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This paper investigates the controller design for the tracking problem of uncertain nonlinear systems with constraints on input and output. First, to avoid the full-state measurement, the high-gain observer is designed to estimate the unmeasured state. Compared to existing observer design for nonlinear systems, the high-gain observer design only requires a modified version of the Lipschitz condition. The output feedback controller is further presented on this basis. Second, to consider the transient constraints on the tracking performance, a barrier Lyapunov function with the user-defined time-varying performance is developed. In addition, the proposed controller design is shown to be free of control direction singularity. The convergence of the learning scheme and the boundedness of all the closed-loop signals during the learning phase are discussed theoretically. Finally, two simulation examples are conducted to verify the advantage of the proposed adaptive output feedback controller design.

Hot metal silicon content is an important indicator for measuring the smooth operation of the blast furnace. However, the hot metal silicon content cannot be directly detected online. Hence, this paper proposes a prediction model of the hot metal silicon content based on the improved multi-layer online extreme learning machine (ML-OSELM). The improved ML-OSLEM algorithm is based on ML-OSELM, the variable forgetting factor (VFF) and the ensemble model. VFF is introduced to make the new coming data get more emphasis. The ensemble model can overcome the overfitting problem of ML-OSELM. This improved algorithm is named as EVFF-ML-OSELM. The real blast furnace production data are used to testify the established prediction model based on EVFF-ML-OSELM. Compared with the prediction models of the hot metal silicon content based on other algorithms, the simulation results demonstrate that the prediction model based on EVFF-ML-OSELM has better prediction accuracy and generalization performance.

This paper develops a novel adaptive optimal control design method with full-state constraints and input saturation in the presence of external disturbance. First, to consider the full-state constraints, a barrier function is developed for system transformation. Moreover, it is shown that, with the barrier-function-based system transformation, the stabilization of the transformed system is equivalent to the original constrained control problem. Second, the disturbance attenuation problem is formulated within the zero-sum differential games framework. To determine the optimal control and the worst-case disturbance, a novel barrier-actor-critic algorithm is presented for adaptive optimal learning while guaranteeing the full-state constraints and input saturation. It is proven that the closed-loop signals remain bounded during the online learning phase. Finally, simulation studies are conducted to demonstrate the effectiveness of the presented barrier-actor-critic learning algorithm.

This paper presents an observer-based adaptive control for uncertain nonlinear systems with input saturation and output constraints. Backstepping technology is used in the controller design. A state observer is established to estimate the unmeasured states. An asymmetric barrier Lyapunov function (BLF) with prescribed performance is constructed to deal with the output constrains. The input saturation is solved by using Nussbaum function-based method. The dynamic surface control (DSC) technique is employed to reduce the complexity of the virtual control. It is illustrated that all the closed-loop signals are semi-global uniformly ultimately bounded (SGUUB). Finally, a simulation example verifies the proposed results.

Composite adaptive control schemes, which use both the system tracking error and the prediction error to drive the updating law, have become widespread in achieving an improvement of system performance. However, a strong persistent-excitation (PE) condition is required to guarantee the convergence of the parameter estimation errors. This paper proposes a novel composite adaptive control for nonlinear teleoperation systems with dynamic uncertainties and time-varying communication delays, by which the parameter convergence is achieved without the PE condition. The novelty lies in the construction of the prediction errors by designing lower-bounded gain matrices of the prediction errors. The stability criteria of the closed-loop teleoperation system are given in terms of linear matrix inequalities. Simulation studies are given to show 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.

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.

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 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.

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.

This paper investigates the stabilization problem of discrete-time two-dimension (2-D) Takagi-Sugeno (T-S) fuzzy systems. Based on a novel non-parallel distributed compensation (non-PDC) control scheme combined with a new non-quadratic Lyapunov function, less conservative stabilization conditions are developed. The proposed non-quadratic Lyapunov function is homogeneous polynomially parameter-dependent on membership functions. As the degree of the Lyapunov function increases, the conservatism of the obtained stabilization conditions is gradually reduced. By exploiting the algebraic property of membership functions, the stabilization conditions approach to exactness in the sense of convergence. Compared with the existing methods, no slack variables are introduced in control synthesis, and hence the same or less conservative results can be obtained with a lower computational cost. A numerical example is given to illustrate the effectiveness of the proposed method.

Particle swarm optimization (PSO) was originally developed for continuous problem. To apply PSO to a discrete problem, the standard arithmetic operators of PSO are required to be redefined over discrete space. In this paper, a concept of distance over discrete solution space is introduced. Under this notion of distance, the PSO operators are redefined. After reinterpreting the composition of velocity of a particle, a general framework of discrete PSO algorithm is proposed. As a case study, we illustrate the application of the proposed discrete PSO algorithm to number partitioning problem (NPP) step by step. Preliminary computational experience is also presented. The successful application shows that the proposed algorithmic framework is feasible.

According to the characteristics of distributed industrial control process, we analyzed the feasibility and necessity that multi-agent technology was applied to industrial complicated control process. A multi-agent system architecture on the basis of industrial process and cooperation control of the system were described as well. Strip hot rolling process is a typical distributed control problem we met in metallurgy. We proposed a multi-agent structure of hot rolling process from the view of synthesized control. The composition and relationship of every function module was described in detail. The cooperation control process of the main device was described in detail. The cooperation control process of flatness and gauge was analyzed in the article.

Micro-electro-mechanical system (MEMS) technology offers sensors with lower cost, smaller size, and lower power consumption. In this paper, a kind of low cost motion-sensing system based on MEMS sensors is developed. The objective of the design is: small volume, light weight and low cost, in order to be used in many fields. The constituting principle of the system is described and the algorithms and hardware of the system are researched. The definition of coordinates, calculation of pose angles, transform of acceleration and calculation of the velocities and displacement of the moving object are presented with the corresponding mathematics model and the associated algorithms. The experiments are carried out in principle and the results are given. It is proved that the low cost motion-sensing system is effective and accurate.

Because vehicles moving over ground generate a succession of impacts, the soil disturbances propagate away from the source as seismic waves. Thus, in the battlefield environment, we can detect moving ground vehicles by means of measuring seismic signals using a seismic velocity transducer, and automatically classify and recognize them by advance signal processing method. Because seismic sensor is easy to be developed by emerging micro-electro-mechanical system (MEMS) technology, seismic detection that will be low cost, low power, small volume and light weight is a promising method for moving ground targets. Such a detection method can be used in many different fields, such as battlefield surveillance, traffic monitoring, law enforcement and so on. The paper researches seismic signals of typical vehicle targets in order to extract features of seismic signal and to recognize targets. As a data fusion method, the technique of artificial neural networks (ANN) is applied to recognize seismic signals for vehicle targets. An improved BP algorithm and ANN data fusion architecture have been presented to improve learning speed and avoid local minimum points in error curve. The algorithm had been used for classification and recognition of seismic signals of vehicle targets in the outdoor environment. It can be proven that moving ground vehicles can be detected by measuring seismic signal, feature extraction of target seismic signal is correct and ANN data fusion is effective to solve the recognition and classification problem for moving ground targets.