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In this paper, a fixed-time control strategy for a class of uncertain multi-robot systems with prescribed performance under a directed graph is proposed. The leader is given as a reference trajectory and every follower is descriebed as a n-Degree of Freedom robot manipulator. The dynamic model of the robot manipulator is devided into the nominal part and the uncertain part. A Radial Basis Function neural network is used to approximate the uncertain part. By using an error transformation, the prescribed performance constrained system is transformed into an "unconstrained" one, and the prescribed performance control with nonsingular sliding mode is proposed to guarantee that the sliding mode and the transformed error are uniformly ultimately bounded and converge to a small neighborhood within a fixed time. Simulation studies are given to validate the effectiveness of our proposed control strategy.

This paper considers a class of multilateral teleoperation system consisting of a single master manipulator which is operated by a human operator and multiple slave manipulators cooperatively grasping a common constrained object. Considering the network bandwidth constraints, the Round-Robin scheduling protocol is used to orchestrate the sampled slave signals to the master, and adaptive controllers for the teleoperation system are proposed in the presence of uncertain parameters, time delays, and communication bandwidth limitations. The stability of the closed-loop is established by using linear matrix inequalities. It is demonstrated that velocities and position error between the master manipulator end-effector and the object asymptotically converge to zero. Finally, simulation studies are given to demonstrate the effectiveness of the proposed approach.

In this article, we present an intermittent framework for safe reinforcement learning (RL) algorithms. First, we develop a barrier function-based system transformation to impose state constraints while converting the original problem to an unconstrained optimization problem. Second, based on optimal derived policies, two types of intermittent feedback RL algorithms are presented, namely, a static and a dynamic one. We finally leverage an actor/critic structure to solve the problem online while guaranteeing optimality, stability, and safety. Simulation results show the efficacy of the proposed approach.

In the blast furnace production site, the disposable thermocouple is used to measure the hot metal temperature. However, this method is not only inconvenient for continuous data acquisition but also costly for the use of one-time thermocouple. Hence, this paper establishes a prediction model to predict the hot metal temperature. Before the prediction model is established, the corresponding factors of influencing the hot metal temperature are selected, and the noises of production data are removed. In this paper, multi-layer extreme learning machine (ML-ELM) is used as the prediction algorithm of the prediction model. However, the input weights, hidden layer weights and hidden biases of ML-ELM are randomly selected, and the solution of the output weights is based on them, which makes ML-ELM inevitably have a set of non-optimal or unnecessary weights and biases. In addition, ML-ELM may suffer from over-fitting problem. Hence, this paper uses the adaptive particle swarm optimization (APSO) and the ensemble model to improve ML-ELM, and the improved algorithm is named as EAPSO-ML-ELM. APSO can optimize the selections of the input weights, hidden layer weights and hidden biases, the ensemble model can alleviate the over-fitting problem, i.e., this paper combines several of the optimized ML-ELMs which have different input weights, hidden layer weights and hidden biases. Finally, this paper also uses other algorithms to establish the prediction model, and simulation results demonstrate that the prediction model based on EAPSO-ML-ELM has better prediction accuracy and generalization performance.

Gas utilization ratio (GUR) is an important indicator reflecting the operating state and energy consumption of blast furnace (BF). Skilled operators usually refer to changing trends of GUR to guide the next step of production. For these reasons, this paper establishes a soft sensing scheme based on an improved extreme learning machine (ELM) to predict GUR. In order to enhance the modeling capability of ELM for industrial data, an improved ELM, named GR-ELM, is proposed based on grey relational analysis (GRA) and residual modification mechanism. In GR-ELM, considering the different effective information contained in each input attribute for modeling, the input attribute optimization is proposed combining with GRA and entropy weight method. Then, because the modeling capability of ELM is limited and the data collected from industrial process are usually contaminated, the residual modification mechanism is implemented to improve the reliability of the model. In addition, considering the influence of time delay in BF ironmaking process, generalized correlation coefficient method based on mutual information is used for time delay analysis to eliminate the influence. The real data collected from a BF are applied and validated the performance and effectiveness of the proposed soft sensing scheme. The experimental results show that the proposed soft sensing scheme is available and can achieve better performance than some state-of-the-art algorithms. The soft sensing scheme can provide effective decision support and guidance for further optimization operation.

In this paper, an online intermittent actor-critic reinforcement learning method is used to stabilize nonlinear systems optimally while also guaranteeing safety. A barrier function-based transformation is introduced to ensure that the system does not violate the user-defined safety constraints. It is shown that the safety constraints of the original system can be guaranteed by assuring the stability of the equilibrium point of an appropriately transformed system. Then, an online intermittent actor-critic learning framework is developed to learn the optimal safe intermittent controller. Also, Zeno behavior is guaranteed to be excluded. Finally, numerical examples are conducted to verify the efficacy of the learning algorithm.

