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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, an off-policy reinforcement learning method is developed for the robust stabilizing controller design of discrete-time uncertain linear systems. The proposed robust control design consists of two steps. First, the robust control problem is transformed to an optimal control problem. Second, the off-policy RL method is used to design the optimal control policy which guarantees the robust stability of the original system with uncertainty. The condition for the equivalence between the robust control problem and the optimal control problem is discussed. The off-policy does not require any knowledge of the system knowledge and efficiently utilize the data collected from on-line to improve the performance of approximate optimal control policy in each iteration successively. Finally, a simulation example is carried out to verify the effectiveness of the presented algorithm for the robust control problem of discrete-time linear system with uncertainty.

In this paper, based on the Hamiltonian, an alternative interpretation about the iterative adaptive dynamic programming (ADP) approach from the perspective of optimization is developed for discrete time nonlinear dynamic systems. The role of the Hamiltonian in iterative ADP is explained. The resulting Hamiltonian driven ADP is able to evaluate the performance with respect to arbitrary admissible policies, compare two different admissible policies and further improve the given admissible policy. The convergence of the Hamiltonian ADP to the optimal policy is proven. Implementation of the Hamiltonian-driven ADP by neural networks is discussed based on the assumption that each iterative policy and value function can be updated exactly. Finally, a simulation is conducted to verify the effectiveness of the presented Hamiltonian-driven ADP.

Klf5, a member of the Krüppel-like transcription factor family, has essential roles during embryonic development, cell proliferation, differentiation, migration and apoptosis. This study was to define molecular mechanism of Klf5 during the odontoblastic differentiation. The expression of Klf5, odontoblast-differentiation markers, Dspp and Dmp1 was co-localized in odontoblastic cells at different stages of mouse tooth development and mouse dental papilla mesenchymal cells. Klf5 was able to promote odontoblastic differentiation and enhance mineral formation of mouse dental papilla mesenchymal cells. Furthermore, overexpression of Klf5 could up-regulate Dspp and Dmp1 gene expressions in mouse dental papilla mesenchymal cells. In silico analysis identified that several putative Klf5 binding sites in the promoter and first intron of Dmp1 and Dspp genes that are homologous across species lines. Electrophoretic mobility shift assay and chromatin immunoprecipitation analysis indicated that Klf5 bound to these motifs in vitro and in intact cells. The responsible regions of Dmp1 gene were located in the promoter region while effect of Klf5 on Dspp activity was in the first intron of Dspp gene. Our results identify Klf5 as an activator of Dmp1 and Dspp gene transcriptions by different mechanisms and demonstrate that Klf5 plays a pivotal role in odontoblast differentiation.

The relationship between the burden surface and the gas flow in blast furnace was studied in this paper using the improving k-means algorithm and graded case based matching method. An improved k-means classification algorithm was proposed based on the evaluation of effectiveness index to study the relationship between the burden surface and the gas flow in blast furnace from historical data, which proved the proposed algorithm has high accuracy according to the experimental data and different standard data sets. The paper also proposed a matching algorithm on the basis of the above clustering algorithm to obtain the most matched historical burden surface with the current burden surface. At last, compared with both of the improved grey similarity matching algorithm and Euclidean nearest neighbor matching algorithm, the results showed that the proposed method has higher efficiency and matching accuracy, and it is more suitable for the research of the relationship between burden surface and gas flow to assis...

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 order to predict the distribution of gas flow, we need to get the temperature of each point in blast furnace throat in advance. In this paper, firstly, two intelligent modeling methods are used to establish a multiple-input multiple-output prediction model, one is extreme learning machine (ELM) algorithm and the other is online sequential extreme learning machine (OS-ELM) algorithm. And the model is a single-step prediction model of temperature in blast furnace, single-step prediction means the prediction of temperature in the next moment. We use autocorrelation analysis to determine input vector and output vector of the model. The result of autocorrelation analysis indicates that the method of temperature sequence prediction has a higher prediction accuracy and better prediction stability than the method of single point prediction. Next, based on real industrial data, we make a comparison between the multiple - input multiple-output model and least squares support vector machines (LS-SVM) model used in common. The experiment results show that OS-ELM model has a better forecast effect than the ELM model and LS-SVM model.

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 burden distribution real-time estimation problem of multi-loop charging based on the real multi-radar data is resolved. Firstly, an iterative algorithm is introduced to calculate the radial coordinate of the pile-top. Then, based on the multi-radar data, the burden profile is estimated by a cubic-curve equation at the end of the multi-loop charging. Furthermore, the burden profile before the next multi-loop charging is calculated based on multi-radar data by considering the impact of burden descent. On the basis of these burden profiles, a more accurate thickness ratio of ore to coke (Ro/c) at the radial direction of blast furnace can be obtained. Finally, an example is given to calculate the burden profiles and Ro/c by using the real multi-radar data sampled from Baosteel, which shows the effectiveness of the method introduced.

