Control Engineering of China

Research on Reliability of Collision Sensor Based on Acoustic Wave Testing

JIA Wei-bin, YAN Chen, JI Xiao-yu, XU Wen-yuan

2019, 26(11): 1971-1977.

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The reliability of the collision sensor under acoustic wave excitation is tested and analyzed. The MEMS (Micro Electro Mechanical Systems) structures and their working principles are analyzed and explored. Through the self-built system, the acceleration value measured by the collision sensors is analyzed, and the simultaneous test frequency value is read, then they are combined for data analysis. On this basis, acoustic wave from multi-device, multi-frequency point, multi-direction and different modulation methods are used to evaluate the reliability of the collision sensor, which can avoid the situation that the air bag explode in the error cases.

Intermittent Fault Diagnosis and Fault Tolerant Control Based on Two-layer Kalman Filter

SHEN Shi-kun, QIU Ai-bing , QIU Wei-dong, CHEN Juan

2019, 26(11): 1978-1985.

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In this paper, a design framework of intermittent fault (IF) diagnosis and fault tolerant control (FTC) is developed for a class of linear discrete-time systems disturbed by noise. Firstly, a two-layer Kalman filter scheme is employed to realize IF isolation and estimation accurately. The first layer Kalman filter can detect and isolate the actuator intermittent fault by constructing the weighted square sum of the filter error as the system residual. The second optimal Kalman filter achieves a joint optimal estimation of system state, fault-free constraint state and intermittent fault by reconstructing the filter gain into a fault-free constrained gain and an intermittent fault gain, and solving the problem of the minimum variance unbiased estimation under the intermittent fault gain constraint. Furthermore, the optimal estimation, linear quadratic Gaussian (LQG) control and fault decoupling are used to construct the active fault-tolerant controller, so that the system can still guarantee the system performance in case of intermittent fault. Finally, the effectiveness of the proposed method is verified by the simulation of DC motor control system.

Fault Detection Method Based on Weighted k Nearest Neighbor in Multimode Process

FENG Li-wei, ZHANG Cheng, LI Yuan, XIE Yan-hong

2019, 26(11): 1986-1993.

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Industrial processes often operate in multiple production modes, for the characteristics of larger variables, the center drift and the larger modal variance of the multi-modal data, a weighed k-nearest-neighbor fault detection method(FD-wkNN) is proposed. First, find the k nearest neighbor in the training data set and calculate the distance between the training sample and the k nearest neighbor, and calculate the average distance of between the k nearest neighbor and the first K local nearest neighbors, take the reciprocal of the average distance as the distance weight, take the weighted distance as statistic D, D is able to eliminate the influence of center drift and difference of modal variances. Second determine the control line using D distribution. Finally compare the calculation D statistics of online sample and control line. On-line fault detection is realized. Using multi-mode example, as well as examples of penicillin data simulation experiments, compared with the PCA, kPCA, FD-kNN method to verify the effectiveness of this method.

Soft Sensor of Ball Mill Load Parameters Based on Multi-Mode Transfer Learning#br#

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HE Min, ZHI En-wei, CHENG Lan, YAN Gao-wei

2019, 26(11): 1994-1999.

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It is necessary to monitor multiple load parameters during the operation of wet ball mill. However, the change of working condition will lead to the fact that the real time data and the modeling data do not satisfy the assumption of independent and identically distributed. In order to solve the deterioration of model performance caused by the change of probability distribution of historical data and real time data under multiple conditions, and the correlation between the load parameters cannot be fully considered by the traditional soft sensing model. The transfer learning strategy and multi-task learning mechanism is introduced to establish a soft sensing model of wet ball mill load parameters based on multi condition transfer learning. Firstly, the joint distribution adaptation is used to fit the marginal and conditional distribution in the dimension reduction process. Then, the mill load parameters are predicted by multi-task least squares supports vector machines. Experimental result indicates that the performance of the proposed method is superior or at least comparable with existing benchmarking methods, can solve the problem of soft sensor under multiple loading condition.

Actuator Fault Detection for Descriptor System: An Interval Observer Approach

GUO Sheng-hui, ZHU Fang-lai, LI Ze

2019, 26(11): 2000-2005.

