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Targeting the problem of autonomous navigation of indoor robots in large-scale, complicated, and unknown environments, an autonomous online decision-making algorithm based on deep reinforcement learning is put forward in this paper. Traditional path planning methods rely on the environment modeling, which can cause more workload of calculating. In this paper, the sensors to detect surrounding obstacles are combined with the DDPG (deep deterministic policy gradient) algorithm to input environmental perception and control the action direct output, which enables robots to complete the tasks of autonomous navigation and distribution without relying on environment modeling. In addition, the algorithm preprocesses the relevant data in the learning sample with Gaussian noise, facilitating the agent to adapt to noisy training environment and improve its robustness. The simulation results show that the optimized DL-DDPG algorithm is more efficient on online decision-making for the indoor robot navigation system, which enables the robot to complete autonomous navigation and intelligent control independently. [ABSTRACT FROM AUTHOR]

To improve the stability and accuracy of quintic polynomial trajectory tracking, an MPC (model predictive control) and fuzzy PID (proportional-integral-difference)- based control method are proposed. A lateral tracking controller is designed by using MPC with rule-based horizon parameters. The lateral tracking controller controls the steering angle to reduce the lateral tracking errors. A longitudinal tracking controller is designed by using a fuzzy PID. The longitudinal controller controls the motor torque and brake pressure referring to a throttle/brake calibration table to reduce the longitudinal tracking errors. By combining the two controllers, we achieve satisfactory trajectory tracking control. Relative vehicle trajectory tracking simulation is carried out under common scenarios of quintic polynomial trajectory in the Simulink/Carsim platform. The result shows that the strategy can avoid excessive trajectory tracking errors which ensures a better performance for trajectory tracking with high safety, stability, and adaptability. [ABSTRACT FROM AUTHOR]

At present, the construction of tunnel steel structure factories has been common, but most of them are still on the low end. In addition, the manual processing modes face certain problems, such as high waste rate, low efficiency, and discrete welding of steel components. To address these problems, this paper introduces an intelligent management cloud platform, aiming to realize unmanned control methods and construct an intelligent tunnel steel component processing plant. There are four main production lines in the intelligent steel structure factory. First, an intelligent production line of a tunnel grille arch effectively improves the production efficiency, production quality, and dust removal effect of the grille arch through the automatic welding technology of a reinforcement mechanism-based welding robot. Second, a processing line of a steel pipe for tunnel construction improves the overall processing efficiency, saves costs, and reduces work intensity by using an intelligent numerical control system, a pneumatic clamping device, a plasma cutting hole assembly, and a 360-degree automatic rotation system. Third, an automatic feeding platform of section steel arch production line adopts the six-axis robot sensor clamping connecting plate positioning and six-axis robot sensing three-plane automatic welding system, which not only compensates for the defects of manual production but also realizes the reduction, increases efficiency, and improves welding quality and precision. Fourth, an automatic steel mesh production line saves much labor and enhances site management and production efficiency through vertical and horizontal reinforcement pay-off racks. Overall, in this study, the construction goals of informatization, intellectualization, and personnel downsizing of the tunnel steel structure processing have been achieved. [ABSTRACT FROM AUTHOR]

The financial investment risk management system refers to an analysis and control of the intelligent system to invest in the lower financial situation so that investors quickly understand the situation in the financial industry. The purpose of this article is to use a digital model to evaluate financial investment risk management system. The investment risk value can be better evaluated by building a digital model. This paper first introduces financial investment risks and then elaborates the evaluation system and related digital models. The standards of the evaluation system are also given. The GARCH model is established to analyze the LME copper and LME aluminum case selected by this paper by investigation and analysis of the current status of corporate financial investment risks. The experimental results show that the evaluation results are often close to the reality when using the GARCH model evaluation of financial investment risk management system, and the accuracy is quite high. In addition to the EGARCH-N model, the established model is more accurate at 90% confidence level, which is more accurate and is relatively close to a given significance level. [ABSTRACT FROM AUTHOR]