This paper develops optimal control protocols for the distributed output synchronization problem of leader-follower multiagent systems with an active leader. Agents are assumed to be heterogeneous with different dynamics and dimensions. The desired trajectory is assumed to be preplanned and is generated by the leader. Other follower agents autonomously synchronize to the leader by interacting with each other using a communication network. The leader is assumed to be active in the sense that it has a nonzero control input so that it can act independently and update its control to keep the followers away from possible danger. A distributed observer is first designed to estimate the leader's state and generate the reference signal for each follower. Then, the output synchronization of leader-follower systems with an active leader is formulated as a distributed optimal tracking problem, and inhomogeneous algebraic Riccati equations (AREs) are derived to solve it. The resulting distributed optimal control protocols not only minimize the steady-state error but also optimize the transient response of the agents. An off-policy reinforcement learning algorithm is developed to solve the inhomogeneous AREs online in real time and without requiring any knowledge of the agents' dynamics. Finally, two simulation examples are conducted to illustrate the effectiveness of the proposed algorithm.

Reasonable burden distribution matrix is one of important requirements that can realize low consumption, high efficiency, high quality and long campaign life of the blast furnace. This paper proposes a data-driven prediction model of adjusting the burden distribution matrix based on the improved multilayer extreme learning machine (ML-ELM) algorithm. The improved ML-ELM algorithm is based on our previously modified ML-ELM algorithm (named as PLS-ML-ELM) and the ensemble model. It is named as EPLS-ML-ELM. The PLS-ML-ELM algorithm uses the partial least square (PLS) method to improve the algebraic property of the last hidden layer output matrix for the ML-ELM algorithm. However, the PLS-ML-ELM algorithm may have different results in different trails of simulations. The ensemble model can overcome this problem. Moreover, it can improve the generalization performance. Hence, the EPLS-ML-ELM algorithm is consisted of several PLS-ML-ELMs. The real blast furnace data are used to testify the data-driven prediction model. Compared with other prediction models which are based on the SVM algorithm, the ELM algorithm, the ML-ELM algorithm and the PLS-ML-ELM algorithm, the simulation results demonstrate that the data-driven prediction model based on the EPLS-ML-ELM algorithm has better prediction accuracy and generalization performance.

The presence of time delays in communication introduces a limitation to the stability of bilateral teleoperation systems. In this study, we address the design of Internal Model Control (IMC)-based controllers for teleoperation systems with arbitrary time delays. The two degrees of freedom IMC structure is utilized for the master and the slave respectively and thus the passivity-assumption on the external forces is not required. The stability of the overall system is analyzed by regarding the whole system as a multivariable system. A performance-oriented controller design process which guarantees the delayed tracking between the master and the slave is also given. Finally, an example is given to validate the effectiveness of the proposed method.

The permeability index of the blast furnace is a significant symbol to measure the smooth operation of the blast furnace. This paper proposes a novel prediction model for permeability index of the blast furnace based on the multi-layer extreme learning machine (ML-ELM), the principal component analysis (PCA) method and wavelet transform (called as W-PCA-ML-ELM prediction model). This modified ML-ELM algorithm is based on the ML-ELM algorithm and the PCA method (named as PCA-ML-ELM). The PCA method is applied on the ML-ELM algorithm to improve the algebraic property of the last hidden layer output matrix which deteriorates its generalization performance due to the high multicollinearity. Because the production data of the blast furnace field contain noises, this paper applies the wavelet transform to remove the noise. Comparing with other prediction models which are based on the ML-ELM, the ELM, the BP and the SVM, simulation results illustrate that the better generalization performance and stability of the proposed W-PCA-ML-ELM prediction model.

This paper proposes a novel adaptive control framework for nonlinear teleoperation systems with dynamic and kinematic uncertainties and time-varying time delays. Master-slave synchronization in the task space is achieved. Compared with the previous work, the developed method can simultaneously handle the unknown kinematics/dynamics and the asymmetric varying time delays in a unified framework, and the inverses of the robots' Jacobians are not needed in the control design, and thus the master and slave robots can be redundant and the control law will be implemented more efficiently. Based on Lyapunov-Krasovskii methods, the stability criteria are established by linear matrix inequalities. It is shown by simulation studies that the developed control law can guarantee the task-space synchronization between the master and the slave.

