Search

Globally, millions of people die of microbial infection-related diseases every year. The more terrible situation is that due to the overuse of antibiotics, especially in developing countries, people are struggling to fight with the bacteria variation. The emergence of super-bacteria will be an intractable environmental and health hazard in the future unless novel bactericidal weapons are mounted. Consequently, it is critical to develop viable antibacterial approaches to sustain the prosperous development of human society. Recent researches indicate that transition metal sulfides (TMSs) represent prominent bactericidal application potential owing to the meritorious antibacterial performance, acceptable biocompatibility, high solar energy utilization efficiency, and excellent photo-to-thermal conversion characteristics, and thus, a comprehensive review on the recent advances in this area would be beneficial for the future development. In this review article, we start with the antibacterial mechanisms of TMSs to provide a preliminary understanding. Thereafter, the state-of-the-art research progresses on the strategies for TMSs materials engineering so as to promote their antibacterial properties are systematically surveyed and summarized, followed by a summary of the practical application scenarios of TMSs-based antibacterial platforms. Finally, based on the thorough survey and analysis, we emphasize the challenges and future development trends in this area.

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.

A neural-network-based adaptive control scheme is developed for bilateral teleoperation systems with single-master-multiple-slaves in the presence of dynamic uncertainties and communication constra...

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.

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.

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 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.

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.

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.

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

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)

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.

Gas utilization ratio (GUR) is an important indicator used to measure the operating status and energy consumption of blast furnaces (BFs). In this paper, we present a soft-sensor approach, i.e., a novel online sequential extreme learning machine (OS-ELM) named DU-OS-ELM, to establish a data-driven model for GUR prediction. In DU-OS-ELM, firstly, the old collected data are discarded gradually and the newly acquired data are given more attention through a novel dynamic forgetting factor (DFF), depending on the estimation errors to enhance the dynamic tracking ability. Furthermore, we develop an updated selection strategy (USS) to judge whether the model needs to be updated with the newly coming data, so that the proposed approach is more in line with the actual production situation. Then, the convergence analysis of the proposed DU-OS-ELM is presented to ensure the estimation of output weight converge to the true value with the new data arriving. Meanwhile, the proposed DU-OS-ELM is applied to build a soft-sensor model to predict GUR. Experimental results demonstrate that the proposed DU-OS-ELM obtains better generalization performance and higher prediction accuracy compared with a number of existing related approaches using the real production data from a BF and the created GUR prediction model can provide an effective guidance for further optimization operation.

Restarting with speed for PMSM is critical to Rail Train application. And the switching frequencies must be restricted to several hundred hertz to minimize the switching losses. In order to output more torque with limited DC supply voltage, square wave should be used in the condition of high speeds. Hybrid synchronized PWM control schemes are often used to satisfy these restrictions. The restart and control schemes contain a variety of PWM patterns which is applicable to the whole speed range. And a simple but very effective method is proposed to determine the PWM patterns that should be used when restart is enabled. The motor speed feedback as a flag is employed in this method. An improved current controller for IPMSM is proposed to solve the zero speed start problem.

In this paper, a novel frame work of reinforcement learning for continuous time dynamical system is presented based on the Hamiltonian functional and extreme learning machine. The idea of solution search in the optimization is introduced to find the optimal control policy in the optimal control problem. The optimal control search consists of three steps: evaluation, comparison and improvement of arbitrary admissible policy. The Hamiltonian functional plays an important role in the above framework, under which only one critic is required in the adaptive critic structure. The critic network is implemented by the extreme learning machine. Finally, simulation study is conducted to verify the effectiveness of the presented algorithm.

A procedure for acceleration feedback control of the delayed nonlinear teleoperation system is proposed. The acceleration feedback is applied to the master controller, while in the slave side the position error plus nonlinear damping controller is used to guarantee the input-to-state stability (ISS) of the controlled system. The Lyapunov–Krasovskii methodology is applied to analyse the ISS of the closed-loop system, and hence the passivity assumption for human operators and the environment is not required. The stability criterion is formulated in the form of linear matrix inequalities. The behaviour of the resulting teleoperation system is illustrated in simulations, while the simulation results show that the acceleration feedback controller not only preserves good position tracking performance but also eludes contact instability.