In order to improve the ability of enterprises to cope with financial risks, this paper analyzes the financial early warning of enterprises combined with intelligent mathematical models and builds an intelligent financial early warning model to assist in analyzing the financial status of enterprises. Moreover, this paper combines the existing information technology and expenditure business scenarios to construct an intelligent early warning frame for enterprise financial control based on an intelligent mathematical model. In addition, this paper combines the K-means clustering algorithm to design a financial approval process integrity control early warning method to warn the integrity of the financial approval process. Finally, this paper presents the early warning of financial standard compliance control based on the C4.5 decision tree algorithm. The experimental research shows that the enterprise financial early warning model based on the intelligent mathematical model proposed in this paper can play an important role in the enterprise financial management and effectively improve the ability of the enterprise to cope with financial risks. [ABSTRACT FROM AUTHOR]

Intelligent control methods play an important role in the dynamic positioning system (DPS). To improve the control accuracy of dynamic positioning systems, an improved active disturbance rejection controller (IADRC) is designed in this study, which can optimize the steady-state performance of the system and improve the tracking accuracy of the system. For nonlinear active disturbance rejection controllers, their internal parameters are complex and numerous, with difficult settings. Proper parameters cannot be found with the trial and error method, and traditional optimization algorithms showcase some problems, such as slow convergence speed, leading to frequent failures in local optimal solutions. An optimized particle swarm optimization (IPSO) algorithm is applied to the IADRC parameter setting to boost the global search ability and the local development function. Simulation analyses demonstrate that, compared with other intelligent control methods, the IPSO-based IADRC dynamic positioning system has advantages such as fast response speed and strong anti-interference ability. [ABSTRACT FROM AUTHOR]

In this paper, a special design system is developed based on the design of the packaging bottle to achieve the effective acquisition of the image of the cross-section of the packaging bottle to be measured under the condition of limited space size, avoiding the distortion of the object to be measured. At the same time, the image of the area where the target packaging bottle is located is segmented, and the curve features are quickly determined and effectively matched with the template library to realize the recognition of the shape features of the bottle. In this paper, the design of the packaging bottle is first designed by mechanism design and 3D modeling, followed by rapid prototyping methods such as 3D printing, and the prototype is made for functional verification. Finally, the transmission speed and stability of the design system for packaging bottle recognition are improved through structural analysis and optimization methods. To realize the intelligent control of the packaging bottle transmission and identification system, the hardware control circuit is designed and the relevant intelligent control program is prepared based on the embedded system so that the packaging bottles in the transmission process can be quickly and accurately positioned and identified. An improved AdaBoost algorithm is proposed for packaging bottle detection. In the process of algorithm learning, the Haar features are too large and time-consuming, and the training sample is cropped to remove the sample edge pixels, which effectively reduces the number of features, thus reducing the computation. The proposed optical flow method is used to obtain the motion region in the video image as the region of interest, and the canny operator is used in the region of interest for edge detection, and the region of interest is filtered by the edge energy to exclude the noninterest region. Finally, the AdaBoost algorithm is used to detect the region of interest, which reduces the detection area and decreases the detection time. The improved AdaBoost algorithm has a high accuracy improvement over the traditional AdaBoost algorithm for the recognition of various packaging bottles with relatively suitable training set samples, and the system recognition time has reached the requirements of industrial recognition. [ABSTRACT FROM AUTHOR]

The paper is focused to design a robust and a simple fixed structure H ∞ control synthesis for an Electrohydraulic Servo System (EHSS) and comparing its performance with the conventional H ∞ controller and the classical proportional integral derivative (PID) controller. The precise position tracking of the piston load of EHSS system using classical PID controller is a difficult task due to some non-linear properties of EHSS and the dynamic behaviour of its parameters. The conventional H ∞ controllers also have constraints on gain ( H ∞ norm) for typical design requirement such as the speed of response, higher order, complex structure, control bandwidth, and robust stability. To overcome the above limitations, the proposed synthesis is formulated in the MATLAB. The proposed synthesis has fixed order and fixed structure and is also a linear robust controller. The control parameters of proposed synthesis are tuned using Genetic Algorithm optimization in MATLAB. Another advantage of this design is to use higher order complex shaping fillers to shape the close loop sensitivity S s and complementary sensitivity T s for the desired robust performance, without effecting the structure fixed of proposed synthesis. The order of the proposed controller is not affected by using the higher order complex weights transfer functions W s s and W T s on sensitivity and complementary sensitivity, respectively, whereas the structure of the conventional H ∞ controller becomes more complex due to higher order shaping weights. Model of the Electrohydraulic Servo System is validated experimentally, and results of proposed robust synthesis are compared with conventional H ∞ controller and the classical PID. [ABSTRACT FROM AUTHOR]