The surface defects of steel strip have diverse and complex features, and surface defects caused by different production lines tend to have different characteristics. Therefore, the detection algorithms for the surface defects of steel strip should have good generalization performance. Aiming at detecting surface defects of steel strip, we established a dataset of six types of surface defects on cold-rolled steel strip and augmented it in order to reduce over-fitting. We improved the You Only Look Once (YOLO) network and made it all convolutional. Our improved network, which consists of 27 convolution layers, provides an end-to-end solution for the surface defects detection of steel strip. We evaluated the six types of defects with our network and reached performance of 97.55% mAP and 95.86% recall rate. Besides, our network achieves 99% detection rate with speed of 83 FPS, which provides methodological support for real-time surface defects detection of steel strip. It can also predict the location and size information of defect regions, which is of great significance for evaluating the quality of an entire steel strip production line.

This paper presents a distributed optimal model reference adaptive control approach for solving containment control of heterogeneous multi-agent systems (MASs) with non-autonomous leaders. First, a fully distributed adaptive observer is designed to provide for each agent the desired reference trajectory by estimating the convex hull spanned by leaders. The distributed observer dynamics serves as a reference model for each follower to synchronize. The global communication graph information or the leader dynamics is not required to design the observer. In contrast to existing model reference adaptive controllers (MRAC) for single-agent systems and containment control solutions for MASs, the proposed MRAC approach imposes optimality and presents a distributed adaptive optimal solution to the containment control problem. To impose optimality, a performance function is defined based on the adaptive observers' states as well as the followers' local measurements. It is shown that considering non-autonomous leaders in this optimal control problem leads to solving inhomogeneous algebraic Riccati equations (AREs), instead of normal AREs in standard optimal control problems. To obviate the requirement of knowing the agents' dynamics, an off-policy reinforcement learning approach implemented on an actor-critic structure is utilized for solving the inhomogeneous ARE. A simulation example is conducted to illustrate the effectiveness of the presented method.

Blast furnace gas utilization rate is one of the indicators for measuring the smooth operation of the blast furnace. The prediction model of the blast furnace gas utilization rate based on the extreme learning machine algorithm (ELM) is firstly established. The burden surface characteristics and the indexes of the blast furnace condition are the input parameters, and the blast furnace gas utilization rate is the output parameter. In most cases, the regular item factor is introduced for ELM to ensure satisfactory output. In this paper, the same prediction model based on PCA-ELM algorithm which is based on the principal component analysis method (PCA) and ELM is established secondly. Real production data of the blast furnace is used to verify the prediction model. By comparing with the results of two models, the model based on the PCA-ELM algorithm has better accuracy than that based on ELM.

Sequential learning algorithms are a good choice for learning data one-by-one or chunk-by-chunk. Liang et al. has proposed OS-ELM algorithm based on the ordinary ELM algorithm, which produces better generalization performance than other famous sequential learning algorithms. One of the deficiencies of OS-ELM is that all the observations are weighted equally regardless of the acquisition time. However, the training data often have timeliness in many real industrial applications. In this paper, we propose a modified online sequential learning algorithm with the forgetting factor (named WOS-ELM algorithm) that weights the new observations more. Then a convergence analysis is presented to make sure the estimation of output weights tend to converge at the exponential speed with the arriving of new observations. For the determination of the value of forgetting factor, it would change with the forecast error automatically and get rid of excessive human interference. We employ several applications in the simulation part including time-series predication, time-variant system identification and the weather forecast problem. The simulation results show that WOS-ELM is more accurate and robust than other sequential learning algorithms.

This paper presents a Hamiltonian-driven framework of adaptive dynamic programming (ADP) for continuous time nonlinear systems, which consists of evaluation of an admissible control, comparison between two different admissible policies with respect to the corresponding the performance function, and the performance improvement of an admissible control. It is showed that the Hamiltonian can serve as the temporal difference for continuous-time systems. In the Hamiltonian-driven ADP, the critic network is trained to output the value gradient. Then, the inner product between the critic and the system dynamics produces the value derivative. Under some conditions, the minimization of the Hamiltonian functional is equivalent to the value function approximation. An iterative algorithm starting from an arbitrary admissible control is presented for the optimal control approximation with its convergence proof. The implementation is accomplished by a neural network approximation. Two simulation studies demonstrate the effectiveness of Hamiltonian-driven ADP.

Silicon content in hot metal is an important indicator for the thermal condition inside the blast furnace in the iron-making process. The operators often refer the silicon content and its change trend for the guidance of next production. In this paper, we establish the neural network model for the prediction of silicon content in hot metal based on extreme learning machine (ELM) algorithm. Considering the imbalanced operating data, weighted ELM (W-ELM) algorithm is employed to make prediction for the change trend of silicon content. The outliers hidden in the real production data often tend to undermine the accuracy of prediction model. First, an outlier detection method based on W-ELM model is proposed from a statistical view. Then we modified the ordinary ELM and W-ELM algorithms in order to reduce the interference of outliers, and proposed two enhanced ELM frameworks respectively for regression and classification applications. In the simulation part, the real operating data is employed to verify the better performance of the proposed algorithm.

