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In this article, the ultrafast tracking control for high-order discrete-time multiagent networks is investigated based on the predictive ability of agents. First, in view of the evolution of the original network dynamics, a distributed multistep prediction algorithm is established. Further, a control strategy containing predictive information is presented to achieve the ultrafast tracking control. Second, the problem of ultrafast tracking control with an $H_{\infty }$ performance specification is dealt with. The obtained result indicates that the robustness of the whole multiagent network can be converted into the robustness of multiple separable subsystems. Finally, the optimal ultrafast static tracking control design is provided for the single-integrator network with sampled dynamics. Two simulation cases are carried out to verify the correctness of the obtained theoretical results. [ABSTRACT FROM AUTHOR]

This paper studies the multi‐tracking control for high‐order heterogeneous multi‐agent systems under a directed acyclic graph. The authors first convert the multi‐tracking control problem for multi‐agent systems into the zero steady‐state error control problem of some independent subsystems. Based on final‐value theorem and Mason's rule, a sufficient condition for achieving the multi‐tracking control is proposed. It explicitly reveals how the system dynamic and desired object dynamic jointly affect the multi‐tracking control of heterogeneous multi‐agent systems. Meanwhile, a new result on the steady‐state error for multi‐subnetworks is established. Moreover, the multi‐tracking control for heterogeneous multi‐agent systems with time delay is discussed. Some simulations are performed to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

UV–Vis spectroscopy coupled with chemometrics was used effectively to study the impact of heating on edible oils (corn oil, sunflower oil, rapeseed oil, peanut oil, soybean oil and sesame oil) and determine their acid value. Analysis of their first derivative spectra showed that the peak at 370 nm was a common indicator of the heated oils. Partial least squares regression (PLS) and principle component regression (PCR) were applied to building individual quantitative models of acid value for each kind of oil, respectively. The PLS models had a better performance than PCR models, with determination coefficients ( R 2 ) of 0.9904–0.9977 and root mean square errors (RMSE) of 0.0230–0.0794 for the prediction sets of each kind of oil, respectively. An integrate quantitative model built by support vector regression for all the six kinds of oils was also developed and gave a satisfactory prediction with a R 2 of 0.9932 and a RMSE of 0.0656. [ABSTRACT FROM AUTHOR]

This paper deals with the ultra-fast consensus problem of high-order discrete-time multi-agent systems under a unified framework. A multi-step neighbour-error predictive mechanism is established based on the future evolution of the network dynamic without predictive information. By predicting the dynamics of a network several steps ahead and adding this information into the consensus protocol, a novel ultra-fast consensus protocol with the self-feedback term is proposed. The asymptotic convergence factor is improved by a power ofq+ 1 compared to the routine consensus protocol. The ultra-fast consensus algorithm can overcome the influence of communication topology to the convergence speed toward consensus, and solve the difficult problem of selecting the optimal control gain which minimises the asymptotic convergence factor. Moveover, some sufficient conditions for ultra-fast consensus design are given herein. The ones decouple the design of the synchronising gains from the detailed graph properties, and explicitly reveal how the agent dynamic and the communication graph jointly affect ultra-fast consensus of discrete-time multi-agent systems. A simulation is performed to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM PUBLISHER]

Vision-based formation control offers an alternate solution for unmanned aerial vehicles (UAVs) to work together in the external position system denied environment. In this paper, we present a vision-based formation control strategy with the advantages of high tracking performance and low consumption of communication resources. A vision-based multi-robot localisation methods is applied to get precise position information, and the extended Kalman filter (EKF) is used to fuse measurement data from different sensors. The integral sliding mode controller (ISMC) is utilised to ensure the fast convergence and robustness to the desired formation, and the event-triggered communication scheme is employed to reduce redundant transmission of state information used in the formation controller. The simulation results in Gazebo with a team of three UAVs are given to illustrate the effectness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

A method for recursive subspace identification of continuous-time systems based on generalized Poisson moment functionals is proposed. Most of the existing subspace identification methods have concentrated mainly on the time-invariant discrete-time systems. The results of subspace identification methods are confined to the discrete-time cases, due to the difference on the construction of Hankel matrices. In addition, the time-invariant identification algorithms are not suitable for online identification cases. In order to solve the problems above, the time derivatives of Hankel matrices can be evaluated by generalized Poisson moment functionals, which provides a simple linear mapping for identification algorithm without the amplification of stochastic noises. The size of the data matrices is fixed a priori to fade the influence of old data to the updated data, which is a key to reduce computational burden and storage cost of recursive algorithms. The efficiency of the presented method is provided by comparing simulation results. [ABSTRACT FROM AUTHOR]

Atmospheric cold plasma is a powerful and profitable microbial decontamination technology in the food industry. The microbial load and food matrix have a great influence on the plasma inactivation effect. This study aimed to quantify the effects of initial cell concentration and soluble solids on the inactivation of yeast in apple juice by gas-phase surface discharge plasma. A computer simulation model was developed to predict the inactivation process using commercial finite element software, COMSOL Multiphysics, and the model was verified by experiments. Studies have shown that the inactivation of yeast was affected by the initial cell concentration and soluble solids. These effects were quantified by fitting equations and used as model inputs. The simulation model predicted the D-value of plasma inactivation of yeast under different discharge voltages and apple juice volume, which was verified in experiments. Therefore, the established model can be used for plasma development to predict how the food matrix affects the inactivation process, thereby providing useful and practical tools for amplifying the commercial application of plasma. • A surface discharge voltage plasma deactivation model was developed and verified. • Model correlated the cell concentration and soluble solids with inactivation rates. • The influence of discharge voltage and process capacity was predicted in the model. [ABSTRACT FROM AUTHOR]

Most existing industrial process fault detection and diagnosis (FDD) techniques operate on data collected at a single scale and focus only on known faults. However, actual process data are inherently multiscale and unknown faults are always inevitable during system running. Therefore, they may perform unsatisfactorily. To tackle this problem, this paper develops a decentralized industrial process FDD framework using multiple enhanced supervised kernel entropy component analysis (enhanced SKECA) models, where each model acts as a fault indicator for one specific fault. Faults can be easily diagnosed by monitoring the outputs of all models within the framework. In particular, when new faults are identified, the framework can update itself only by adding the corresponding enhanced SKECA models without a complete rebuilding process. The monitoring results for the continuous stirred tank reactor (CSTR) process show that the proposed framework is effective in diagnosing both known and unknown faults. • A decentralized industrial process FDD framework based on enhanced SKECA is proposed. • Enhanced SKECA boasts high classification accuracy. • Enhanced SKECA shows good robustness to the kernel size parameter. • Enhanced SKECA is sensitive to unknown data. • The proposed framework is effective in diagnosing both known and unknown faults. [ABSTRACT FROM AUTHOR]