It is well known that the feedforward neural networks meet numbers of difficulties in the applications because of its slow learning speed. The extreme learning machine (ELM) is a new single hidden layer feedforward neural network method aiming at improving the training speed. Nowadays ELM algorithm has received wide application with its good generalization performance under fast learning speed. However, there are still several problems needed to be solved in ELM. In this paper, a new improved ELM algorithm named R-ELM is proposed to handle the multicollinear problem appearing in calculation of the ELM algorithm. The proposed algorithm is employed in bearing fault detection using stator current monitoring. Simulative results show that R-ELM algorithm has better stability and generalization performance compared with the original ELM and the other neural network methods.

In view of the fact that the traditional genetic algorithm easily falls into local optimum in the late iterations, an improved chaos genetic algorithm employed chaos theory and genetic algorithm is presented to optimize the low side-lobe for T-shaped MIMO radar antenna array. The novel two-dimension Cat chaotic map has been put forward to produce its initial population, improving the diversity of individuals. The improved Tent map is presented for groups of individuals of a generation with chaos disturbance. Improved chaotic genetic algorithm optimization model is established. The algorithm presented in this paper not only improved the search precision, but also avoids effectively the problem of local convergence and prematurity. For MIMO radar, the improved chaos genetic algorithm proposed in this paper obtains lower side-lobe level through optimizing the exciting current amplitude. Simulation results show that the algorithm is feasible and effective. Its performance is superior to the traditional genetic algorithm.

In this paper, we present a novel embedded-target detection system built upon a visible image sensor. This system can detect both static and dynamic targets under difficult situations. It is small in size, light in weight, has low power consumption, and can be embedded into the system of robotics and intelligent systems. In this system, the target images are captured by a visible image sensor and then processed by a digital signal processor based on the ripple algorithm proposed in this paper. The ripple algorithm uses several templates to represent the target in different view angles and sizes. Then, it extracts the features of objects by a group of concentric circles, and compares the features to those of the templates. These features are invariant to image rotations or target translations and scaling to overcome the robotics moving characteristics of swing and sway. In addition, the algorithm has low computational complexity, and can fulfill the requirement of real-time applications. From this experiment, it is shown that the developed automatic target detection system is effective to detect static or dynamic targets in the real-time application of robotics, and the ripple algorithm is correct and effective.

The fault signal is a non-stationary and nonlinear signal, and because of the complexity of the field environment, the fault signal often has a lot of noise interference. In order to reduce the noise interference to the greatest extent, a feature extraction method based on digital filter and the local mean decomposition is proposed. Firstly, the Fourier transform is used to obtain the dominant frequency of the signals. Then, an IIR low-pass digital filter is designed to achieve the effect of noise reduction. Finally, the de-noised signal is decomposed by local mean decomposition. Every component PF can be represented as the product of the envelope signals and the frequency modulated signals. The component PF1 containing the highest power is selected to conduct energy spectrum analysis, and the fault features are exacted. The results show that the method can effectively extract the fault features, proving the feasibility of the proposed method.

An online monitoring system of alumina concentration in aluminium electrolysis has been designed directing at the difficult concentration measurement of alumina. Firstly, a current-measuring instrument with anodic guide rod is developed to collect data about the temperature of anodic guide rod and isometric pressure drop. Then a set of upper-computer software based on COM technology is produced, which consists of OPC server and OPC client software. Eventually, preliminary analysis on the collected data is conducted, least-square support vector machine algorithm optimized by particle swarm is applied to take the training and prediction regarding alumina concentration. Thus achieving the online real-time display of alumina concentration, with mean absolute measuring error reaching 2.631%, which is instructive and meaningful to electrolytic aluminium production process.

The servo systems are always difficult to design with high-accuracy due to nonlinear stick-slip at the low velocities and the uncertainties of friction dynamic model. A linear active disturbance rejection control (ADRC) dynamic compensation solution is presented independent of the friction model. To deal with the problem of observing or tracking for unknown discontinuous signals of friction with Stribeck effect, a kind of linear extended state observer (LESO) algorithm is designed. Then a linear control law is proposed to compensate the friction disturbance dynamically. In comparison with proportional-integral-derivative (PID) controller, the simulation results show that the proposed method not only has better accuracy of the discontinuous friction signal tracking, but also ensures better dynamic friction compensation at the low velocities.