Extreme learning machine (ELM) is an emerging machine learning algorithm with single-hidden-layer feedforward neural networks (SLFNs). The key strength of ELM algorithm is the significantly fast training speed and good generalization performance since the learning parameters of hidden nodes are generated randomly. The kernel online sequential ELM (KOS-ELM) is a straightforward extension of the well-known recursive least-squares method to the ELM framework. KOS-ELM is a good choice for the online learning of stationary applications. However, there are lots of situations and applications which are time varying quickly. It is unreasonable and inaccurate to pay equal emphasis on both old and new observations. In this paper, we proposed a modified KOS-ELM algorithm with forgetting mechanism (KOS-ELMF) to deal with the time-sensitive applications. A sliding window is applied to limit the active training data in order to ’forget’ the old observations. The size of the sliding window can change based on the forecast error automatically. The automatic determination of model parameters can avoid human interference and save training time. Empirical study of KOS-ELMF on several benchmark applications shows that the proposed approach achieves more satisfied and robust performance, compared with other ELM-related algorithms.

Gas flow temperature of blast furnace is one of significant indicators of judging the anterograde state of blast furnace. Aiming at the defects of traditional gas flow temperature prediction model, this paper proposes a prediction model for gas flow temperature based on improved regularized extreme learning machine algorithm (RELM). Firstly, influencing factors are chosen through analyzing the distribution of blast furnace gas flow. Considering the blast furnace production data contain high frequency noise, the Fourier transform method is used for spectrum analysis, and the appropriate digital filter is selected according to the spectrum analysis result to eliminate the high frequency noise. Secondly, this paper establishes the prediction model of gas flow temperature. Regularized extreme learning machine and finite memory recursive least squares are combined to overcome Data saturation problem of ordinary extreme learning machine. And the improved algorithm is named as RFM-RELM. Finally, actual production data are used to train and test this model. Experimental result indicate that the model can quickly and accurately predict gas flow temperature ,which provides effective help and support for blast furnace operators to analyze the conditions of blast furnace.

This paper is focused on the adaptive control design for flexible joint robots with parameter uncertainties. Based on the singular perturbation theory, the flexible joint robot is transformed into a fast subsystem and a slow subsystem. For the fast subsystem, the PD control strategy is used to suppress elastic oscillations by the feedback of the motor angular displacement error. For the slow subsystem, an adaptive control scheme is proposed to guarantee the parameter estimation error convergence. The closed-loop system is testified to be globally exponentially stable via the designed Lyapunov function. The comprehensive simulation results demonstrate great tracking precision and high robustness of the proposed control scheme.

This paper proposes an adaptive control for nonlinear bilateral teleoperation systems with single master and multiple slaves in the presence of uncertain parameters and variable time delays, and the master-slaves synchronization in the task space is achieved. The Round-Robin scheduling protocol is used to orchestrate the sampled slave signals to the master. The stability criterion of the closed-loop system is given by using LMIs based on Lyapunov-Krasovskii functional synthesis. The simulation is carried out on a single master/three-slaves teleoperation system, and the results show the effectiveness of the proposed controller.

T-distributed stochastic neighbor embedding (t-SNE) dimension reduction technique are combined with Spectral clustering algorithm to mine and analyze the historical condition data of the blast furnace, and the clustering results are divided into different furnace conditions. The nonlinear dimensionality reduction algorithm tSNE finds the rule in the data by recognizing the observed pattern based on the similarity of data points with multiple features. Most nonlinear techniques except tSNE can not retain local structure and global structure of the data. At the same time, the clustering problem of dataset is transformed into the optimal partitioning problem of graph by spectral clustering, which reduces the impact of blast furnace operating point drift. The test based on the field history library shows its accuracy. At the same time, the data of the blast furnace condition data can be visualized, and the difference between different furnace conditions is demonstrated, which provides convenience for the research of the blast furnace condition in the future. Finally, based on historical data, the well-trained model can also be used for prediction, and it is effective and convenient for the blast furnace operator to adjust the abnormal furnace condition.

This paper considers the control problem with constraints on full-state and control input simultaneously. First, a novel barrier function based system transformation approach is developed to guarantee the full-state constraints. To deal with the input saturation, the hyperbolic-type penalty function is imposed on the control input. The actor-critic based reinforcement learning technique is combined with the barrier transformation to learn the optimal control policy that considers both the full-state constraints and input saturations. To illustrate the efficacy, a numeric simulation is implemented in the end.