Aero-engine real-time models are widely used in control system design, integration, and testing. They can be used as the basis for model-based engine intelligent controls and health management, which is critical to improve engine safety, reliability, economy, and other performance indicators. This article provides an up-to-date review on aero-engine real-time modeling methods, model adaptation techniques, and applications for the last several decades. Besides, future research directions are also discussed, mainly focusing on the following four areas:(1) verification of the aero-engine real-time model over the full flight envelope; (2) better balance between real-time performance and accuracy in simplified methods for the aero-thermodynamic component level models; (3) further improvement in the real-time performance for the identified nonlinear models over the full flight envelope; (4) improvement of hybrid on-board adaptive real-time models combining the advantages of both model-based and data-based on-board adaptive real-time modeling methods. [ABSTRACT FROM AUTHOR]

With the continuous development of neural network theory itself and related theories and related technologies, neural network is one of the main branches of intelligent control technology. Artificial neural network is a nonlinear and adaptive information processing composed of a large number of processing units. In this paper, an adaptive fuzzy neural network (FNN) is used to construct an intelligent system architecture for English learning, and activation function is used to apply the knowledge of computer science and linguistics to English learning. The network neural structure diagram is presented. English machine learning model framework is established based on recursive neural network. On this basis, feature vector extraction and normalization algorithm are used to meet the needs of neural network model. After acquiring the feature vectors of users' learning styles, the clustering algorithm is used to effectively form a variety of learning styles. The validity of the English learning model was verified by designing the functional flow based on tests. Accurate mastery can activate the corresponding brain regions not only to improve the efficiency of learning, but also to better facilitate language learning. [ABSTRACT FROM AUTHOR]

This study considers the necessity of hiring heterogeneous labors. So far, many studies focus more on manufacturing of equipment and control systems in the intelligent production planning. In fact, the regular labors' processing time may be affected by the external factors and the disabled labors by the mentally handicapped. Therefore, this study sets the processing time of the two types of labors as fuzzy sets. The extra processing time from overtime generated by physical deterioration of old-aged labors is equal to the processing time of regular labors multiplied by the physical deterioration rate of old-aged labors on machine. The coefficient of the cost function is the stepwise function of cost structure. Besides, the dispatching rule based on floating time utilizes ant colony optimization to minimize the makespan. The data test results indicate that the proposed algorithm can efficiently dispatch and schedule the operations, with the average improvement ratio about 11.76%, and demonstrates high capability for the intelligent production planning [ABSTRACT FROM AUTHOR]

In this paper, a multimodel intelligent hierarchical control (MIHC) algorithm with dual systems is proposed to reduce the performance conflict between a path-tracking motion system and its subsystems during the motion control process of an intelligent vehicle (IV). The working principle of the MIHC algorithm is briefly introduced first, and the dynamic models of IV and the subsystems are constructed. Then, correlation controller models based on MIHC are established. Lastly, the influence of the subsystems on the trajectory tracking of IV is validated through simulations and hardware-in-the-loop test with various condition forms. Results show that the control performance of the automatic steering system has a great influence on the path-tracking accuracy compared with that of the antilock braking system. [ABSTRACT FROM AUTHOR]

The quality of power that is degrading day by day is an important issue for all the consumers. The important factor for this is harmonics in the voltage and current waveforms which can be resolved by the use of hybrid series active power filter. The combination consists of a series active power filter and a shunt passive filter connected in parallel to the load. The method used in this paper is for the purpose of achieving good harmonic compensation and reduced total harmonic distortion for various types of nonlinear loads as per the standards of IEEE 519. The proposed HSAPF technique uses the synchronous reference frame method for generating the compensating signal with an intelligent PI controller that uses particle swarm optimization (PSO) technique to obtain the required gain values needed to improve the steady state response of the system. The concept of vigorous HSAPF has been authenticated through MATLAB simulation analysis, and the results obtained validate the accuracy of the method for the different load conditions. [ABSTRACT FROM AUTHOR]

