-agent system subject to state consensus constraint over directed networks, where each agent has local access to its own strongly convex utility function. A novel gradient-based optimisation consensus algorithm is proposed to solve the optimisation consensus problem, where the event-triggered strategy based on sample-data is employed. In contrast to previous optimisation consensus work, their algorithm guarantees that the equilibrium point of the multi-agent systems is optimal solution, and it uses the constant step-size in the optimisation term. Under the algorithm, it can be proved that there exists a certain vector established with the optimal solution is the system equilibrium point and also the consensus point. Moreover, the sufficient condition on optimisation consensus for multi-agent systems is derived. Finally, a numerical simulation example is given to illustrate the theoretical analysis.
- Author(s): Yang Liu ; Zidong Wang ; Donghua Zhou
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1520 –1526
- DOI: 10.1049/iet-cta.2017.1119
- Type: Article
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This study is concerned with the state estimation and fault reconstruction problems for a class of discrete systems with integral measurements under partially decoupled disturbances. The considered integral measurements, as functions of the system states over a period of time, reflect the interval time between sample collections and real-time signal processing. Moreover, the process disturbances are allowed to be partially decoupled in the observer design. An augmented state vector is constructed, which consists of the current system state, the delayed system state and the additive fault, and the resultant augmented system is described in a singular form. Then, an unknown input observer is obtained that decouples partial disturbances and attenuates the effect from the remaining undecouplable disturbances. The existence conditions of the desired observer are thoroughly investigated and an algorithm for designing the observer gains is also provided. Finally, a numerical example is presented to show the effectiveness of the proposed method.
- Author(s): Yilin Ma and Ruizhu Han
- Source: IET Control Theory & Applications, Volume 12, Issue 10, p. 1527 –1532
- DOI: 10.1049/iet-cta.2017.0878
- Type: Article
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p.
1527
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This study addresses two algorithms for set stabilisation of Boolean control networks (BCNs). Based on the semi-tensor product tool, the dynamics of BCNs can be characterised by its labelled digraph, which derived an graphical expression for the set stabilisation of BCNs. Then, two tree-search algorithms, namely, generalised breadth-first search and generalised depth-first search, are proposed for the first time to decide the controllers for the set stabilisation of BCNs. In addition, some properties concerning the tree search algorithm are proposed. Finally, an example is employed to show the application of the presented algorithms.
Event-triggered control for a class of non-linear systems: an exponential approximation method
LQG control based lateral active secondary and primary suspensions of high-speed train for ride quality and hunting stability
Event triggered control for a switched LPV system with applications to aircraft engines
Event-triggered gradient-based distributed optimisation for multi-agent systems with state consensus constraint
State estimation and fault reconstruction with integral measurements under partially decoupled disturbances
Algorithms for set stabilisation of Boolean control networks
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