In this paper, in order to solve the two-player nonzero-sum (NZS) differential games with completely unknown linear discrete-time dynamics, we develop a data-driven algorithm to learn the Nash equilibrium based on off-policy reinforcement learning (RL). This algorithm is a fully model-free method, which solves the couple algebraic Riccati equations (CAREs) forward in time using measured data along the system trajectories. It is shown that the two-player NZS differential games results in solving the CAREs. Then, model-based on-policy and model-free off-policy RL algorithms are presented to solve the CAREs. Compared to the on-policy RL, the off-policy RL algorithm can eliminate the influence of probing noise to guarantee unbiased solutions. Finally, a simulation example is carried out to show the efficacy of the proposed approach.

Burden distribution matrix is a key part of smooth operation for the blast furnace ironmaking process. It directly influences the burden surface of the blast furnace. Recently, many researchers establish the mathematical model from the burden distribution matrix to the burden surface. However, the research from the burden surface to the burden distribution matrix is rare. Hence, this paper establishes the generative mechanism from the burden surface to the burden distribution matrix. In this paper, our previously improved multi-layer extreme learning machine (named as EAPSO-ML-ELM) is used as the modelling algorithm of the generative mechanism. And the real blast furnace production data are used to verify the generative mechanism. Compared with the generative mechanism based on ML-ELM, the simulation results demonstrate that the generative mechanism based on EAPSO-ML-ELM has better accuracy and generalization performance.

The blast furnace (BF) gas flow distribution is one of the major influences on BF production situation. While burden surface temperature field of BF is a direct reflection of the BF gas flow distribution. In this paper, a prediction model of burden surface temperature field is established based on the online sequential extreme learning machine algorithm with forgetting factor (WOS-ELM). Firstly, the BF condition data and temperature data of the cross temperature measurement are used to establish a temperature prediction model to predict the temperature of the gas flow of the throat. Secondly, the burden surface temperature is obtained by the predicted temperature and heat transfer principle. Finally, the burden surface temperature field is established using Newton interpolation and angle weight. The large amount of data and comparative experiments show that the prediction model of burden surface temperature field has a high prediction accuracy. It can provide the theoretical guidance for the actual operation of the BF.

This note proposes a modified ELM algorithm named P-ELM subject to how to get rid of the multicollinear problem in calculation based on PCA technique. By reducing the dimension of hidden layer output matrix (H) without loss major information through PCA theory, the proposed P-ELM algorithm can not only ensure the full column rank of newly generated hidden layer output matrix ( H ' ), but also improve the training speed. In order to verify the effectiveness of P-ELM algorithm, this paper establishes a soft measurement model for hot metal temperature in the blast furnace (BF). Some comparative simulation results with other famous feedforward neural network and the ordinary ELM algorithm with its variants illustrate the better generalization performance and stability of the proposed P-ELM algorithm.

Periprosthetic joint infection (PJI) is one of the most pivotal issues accounting for clinical failure of titanium (Ti)-based implants. Therefore, it is of great significance to develop an antibacterial coating with high biocompatibility on the Ti implant surface. In this article, a novel zinc-doped strontium phosphate coating, denoted as Sr-Zn-PCC, was deposited on Ti substrates by phosphate chemical conversion (PCC) method. The texture characterizations indicated that continuous coatings containing SrHPO4, Sr3(PO4)2 and SrZn2(PO4)2 phases were formed on the Ti surface. It is found that the doping of Zn could tune the surface microstructure, roughness, wettability and corrosion resistance of the coatings. The Sr-Zn-PCC coating showed the same bacterial adhesion property at short time (2 h), but tended to hinder biofilm formation on coatings after incubation for up to 24 h. Up to 80% of Staphylococcus aureus (S. aureus) were killed within 24 h by contact with the Sr-Zn-PCC surface. Moreover, cytocompatibility assay indicated that MC-3T3 cells had good adhesion, spreading properties on the coatings, suggesting that it is a promising alloy with both excellent antibacterial ability and high biocompatibility for the applications on orthopedic implants.

The burden distribution matrix is an important means of controlling the burden distribution of blast furnace. In order to provide references to foremen of blast furnaces, a model is established in this paper, which is a relationship model between blast furnace parameters and burden distribution matrix, and is based on the extreme learning machine algorithm (ELM). The model decides if the next burden distribution matrix should be adjusted through a series of blast furnace parameters, such as the gas utilization rate, the blast volume, the blast pressure, the top pressure, the blast velocity, the permeability index, and the utilization coefficient. Finally, compared to other methods used LSSVM and PNN, the method based on ELM is faster and more accurate, so it is more suitable.