It is difficult to coordinate the various processes in the process industry. We built a multiagent distributed hierarchical intelligent control model for manufacturing systems integrating multiple production units based on multiagent system technology. The model organically combines multiple intelligent agent modules and physical entities to form an intelligent control system with certain functions. The model consists of system management agent, workshop control agent, and equipment agent. For the task assignment problem with this model, we combine reinforcement learning to improve the genetic algorithm for multiagent task scheduling and use the standard task scheduling dataset in OR-Library for simulation experiment analysis. Experimental results show that the algorithm is superior. [ABSTRACT FROM AUTHOR]

Real-time load detection method for belt conveyors based on computer vision is the research topic of this paper. A belt conveyor system equipped with cameras and a laser generator is used as the test apparatus. As the basis for conveyor intelligent speed regulation, two methods from different angles to perceive the load of conveyor belt were proposed, applied, and compared in this paper. Method 1 is based on the area proportion and method 2 is the detection based on laser-based computer vision technology. Laboratory experiments show that both methods can well detect the load on the conveyor belt. Method 2 is more economical and practical under the background of existing technology, also compared to the method 1, which provides a new idea and theoretical basis for the energy-saving control and intelligent development of the conveyor. [ABSTRACT FROM AUTHOR]

In this paper, we state a combining programming approach to optimize traffic signal control problem. The objective of the model is to minimize the total queue length with weight factors at the end of each phase. Then, modified Twin Gaussian Process (MTGP) is employed to predict the arrival rates for the traffic signal control problem. For achieving automatic control of the traffic signal, an intelligent control method of the traffic signal is proposed in view of the combining method, that is to say, the combining method of MTGP and LP, called MTGPLP, is embraced in the intelligent control system. Furthermore, some numerical experiments are proposed to test the validity of the model and the MTGPLP approach. In particular, the results of numerical experiments show that the model is effective with different arrival rates, departure rates, and weight factors and the combining method is successful. [ABSTRACT FROM AUTHOR]

An intelligent proportional-derivative sliding mode controller (i-PDSMC) is presented to overcome the unmodeled complexity of the robot manipulator under an actuator. i-PDSMC is a free model intelligent control based on the ultralocal, sliding mode, and PD control structure. A stability condition is determined by the Lyapunov theory. A comparative study between a classical PD, an intelligent PD control, and i-PDSMC is done through a robot manipulator under actuators. The simulation results prove that the proposed controller is more robust to trajectory tracking under parameter variations and external disturbances. [ABSTRACT FROM AUTHOR]

The relief valve is an important control and overload protection component of the emulsion pumping station. Its performance will affect the overall performance of the emulsion pumping station and the stable and intelligent control of the working surface. However, the research on high pressure and large flow relief valve for mine emulsion pumping station is still inadequate. In order to meet the requirements of emulsion pump station for large flow sensitivity, stability, reliability, and remote intelligent control of overflow valve, this paper uses the digital control method to establish the mathematical model of the relief valve and uses the software such as AMESim to its dynamic characteristics. The simulation results show that the structural parameters such as spool quality, damping hole, and spring stiffness have an effect on the working characteristics of the relief valve. It also provides reference for the intelligent control research of the large flow relief valve for the emulsion pumping station. [ABSTRACT FROM AUTHOR]

Aiming at improving the tracking stability performance for intelligent electric vehicles, a novel stability coordinated control strategy based on preview characteristics is proposed in this paper. Firstly, the traditional stability control target is introduced with the two degrees of freedom model, which is realized by the sliding mode control strategy. Secondly, an auxiliary control target further amending the former one with the innovation formulation of the preview characteristics is established. At last, a multiple purpose Vague set leverages the contribution of the traditional target and the auxiliary preview target in various vehicle states. The proposed coordinated control strategy is analyzed on the MATLAB/CarSim simulation platform and verified on an intelligent electric vehicle established with A&D5435 rapid prototyping experiment platform. Simulation and experimental results indicate that the proposed control strategy based on preview characteristics can effectively improve the tracking stability performance of intelligent electric vehicles. In the double lane change simulation, the peak value of sideslip angle, yaw rate, and lateral acceleration of the vehicle is reduced by 13.2%, 11.4%, and 8.9% compared with traditional control strategy. The average deviations between the experimental and simulation results of yaw rate, lateral acceleration, and steering wheel angle are less than 10% at different speeds, which demonstrates the consistency between the experimental and the simulation results. [ABSTRACT FROM AUTHOR]