Considering the complexity of aluminum electrolysis process, transfer function model of single anode of aluminum electrolytic cell was obtained by means of system identification. Firstly, the frequency characteristics of single anode were measured and analyzed. Secondly, transfer function model was built. Finally, the measured frequency characteristic data was identified to be a high-precision transfer function with Levys method. Then, simulation experiment was carried out on the mathematical model obtained via identification. The comparison between the simulation result and the measured data shown that the maximum absolute error of the amplitude in low and middle frequency bands is less than 0.727dB. The maximum absolute phase error is less than 8.22. Experiments shown that the identification results of the transfer function can fully describe the characteristics of this system, which is of instructive significance to the production, operation and management of electrolytic cell.

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.

To deal with the problem of observing or tracking for discontinuous signal, a kind of second order linear extended state observer (LESO) algorithm is proposed. By ESO estimating the uncertain disturbance, a simple second-order linear ESO with less tunable parameters was designed in the absence of dynamic model, and the method of discrete time realization was given. The proposed LESO was applied to the tracking of discontinuous Stribeck Friction in comparison with adaptive state observer. Simulation results show that the proposed ESO is effective and practical.

This paper investigates the problem of nonfragile H∞ and H2 filter designs for continuous-time linear systems. Additive filter gain variations to reflect the imprecision in filter implementation are considered. The nonfragile filter design is first formulated as a robust convex optimization problem. Then, both deterministic and randomized algorithms are employed to solve the obtained robust convex optimization problem. Compared with the deterministic algorithm, the proposed randomized one has two advantages: On one hand, it has acceptable computational complexity for systems with high dimensions; on the other hand, it can alleviate the conservatism of deterministic algorithms. Several examples are given to illustrate the effectiveness of the proposed method.

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.

This brief is concerned with the Lyapunov-type stability theorem of general 2-D nonlinear parameter-varying Fornasini-Marchesini (FM) second model. First, three comparison lemmas are given and play an important role in the stability analysis. Then, it is shown that a general 2-D nonlinear parameter-varying FM second model is uniformly stable (or uniformly asymptotically stable and exponentially stable) if it admits a corresponding continuous Lyapunov-type function.

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.

Procedure for acceleration feedback control of teleoperation systems with asymmetric time-varying delays is proposed. The acceleration feedback is applied to the master controller, while in the slave side the position error plus damping controller is used. The ISS/IOS small-gain theory is applied to analyze the stability of the closed-loop system, and hence the passivity assumption for the human operator and the environment is not required. Simulation results are presented, which show that the resulting teleoperation system is stable with good tracking performance between the master and the slave.

This study investigates the problem of state feedback robust stabilisation for discrete-time fuzzy singularly perturbed systems (SPSs) with parameter uncertainty. The considered system is approximated by Takagi–Sugeno fuzzy model. Based on a matrix spectral norm approach, new sufficient conditions, which ensure the existence of state feedback controller such that the resulting closed-loop system is asymptotically stable, are given. The gains of controllers are obtained by solving a set of ɛ-independent linear matrix inequalities (LMIs) such that, the ill-conditioned problems caused by ɛ can be easily avoided. In contrast to the existing results, the proposed method can be applied to both certain and uncertain SPSs with greater singular perturbation parameter ɛ. A numerical example is provided to illustrate the reduced conservatism of the authors’ results.

This paper presents the novel results for stabilizing uncertain standard discrete-time fuzzy singularly perturbed systems (SPSs) via a state feedback control law. Two standard discrete-time fuzzy SPSs are constructed firstly by using the Takagi-Sugeno (T-S) fuzzy model. Based on a matrix spectral norm approach, two new e-dependent stability conditions are derived, which guarantee the resulting closed-loop systems are asymptotically stable. The gains of controllers are obtained by solving a set of e-dependent linear matrix inequalities (LMIs). In contrast to the existing results, the proposed methods have two advantages: (i) the designed controllers can overcome the external disturbances and parameter uncertainty; and (ii) the upper bound of e is improved, especially it is not required to be smaller than one. Examples are provided to illustrate the reduced conservatism of our results. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

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.

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.

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.

This paper provides a tutorial introduction and overview of design techniques for a system with both parametric uncertainty and unmodeled uncertainty, using mixed μ-synthesis. This paper also includes LFTs representation for modeling and an example of two-cart mass-spring-damper system (MSDs) is used to analyze its robust stability and performance, based on mixed μ-synthesis.

This paper proposes a modified ELM algorithm named P-ELM subject to how to select the number of hidden nodes and 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 of any 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 reduce the number of hidden nodes resulting the improvement in training speed. In order to verify the effectiveness of P-ELM algorithm, the comparative simulations are performed. The simulation results illustrate the better generalization performance and the stability of the proposed P-ELM algorithm.

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.