Gas utilization ratio (GUR) is a significant indicator to measure the operation status and energy consumption of blast furnaces (BFs). Accurately predicting the GUR can reflect the actual operating status of the BFs and the consumption of the charge in real time. Kernel extreme learning machine (KELM) algorithm not only has the characteristics of fast computation speed of extreme learning machine (ELM), but also has better stability and generalization ability. This study applies KELM to investigate the relationship between GUR and some significant factors which affect GUR. An improved fruit fly optimization algorithm (IFOA) is used to optimize the parameters in the KELM model. The experimental results demonstrate that the prediction model based on the KELM has better prediction effect in forecasting accuracy and modeling time needed.

Permeability index of the blast furnace is one of the vital monitoring parameters to reflect the operation status of the blast furnace. At present, there are few prediction models for the permeability index at home and abroad. Therefore, this paper proposes to establish a prediction model of the permeability index by using online sequential extreme learning machine (OS-ELM) combined with wavelet analysis, and this paper compares it with the prediction models established by extreme learning machine (ELM), support vector machine (SVM) and BP neural network algorithm. The simulation results show that the prediction model based on OS-ELM has better accuracy than others.

Burden distribution matrix is the key to guarantee the long-term stable production of the blast furnace. The optimization of the burden distribution matrix aims to form a reasonable burden surface. It can help to achieve the goal of smooth, high-quality and low-consumption blast furnace production. This paper uses the blast furnace condition parameter to measure the burden distribution matrix. And these data is characterized by panel data in statistics. The fuzzy c-means algorithm is used to cluster. Finally, evaluation indicators are using to analyze the clustering effect. It has important reference value for the blast furnace actual production.

Extreme learning machine (ELM) is a competitive machine learning approach for training single hidden layer feedforward neural networks with fast learning speed and good generalization performance. However, ELM also suffers from the imbalanced classification problem, which exists in many fields and degrades the performance of classifier significantly. In order to address this issue, in this paper, we propose a novel parallel one-class ELM based on Bayesian approach for imbalanced classification problem, named P-ELM. In P-ELM, the training dataset is firstly divided into k (k is the number of classes) components according to the class attribution of the samples, and then the divided training datasets will be fed into the corresponding k Kernel-based one-class ELM classifiers. Estimation of the probability density based on the output function of Kernel-based one-class ELM classifier is constructed. Thus, the class attribution of the samples can be judged directly by comparing the values of the output function of each Kernel-based one-class ELM classifier of P-ELM based on the Bayesian approach. The detailed performance comparison and discussions of P-ELM are performed with several related class imbalance learning approaches on some selected benchmark datasets including both the binary classification and the multiclass classification problems, as well as a real world application, i.e., the blast furnace status diagnosis problem. The experimental results show that the proposed P-ELM can achieve better performance.

Gas utilization ratio (GUR) is considered as an important indicator to evaluate the operation status and energy consumption of blast furnace (BF). This paper proposes a modified ensemble framework based on extreme learning machines (ELM), named ME-ELM, to establish a prediction model of GUR. Due to the randomly generated hidden layer parameters, ELM may have variations in different trials of simulations. In order to improve the negative impacts of randomness and obtain better prediction accuracy, we investigate an adaptive ensemble framework via ELMs, which can adjust the ensemble weights automatically according to the contribution of the individual ELM model to the final results. Meanwhile, an attenuation factor is added to highlight the role of new coming data to enhance the dynamic tracking capability. To verity the performance of the proposed ME-ELM, experimental comparisons are carried out with the classic ELM and ensemble ELM (E-ELM) to predict GUR using the actual production data. Simulation results demonstrate that ME-ELM is more stable and accurate than ELM and E-ELM.

This paper presents a novel intermittent suboptimal event-triggered controller design for continuous-time nonlinear systems. The stability of the equilibrium point of the closed-loop system, and the performances are analyzed and quantified theoretically. It is proven that the static and the dynamic event-triggered suboptimal controllers have a known degree of suboptimality compared to the conventional optimal control policy. In order to generate dynamic event-triggering framework, we introduce an internal dynamical system. Moreover, the Zeno behavior is excluded. Finally, a simulation example is conducted to show the effectiveness of the proposed intermittent mechanisms.

In practical applications, the actual models of the robots in teleoperation systems are difficult to be obtained. In addition, the data-communication exhibit asymmetric variable time delays. All of those will definitely affect the performance of the teleoperation system. In this paper, we use multiple neural networks to approximate the uncertain parts of the system, so as to realize the compensation for uncertain terms in the control scheme. By choosing an appropriate Lyapunov functional, we show that the teleoperation system is stable. The stability criteria of the closed-loop teleoperation system are given in terms of linear matrix inequality (LMI). It is shown that the position errors between the master and the slave, the velocities of each robot converge to zero, asymptotically. Simulation studies which are performed on a nonlinear teleoperation system with 2-two-degree-of-freedoms manipulators are given to illustrate the performance of the proposed control scheme in the presence of asymmetric variable time-delays.