As computer vision develops, pan-tilt platform visual systems are able to track moving target over static camera systems. In this paper, a novel motion-intelligence-based control algorithm for object tracking by controlling pan-tilt platform has been proposed. The algorithm includes the motion control model based on angular speed and the intelligent control algorithm based on reinforcement learning (RL). The motion control model converts deviation between the center point of the tracked target and the center point of the frame to angular speed of pan-tilt platform. It can keep position of the tracked object in the center of the frame automatically. The intelligent control algorithm based on reinforcement learning can reduce the error between the ideal value and the actual value when the pan-tilt platform moves. The two blocks work together to make the pan-tilt platform track a dynamic object more stably and the experiment result shows that both the tracking accuracy and robustness are improved. [ABSTRACT FROM AUTHOR]

According to the parallel mechanism theory, this paper proposes a novel intelligent robotic spine brace for the treatment of scoliosis. Nevertheless, this type of parallel mechanism has the following disadvantages: strong dynamic coupling in task space or joint space, adverse effect of system's gravity, and lower response frequency in roll and pitch orientations, which seriously affect the performance of the system. In order to solve those boring problems, this paper presents a novel active force control structure, modal space dynamic feed-forward (MSDF) force control strategy. Besides, this paper expresses the intelligent robotic brace system model including the dynamic and kinematic models and the electric actuator model with Kane strategy. The stability of the intelligent system with the novel control strategy is proved. In order to evaluate the performance of the presented MSDF force control method, this paper builds the parallel mechanism experimental platform. It can be seen from experimental results that the proposed motion control method solves these boring problems well. [ABSTRACT FROM AUTHOR]

Nonlinear time-varying systems without mechanism models are common in application. They cannot be controlled directly by the traditional control methods based on precise mathematical models. Intelligent control is unsuitable for real-time control due to its computation complexity. For that sake, a multidimensional Taylor network (MTN) based output tracking control scheme, which consists of two MTNs, one as an identifier and the other as a controller, is proposed for SISO nonlinear time-varying discrete-time systems with no mechanism models. A MTN identifier is constructed to build the offline model of the system, and a set of initial parameters for online learning of the identifier is obtained. Then, an ideal output signal is selected relative to the given reference signal. Based on the system identification model, Pontryagin minimum principle is introduced to obtain the numerical solution of the optimal control law for the system relative to the given ideal output signal, with the corresponding optimal output taken as the desired output signal. A MTN controller is generated automatically to fit the numerical solution of the optimal control law using the conjugate gradient (CG) method, and a set of initial parameters for online learning of the controller is obtained. An adaptive back propagation (BP) algorithm is developed to adjust the parameters of the identifier and controller in real time, and the convergence for the proposed learning algorithm is verified. Simulation results show that the proposed scheme is valid. [ABSTRACT FROM AUTHOR]

To achieve the wind sail-assisted function of the unmanned surface vehicle (USV), this work focuses on the design problems of the sail-assisted USV intelligent control systems (SUICS) and illustrates the implementation process of the SUICS. The SUICS consists of the communication system, the sensor system, the PC platform, and the lower machine platform. To make full use of the wind energy, in the SUICS, we propose the sail angle of attack automatic adjustment (Sail_4A) algorithm and present the realization flow for each subsystem of the SUICS. By using the test boat, the design and implementation of the SUICS are fulfilled systematically. Experiments verify the performance and effectiveness of our SUICS. The SUICS enhances the intelligent utility of sustainable wind energy for the sail-assisted USV significantly and plays a vital role in shipping energy-saving emission reduction requirements issued by International Maritime Organization (IMO). [ABSTRACT FROM AUTHOR]