This study deals with the estimation of the stability domain of the spacecraft attitude control system with delayed feedback. For autonomous functional–differential equations, by using the invariance principle, a general estimation theorem is established to explicitly estimate the stability domain. Furthermore, based on the estimation theorem and an applicable Lyapunov functional, an estimation criterion of the stability domain for the spacecraft attitude control system with delayed proportional-derivative (PD) controller is obtained. It is worth noting that the estimation criterion can be described as a generalised eigenvalue problem and thus can be solved by linear matrix inequality (LMI) optimisation techniques. Finally, numerical examples show the effectiveness of the estimation criterion.

A temperature model of the electrical heating furnace which is commonly used in industry is built by means of Elman neural network combined with an improved particle swarm optimization (IPSO). The input is duty cycle, and IPSO is used to optimize the weights and threshold values of Elman neural network to improve convergence capability and generalization performance of Elman network. Results show that the method performs better than ordinary Elman network in convergence speed and accuracy.

Terahertz (THz) absorption spectra have been applied to measure the compositions of amino acid samples. Because the concentration information about a specific composition is involved in its spectrum, keeping the spectrum length and dynamic range as wide as possible is essential for precise measurements. It is well known that a spectrum is always contaminated by apparatus noises. In this paper, we present a criterion for noise cutting based on wavelet transform, which usually extends the range determined by traditional threshold criterion. Finally, the improvement on measurement precision shows the validity of the new criterion.

A new potential field method for mobile robot path planning is proposed in this paper. At present, most potential field methods are designed to be applied in the stationary environment, and several improved potential functions have brought in the velocity factors in the dynamic circumstances. Based on the consideration that the moving trend of the robot in the dynamic environments is also necessary to produce more reasonable path, this paper defines new attractive potential function with respect to the relative position, velocity, and acceleration between the robot and the goal, as well as the repulsive potential function with respect to the relative positions, velocities, and accelerations between the robot and the obstacles. The virtual forces are calculated to make the robot plan its motion, not only with right positions, but also with suitable velocities. Furthermore, the robot will keep a similar moving trend with the goal and contrary trends with the obstacles. Finally, some methodic simulations are carried out to validate and demonstrate the effectiveness of the new potential field method.

In this paper, a local-learning algorithm for multi-agent is presented based on the fact that individual agent performs local perception and local interaction under group environment. As for individual- learning, agent adopts greedy strategy to maximize its reward when interacting with environment. In group-learning, local interaction takes place between each two agents. A local-learning algorithm to choose and modify agents’ actions is proposed to improve the traditional Q-learning algorithm, respectively in the situations of zero-sum games and general-sum games with unique equilibrium or multi-equilibrium. And this local-learning algorithm is proved to be convergent and the computation complexity is lower than the Nash-Q. Additionally, through grid-game test, it is indicated that by using this local-learning algorithm, the local behaviors of agents can spread to globe.

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.

This paper presents a six-layer Aluminum Industry 4.0 architecture for the aluminum production and full lifecycle supply chain management. It integrates a series of innovative technologies, including the IoT sensing physical system, industrial cloud platform for data management, model-driven and big data driven analysis & decision making, standardization & securitization intelligent control and management, as well as visual monitoring and backtracking process etc. The main relevant control models are studied. The applications of real-time accurate perception & intelligent decision technology in the aluminum electrolytic industry are introduced.

This article uses the phased array radar data to estimate burden surface shape. First of all, using weighted least squares method to estimate burden material line shape of the blast furnace ; Secondly, b-spline interpolation method is used for surface fitting of three dimension blast furnace. Then, using VS2013/Qt5 as a development platform to develope a simulation platform for the blast furnace burden distribution based on the fitting method. Using the actual radar measurement data of a steel mill, confirmed the effectiveness of the method.

The concentration of alumina in the electrolyte is of great significance during the production of aluminum; it may affect the stability of aluminum reduction cell and the current efficiency. However, the concentration of alumina is hard to be detected online because of the special circumstance in the aluminum reduction cell. At present, there is lack of fast and accurate soft sensing methods for alumina concentration and existing methods can not meet the needs for online measurement. In this paper, a novel soft sensing method based on the voltage between the anode and the cathode, the current of the anode rod and the modified extreme learning machine for online measurement of the alumina concentration is proposed. In order to achieve the soft sensing scheme, considering the problems on the work spot, such as anode effect, the paper mainly develops a system that can realize the online measurement of the voltage between the anode and the cathode. The hardware of the system is composed of three parts including protection circuit, filter circuit and data acquisition card. When the anode effect occurs, the protection circuit which is used to ensure the safe use of the acquisition device will switch off the connection between the voltage and the data collection card. When the voltage returns to normal level, it will switch on the connection. The modified ELM algorithm is based on the enhanced random search which is called incremental extreme learning machine in some references. It randomly chooses the input weights and analytically determines the output weights without manual intervention. The experimental results show that the approach can give accurate estimations of alumina concentration within 6 percent relative error.