In the blast furnace iron-making, furnace condition is directly related to the production efficiency and blast furnace life. Currently, the analysis of furnace condition is mainly based on the artificial experience. Further progress does not exist in the analysis of furnace condition due to the difficulty of the mechanism modeling. In this paper, the biclustering algorithm is applied to the analysis of furnace condition, which is more suitable for the analysis of high dimensional data and has the ability to deal with large-scale data. First, the rank Order-Preserving Submatrix (r-OPSM) algorithm based on hierarchical search is proposed, which improves the robustness of OPSM algorithm to noise and is used for correlation analysis among multiple blast furnace conditions. On this basis, the novel concept of intermediate furnace condition is pointed out, which lays a theoretical foundation for further analysis of the state of the furnace.

This paper presents a data-driven method based on off-policy integral reinforcement learning to solve the semi-global output regulation of continuous-time linear systems with input saturation. A family of state feedback laws for the input constrained output regulation problem is designed based on solving an algebraic Riccati equation. In contrast to the existing methods, complete knowledge of the system dynamics is no longer required in this paper. Instead, the data collected from online implementation is efficiently utilized to design the controller. Therefore, the controller design in this paper is data-driven. It is shown that the presented method can find feedback control inputs with constraint of amplitude saturation and stabilize a given linear system with all its poles inside or on the imaginary axis. Finally, a simulation example is conducted to show the validity of the presented approach to solve the semi-global output regulation of continuous-time linear systems with input saturation.

Hot metal silicon content is an important indicator of measuring the smooth operation of the blast furnace. However, the hot metal silicon content cannot be directly detected online. Hence, this paper establishes a prediction model of the hot metal silicon content based on multi-layer online sequential extreme learning machine (ML-OSELM). ML-OSELM is an online sequential version of multi-layer extreme learning machine (ML-ELM), and it can learn data one-by-one or chunk-by-chunk. It uses online sequential extreme learning machine auto-encoder (OS-ELM-AE) to perform layer-by-layer unsupervised sequential learning. The prediction model of the hot metal silicon content based on ML-OSELM is verified by using the production data of the blast furnace. Simulation results demonstrate that this prediction model can accurately and quickly predict the hot metal silicon content. Moreover, this prediction model can provide efficient decision for subsequent operation of the blast furnace.

This paper presents a model-free distributed event-triggered containment control scheme for linear multiagent systems. The proposed event-triggered scheme guarantees asymptotic stability of the equilibrium point of the containment error as well the avoidance of the Zeno behavior. To relax the requirement of complete knowledge of the dynamics, we combine an off-policy reinforcement learning algorithm in an actor critic structure with the event-trigger control mechanism to obtain the feedback gain of the distributed containment control protocol. A simulation experiment is conducted to verify the effectiveness of the approach.

In this paper, a data-driven method is developed based on off-policy reinforcement learning to solve the semi-global output regulation of discrete-time linear systems with input saturation. Algebraic Riccati equation based method is used to design a family of state feedback laws for the constrained output regulation problem. In contrast to the existing methods, complete knowledge of the system dynamics is no longer required in this paper. Instead, the data collected from on-line is efficiently utilized to obtain the adaptive optimal control policy. It is shown that the presented method can find feedback control inputs with constraint of amplitude saturation and the ability to stabilize a given linear system with all its poles inside or on the unit circle. Finally, a simulation example is carried out to demonstrate the conclusions of the whole paper.

Gas utilization ratio (GUR) is an important indicator that is used to evaluate the energy consumption of blast furnaces (BFs). Currently, the existing methods cannot predict the GUR accurately. In this paper, we present a novel data-driven model for predicting the GUR. The proposed approach utilized both the TS fuzzy neural network (TS-FNN) and the particle swarm algorithm (PSO) to predict the GUR. The particle swarm algorithm (PSO) is applied to optimize the parameters of the TS-FNN in order to decrease the error caused by the inaccurate initial parameter. This paper also applied the box graph (Box-plot) method to eliminate the abnormal value of the raw data during the data preprocessing. This method can deal with the data which does not obey the normal distribution which is caused by the complex industrial environments. The prediction results demonstrate that the optimization model based on PSO and the TS-FNN approach achieves higher prediction accuracy compared with the TS-FNN model and SVM model and the proposed approach can accurately predict the GUR of the blast furnace, providing an effective way for the on-line blast furnace distribution control.