In order to improve the active safety driving vehicle and alleviate the intension of driving fatigue, an intelligent control strategy of automobile cruise is put forward based on the throttle or braking pedal combined control adopting the fuzzy control theory. A fuzzy logic controller is presented, which consists of the two input variables, the deviation of the theoretical safe distance and relative distance and the relative velocity between the preceding vehicle and the cruise vehicle, and the single output variable, that is, the throttle opening or the braking pedal travel. Taking the test data of 1.6 L vehicle with auto-transmission as an example, the function on the intelligent cruise control system is simulated adopting MATLAB/Simulink aiming at different working conditions on the city road. The simulation results show that the control strategy possesses integrated capability of automated Stop & Go control, actively following the preceding vehicle on the conditions of keeping the safety distance and the constant velocity cruise. The research results can offer the theory and technology reference for setting dSPACE type and developing the integrated control product of automobile cruise system. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present a novel controller of differential assist steering system. Firstly, the integrated controller module in electric vehicle is investigated, in which the front axle is refitted for two in-wheel motors. Secondly, genetic algorithm is used to optimize the control decision-making rules of fuzzy controller. Thereafter, the built model is embedded in veDYNA development environment with an offline simulation test of driver/vehicle/road closed-loop system. At last, control strategy, which has been compiled and downloaded to Mirco AutoBox controller prototype, is used for the real vehicle experimental analysis and validation of the proposed controller. Both simulation and experimental results demonstrate that the controller can improve the yaw motion and reduce the slip angle of vehicle. Here, the vehicle is equipped with the in-wheel motors on both sides. At the same time, the controller cannot only track the steering torque indication of driving current, but also preferably maintain the steering assist handle. Furthermore, it can bring about a good road feeling to the driver because of the reduced torque fluctuation. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) has been already widely used in many fields in terms of industry, agriculture, and military, and so forth. The basic composition is WSN nodes that are capable of performing processing, gathering information, and communicating with other connected nodes in the network. The main components of a WSN node are microcontroller, transceiver, and some sensors. Undoubtedly, it also can be added with some actuators to form a tiny mechanical system. Under this case, the existence of task preemption while executing operating system will not only cost more energy for WSN nodes themselves, but also bring unacceptable system states caused by vibrations. However for these nodes, task I/O delays are inevitable due to the existence of task preemption, which will bring extra overhead for the whole system, and even bring unacceptable system states caused by vibrations. This paper mainly considers the earliest deadline first (EDF) task preemption algorithm executed in WSN OS and proposes an improved task preemption algorithm so as to lower the preemption overhead and I/O delay and then improve the system performance. The experimental results show that the improved task preemption algorithm can reduce the I/O delay effectively, so the real-time processing ability of the system is enhanced. [ABSTRACT FROM AUTHOR]

The grinding process is a typical complex nonlinear multivariable process with strongly coupling and large time delays. Based on the data-driven modeling theory, the integrated modeling and intelligent control method of grinding process is carried out in the paper, which includes the soft-sensormodel of economic and technique indexes, the optimized set-pointmodel utilizing case-based reasoning, and the self-tuning PID decoupling controller. For forecasting the key technology indicators (grinding granularity and mill discharge rate of grinding process), an adaptive soft-sensor modeling method based on wavelet neural network optimized by the improved shuffled frog leaping algorithm (ISFLA) is proposed. Then, a set point optimization control strategy of grinding process based on case-based reasoning (CBR) method is adopted to obtain the optimized velocity set-point of ore feed and pump water feed in the grinding process controlled loops. Finally, a self-tuning PID decoupling controller optimized is used to control the grinding process. Simulation results and industrial application experiments clearly show the feasibility and effectiveness of control methods and satisfy the real-time control requirements of the grinding process. [ABSTRACT FROM AUTHOR]

Complex engineering system optimization usually involves multiple projects or tasks. On the one hand, dependency modeling among projects or tasks highlights structures in systems and their environments which can help to understand the implications of connectivity on different aspects of system performance and also assist in designing, optimizing, and maintaining complex systems. On the other hand, multiple projects or tasks are either happening at the same time or scheduled into a sequence in order to use common resources. In this paper, we propose a dynamic intelligent decision approach to dependency modeling of project tasks in complex engineering system optimization. The approach takes this decision process as a two-stage decisionmaking problem. In the first stage, a task clustering approach based on modularization is proposed so as to find out a suitable decomposition scheme for a large-scale project. In the second stage, according to the decomposition result, a discrete artificial bee colony (ABC) algorithm inspired by the intelligent foraging behavior of honeybees is developed for the resource constrained multiproject scheduling problem. Finally, a certain case from an engineering design of a chemical processing system is utilized to help to understand the proposed approach. [ABSTRACT FROM AUTHOR]