The anode current is a very important parameter in the aluminum electrolysis production. However at present, manual measurement is still being used in the aluminum industrial. There are some shortcomings, such as low measurement precision, data lag, etc. This approach can't meet the requirements of the modern management and production for aluminum electrolysis. Based on the principle of anode rod equidistant voltage drop and weak signal detection, this paper designs the online measuring instrument for anode current distribution with STM32 as control core, and detailed describes the composition of the hardware circuit and the software programing. The measuring instrument has the function of collection and storage of the real-time data, and also can communicate with the host computer by RS-485. Experimental results in the laboratory and industrial site show that the measuring instrument has high measurement accuracy of up to 0.379% and high reliability, the communication system is kept in a good condition, and realizes the online detection of the anode current distribution.

This note proposes an adaptive fault diagnosis scheme for the continuous time-delay repetitive system subject to the sensor fault. The theory of iterative learning control(ILC) is employed in the fault reconstructed method. With the increase of iterative step, the sensor fault estimation signal can track the actual fault tightly including slow- and fast- varying fault. In order to verify the effectiveness of the proposed fault diagnosis method, three kinds of sensor faults are taken into consideration in the simulation part. The results show that the reconstructed fault signal can track the actual fault well despite of the kinds of sensor faults.

An online detecting system of aluminum electrolytic anode current is designed. This system adopts the architecture of state machine based on queue to solve a problem of real time response between data communication and human-computer interaction by using multithreading. It can detect real-time current distribution of anode rod and to deal with abnormal alarm. Under each work condition of aluminum electrolysis, the time-frequency characters of current signals are explored using multi-resolution analysis. It can determine the time points of beginning and ending of each state. By analysis of Daubechies12 wavelet functions, the vibration frequency of magneto fluid in aluminum cells is between 0.0048Hz and 0.0098Hz. Through FFT transform, Frequency of magneto fluid can be identified as 0.005Hz.

Due to the nonlinearities, model uncertainties and disturbances of the two-tank liquid level cascade system, active disturbance rejection controller (ADRC) solution was developed. First, a nonlinear mechanism model and its linearized model were built based on the theories of fluid dynamics for the system. Second, first-order linear ADRCs for the outer loop of the two cascade systems with an unknown order were designed, respectively. With a simple integrator as the normalized type of the feedback system, other parts of dynamic system which are different from the normalized type are treated as the total disturbances. The disturbances were estimated by the second-order linear extended state observer and compensated in the control law. Then the system, with nonlinearities, uncertainties and disturbances, is reduced to only a linear integrator. In comparison with the traditional PI controller, simulation results show that both the proposed ADRCs have better robustness and adaptability against model uncertainties.

A novel active disturbance rejection control (ADRC) solution and a particular tuning method are presented for a class of time delay system (TDS) with uncertainty. First, the complicated process dynamics is modeled as a simple first order plus large time delay (FOPTD) plant, with the difference between the actual dynamics and its model treated as disturbances to be rejected. Then the reduced order linear extended state observer (RLESO) with input delay is proposed to estimate the time delay state and disturbance. It is shown how the time delay could be eliminated from the characteristic equation of the closed-loop system by manipulations of controller parameters. Secondly, the one parameter tuning (OPT) technique is developed where all controller parameters are made function of a single coefficient. In comparison with optimal proportional-integral-derivative (PID) controller and twice optimum controller (TOC), the simulation results show that the proposed method not only has better accuracy and faster response, but also ensures better robustness and adaptability against uncertain model parameters and external disturbances, especially for the plant with very large time delays.

Due to the nonlinearities, disturbances and uncertainties of the single water tank system, an active disturbance rejection control (ADRC) solution with high-precision was proposed. Firstly, a nonlinear mechanism model was built based on the theories of fluid dynamics for water tank system. Then, the nonlinear model was linearized in static work point to get its simplified model. Secondly, first-order linear ADRC solutions for the system models with unknown order were designed. With a simple “integrator series” as the normalized type of the feedback system, the parts of dynamic system is different from the normalized type are treated as the total disturbance. So it was estimated by the second-order linear extended state observer and canceled out in the control law. The system, with disturbances, uncertainties and nonlinearities, is reduced to the canonical form of the linear chain of integrators. Simulation results show that both the ADRCs have better performances than PID controller on disturbance-rejection and robustness.