In this paper, bilateral teleoperation of multiple slaves coupled to a single master under scheduling communication is investigated. The sampled-data transmission between the master and the multiple slaves is fulfilled over a delayed communication network, and at each sampling instant, only one slave is allowed to transmit its current information to the master side according to some scheduling protocols. To achieve the master-slave synchronization, Round-Robin scheduling protocol and Try-Once-Discard scheduling protocol are employed, respectively. By designing a scheduling-communication-based controller, some sufficient stability criteria related to the controller gain matrices, sampling intervals, and communication delays are obtained for the closed-loop teleoperation system under Round-Robin and Try-Once-Discard scheduling protocols, respectively. Finally, simulation studies are given to validate the effectiveness of the proposed results., Comment: 13 pages, 12 figures, 4 tables, submitted to IEEE Transactions on Control Systems Technology

It is well known that parameter convergence in adaptive control can bring about an improvement of system performance, including accurate online identification, exponential tracking, and robust adaptation without parameter drift. However, strong persistent-excitation (PE) or sufficient-excitement (SE) conditions should be satisfied to guarantee parameter convergence in the classical adaptive control. This paper proposes a novel adaptive control to guarantee parameter convergence without PE and SE conditions for nonlinear teleoperation systems with dynamic uncertainties and time-varying communication delays. The stability criterion of the closed-loop teleoperation system is given in terms of linear matrix inequalities. The effectiveness of this approach is illustrated by simulation studies, where both master and slave are assumed to be two-link manipulators with full nonlinear system dynamics.

A procedure for guaranteed cost control design of delayed linear bilateral teleoperation systems with nonlinear external forces is proposed. The assumption that the external forces are nonlinear functions of velocities and/or positions of local devices, and one part of these forces satisfies a sector condition has been made. A virtual tool system is introduced to 'observe' the forces at the remote sides, the position and velocity information of the master, the slave and the virtual tool are feedbacked to the controllers, hence the proposed control scheme actually has a four-channel architecture. A delay-dependent stability criterion is formulated, and then a sub-optimal guaranteed cost controller is obtained by solving a convex optimization problem in the form of linear matrix inequalities (LMIs). The behavior of the resulting teleoperation system is illustrated in simulations. (C) 2015 The Franklin Institute. Published by Elsevier Ltd. All rights reserved. (Less)

In this paper, active control of impulsive noise with symmetric α-stable (SαS) distribution is studied. A general step-size normalized filtered-x Least Mean Square (FxLMS) algorithm is developed based on the analysis of existing algorithms, and the Gaussian distribution function is used to normalize the step size. Compared with existing algorithms, the proposed algorithm needs neither the parameter selection and thresholds estimation nor the process of cost function selection and complex gradient computation. Computer simulations have been carried out to suggest that the proposed algorithm is effective for attenuating SαS impulsive noise, and then the proposed algorithm has been implemented in an experimental ANC system. Experimental results show that the proposed scheme has good performance for SαS impulsive noise attenuation.

A procedure for the guaranteed cost control design of delayed bilateral teleoperation systems with nonlinear external forces is proposed. The assumption that the external forces are nonlinear functions of velocities and/or positions of local devices and that one part of these forces satisfies sector condition has been made. A stability criterion is formulated firstly and then the optimal guaranteed cost controller is obtained by solving the equivalent convex optimization problem in the form of linear matrix inequalities (LMIs). The controller preserves closed-loop stability regardless of the delay length. The behavior of the resulting teleoperation system is illustrated in simulations.

Silicon content in hot metal in the iron-making process has been used as a major indicator of the internal thermal state in blast furnace for many years. Due to the harsh environment in the blast furnace, how to measure the silicon content in hot metal is quite difficult. Many efforts have been made on the estimation of the silicon content in hot metal. In this paper, a soft-sensing modeling method based on a modified extreme learning machine is proposed to tackle the problem. In this approach, a modified pruning algorithm is utilized to optimize the weights which are generated randomly in the original ELM algorithm. The real data collected from a blast furnace in the factory are applied and tested by the proposed algorithm, and the results show that the proposed prediction model has less error than the other algorithm such as BP algorithm and support vector method.

This paper solves the containment problem of multi-agent systems on undirected graph with multiple active leaders using off-policy reinforcement learning (RL). The leaders are active in the sense that there exists bounded control input in the dynamics which is unknown to all followers and the followers are heterogeneous with different dynamics. Not only the steady states of agent i but also the transient trajectories are taken into account to impose optimality to the proposed containment control. Inhomogeneous algebraic Riccati equations (ARE) are derived to solve the optimal containment control protocol. To avoid the requirement of agents' dynamics to obtain containment control, an off-policy RL algorithm is developed to solve the inhomogeneous AREs online in real time and without requiring any knowledge of the agents' dynamics. Finally, a simulation example is presented to illustrate the effectiveness of the proposed algorithm.