Freeway traffic state estimation is useful for intelligent traffic guidance, control, and management. The freeway traffic state is featured with rapid and dramatic fluctuations, which presents a strong nonlinear feature. In theory, a particle filter has good performance in solving nonlinear problems. This paper proposes a particle filter based approach to estimate freeway traffic state. The freeway link between the west of Peace Bridge and the west of San Yuan Bridge of the third ring in Beijing is used as the experimental object. According to the traffic characteristics and measurement mode of the link, the second-order validated macroscopic traffic flow model is adopted to set up the link model. The implementation steps of the particle filter for freeway traffic state estimation are described in detail. The estimation error analysis for the experiments proves that the proposed approach has an encouraging estimation performance. [ABSTRACT FROM AUTHOR]

A novel compensator topology is proposed for three-phase unbalanced loads in the distribution networks. The negative and zero sequence compensation networks are proposed using delta-connected and star-connected capacitor banks. The compensation networks are paralleled with the unbalanced load to achieve a perfect balancing of active power and a complete compensation of reactive power of the unbalanced load. Intelligent grouping compound switches are implemented to control the capacitors, where, therefore, each capacitor can be switched between phases or phase and ground. Wireless sensors are also applied to acquire the load quantities. The compensation method proposed is based on symmetrical component theory. In addition, the load compensation based on instantaneous symmetrical component theory with positive sequence online extraction is proposed under unbalanced and distorted source voltages. The proposed load-balancing method and device are applied to Liaoning, Neimenggu, and Jilin provinces. The application results are provided to validate the performance of the proposed compensator topology and compensation method. [ABSTRACT FROM AUTHOR]

Due to the short duration of impulsive impact of an aircraft during touchdown, a traditional landing gear can only achieve limited performance. In this study, a magnetorheological (MR) absorber is incorporated into a landing gear system; an intelligent control algorithm, a human simulated intelligent control (HSIC), is proposed to adaptively tune the MR absorber. First, a two degreeof- freedom (DOF) dynamic model of a landing gear system featuring an MR absorber is constructed. The control model of an MR damper is also developed. After analyzing the impact characteristic during touchdown, an HSIC is then formulated. A genetic algorithm is adopted to optimize the control parameters of HSIC. Finally, a numerical simulation is performed to validate the proposed damper and the controller considering the varieties of sink velocities and sprung masses. The simulations under different scenarios show that the landing gear system based on the MR absorber can greatly reduce the peak impact load of sprung mass within the stroke. The biggest improvement of the proposed controller is over 40% compared to that of skyhook controller. Furthermore, HSIC exhibits better adaptive ability and strong robustness than skyhook controller under various payloads and sink velocities. [ABSTRACT FROM AUTHOR]

This paper presents a novel multiloop and Multi-objective cooperative intelligent control system MMCICS used to improve the performance of position, velocity and acceleration integrated control on a complex multichannel plant. Based on regulation mechanism of the neuroendocrine system NES, a bioinspired motion control approach has been used in the MMCICS which includes four cooperative units. The planning unit outputs the desired signals. The selection unit chooses the real-time dominant control mode. The coordination unit uses the velocity Jacobian matrix to regulate the cooperative control signals. The execution unit achieves the ultimate task based on sub-channel controllers with the proposed hormone regulation self-adaptive Modules HRSMs. Parameter tuning is given to facilitate the MMCICS implementation. The MMCICS is applied to an actual 2-DOF redundant parallel manipulator where the feasibility of the new control system is demonstrated. The MMCICS keeps its subchannels interacting harmoniously and systematically. Therefore, the plant has fast response, smooth velocity, accurate position, strong self-adaptability, and high stability. The HRSM improves the control performance of the local controllers and the global system as well, especially for manipulators running at high velocities and accelerations. [ABSTRACT FROM AUTHOR]