The concentration of alumina in the electrolyte is of great significance during the production of aluminum; it may affect the stability of aluminum reduction cell and the current efficiency. However, the concentration of alumina is hard to be detected online because of the special circumstance in the aluminum reduction cell. At present, there is lack of fast and accurate soft sensing methods for alumina concentration and existing methods can not meet the needs for online measurement. In this paper, a novel soft sensing method based on a modified extreme learning machine (MELM) for online measurement of the alumina concentration is proposed. The modified ELM algorithm is based on the enhanced random search which is called incremental extreme learning machine in some references. It randomly chooses the input weights and analytically determines the output weights without manual intervention. The simulation results show that the approach can give more accurate estimations of alumina concentration with faster learning speed compared with other methods such as BP and SVM.

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.

In view of Hydraulic Automatic Position Control (HAPC) with uncertainty and nonlinearity, the model of HAPC system is established. Then, a controller independent on the model is designed. And particle swarm optimization (PSO) is presented for parameters optimization of the ADRC controller. Finally, ADRC optimized by PSO is selected as the controller for HAPC system and the simulation results show that it has better performance in tracking, anti-interference and robustness compared with other three algorithms.

Firstly, the static fuzzy controller is designed and comparative simulation studies with the artificial potential field method show that it has better performance such as shorter path length, less computing time and local minimum and unreachable problems are avoided. Specific obstacle avoidance strategy is proposed for the partially unknown environment and simulation results illustrate its effectiveness. Moreover, improved control rules are developed respectively for u-slot and maze environments by modifying and optimizing some rules of the universal fuzzy controller.

This paper proposes a human motion tracking approach for a daily life surveillance in a distributed wireless sensor network using ultrasonic range sensors. Because the human target often moves with high nonlinearity, the proposed approach applies the unscented Kalman filter (UKF) technique. A novel sensor node selection scheme at each time step considering both the tracking accuracy and the energy cost is presented. Experimental results in a real human motion tracking system show that the proposed approach can perform better tracking accuracy compared to the most recent human motion tracking scheme in the real testbed implementation.

Smith predictor is presented for monitor finishing automatic width control (M-FAWC) system to deal with the large transport time-delay. With the minimal settling time with a constraint, the optimal parameters of Smith predictor are tuned based on the mutative scale chaos optimization method to meet high performance of strip width. The simulation results of the M-FAWC system show that the optimized Smith predictor has better dynamic performance than the conventional PID controller, and the chaos optimization algorithm is effective.

A practical data-based third-order linear extended state observer (ESO) filter is proposed based on state estimated by ESO. The filter is simple to use with only one parameter to tune. Then its transfer function, parameters tuning method and discrete simulation realization are given respectively. The proposed ESO filter is applied to the tracking of sine signal and square signal, the filter of signal with white Gaussian noise, and the filter of the width data for hot strip rolling mill in real time with measurement noise. Simulation results show the proposed ESO filter is practical without signal model, noise model and statistical characteristic of the measurement noise. Only according to present moment of the measurement data, it could meet the requirement with appropriate parameter.

In this paper, active control of periodic impulsive noise is studied. The stability of an active noise control (ANC) system using repetitive control (RC) algorithm is first analysed. Based on the stability condition, a novel non-causal stable inversion approach combined with an optimal criterion is used to design the RC controller for an ANC system with a non-minimum phase secondary path. A non-causal transversal finite impulse response (FIR) filter is used as the RC learning filter to compensate the phase lag of the plant. Computer simulations have been carried out to validate the effectiveness of the proposed algorithm. The plant model used in the computer simulations is obtained from a practical ANC system in our laboratory. Simulation results show that the proposed scheme can significantly reduce periodic impulsive noise and the convergence rate is fast for a non-minimum phase plant.

Considering of the practical production status of gas scheduling in iron and steel enterprises, a dynamic model of gas system is got, and a static model which can be used for further optimization analysis also derived. In the end, combined with the executive process, a simulation system about gas scheduling is established, and using this system, the practicability of this method is demonstrated.