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For the problem of actuator fault detection on uncertain descriptor system, an interval observer approach is investigated. An interval observer design method for descriptor system without actuator fault is discussed, and the problem is transformed into a Sylvester form by means of two transforms. By using the output of the interval observer and the output of the system, an actuator fault detector is built. The correctness and effectiveness are illustrated via a physical example.

Design of Terminal Sliding Mode Antisway Controller of Overhead Cranes with Disturbance Compensation

CHEN Tian-yu, XU Wei-min, CHEN Xi, YUE Ya-wen

2019, 26(11): 2006-2012.

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In order to solve the internal parameter variation and external disturbance problem of 2-demensional underactuated overhead crane antisway control system, a terminal sliding mode controller with disturbance compensation is presented. In this method, the nonlinear disturbance observer is designed to estimate the uncertain dynamics of the system. And the terminal sliding surface is designed to make the tracking error converge to zero in finite time. At the same time, the chattering of the sliding mode controller is weakened. The stability analysis of the controller is given. The simulation results show the good performance of this control algorithm.

Research of Modeling with Small Sample for Complex Problem

FENG Guo-qi, CUI Dong-liang, ZHU Kai-quan, ZHANG Qi

2019, 26(11): 2013-2018.

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The problem of small sample size for machine learning is caused by the test cost of complex production, and experiment should be well designed to maximize the information under the size constraint of data set. This paper prompts a sample selection method for multiple linear regression (MLR): Hamming distance is used to evaluate the similarity of samples and depth-first strategy is employed to generate a data set with specified size by max-min Hamming distance, and the selected data set is used to evaluate the generalization performance. Finally a case of high pressure turbine disc design is used to verify this strategy, the result shows that the proposed strategy reduces experiment cost with necessary accuracy.

Research on Low Speed Control Strategy of Hybrid Excitation Synchronous Machines

GUO Dong, ZHANG Bo, WANG Wei, QI Xiao-ye

2019, 26(11): 2019-2024.

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In order to improve the load-bearing ability of the hybrid excitation synchronous machines at low speed and reduce the influence of load disturbance and variable speed motion on the control system, a hybrid control strategy based on load torque observer for excitation current given strategy and q-axis current feedforward compensation strategy is presented. And, a reduced-order observer is used to identify the load torque online. The identified load torque is used as one of the criteria for judging whether the hybrid excitation synchronous motor needs to increase the torque output. At the same time, the identified load torque and its rate are used as the excitation current reference inputs and q-axis current feedforward compensation, which can improve the load-bearing ability of the hybrid excitation synchronous motor. And, the control strategy can overcome the effect of time-varying load on control performance. Simulation results show that the proposed low speed control strategy can effectively improve the dynamic characteristics and enhance the robustness of the hybrid excitation synchronous machines speed control system.

The Vehicle Interior Sound Quality Prediction and Analysis Based on RBF Neural Network

ZHANG Yong, WANG Kun-xiang, OU Jian, LIU Ya

2019, 26(11): 2025-2030.

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Considering the sound quality of the special vehicle, a radial basis function (RBF) neural network method is proposed for calculating the weight and predicting subjective evaluation results concerning the influence of the objective evaluation parameters. First, three different types of special vehicles were tested on the road, and a subjective evaluation test was carried out to calculate the objective evaluation parameters of the sound quality by using the grade method. Then, the RBF neural network model was established for sound quality prediction inside the vehicle. The objective evaluation parameters are regarded as the input of RBF neural network model, and the subjective evaluation results as output, and the good consistency is obtained by comparing the simulation result with the subjective evaluation value of sound quality. Finally, the connection weight between the network layers was applied to calculate the influence weight of the objective evaluation parameters on the subjective irritability. The results show that the sound quality of the vehicle is mainly affected by the objective parameters of the overall loudness, loudness and speech interference level(SIL-4).

Nonlinear Integral Sliding Mode Control Method Based on New Reaching Law

LI Tao-chang

2019, 26(11): 2031-2035.

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A nonlinear integral sliding mode control method based on a new reaching law is proposed for a class of nonlinear systems with uncertainty. First, a kind of nonlinear integral sliding mode surface was designed and good performance during the sliding mode stage was ensured. Second, a new fast reaching law was constructed and the finite-time reachability property of the reaching law was verified. And then, a sliding mode controller was designed based on the proposed nonlinear integral sliding mode surface and the reaching law. The stability bound of the closed-loop sliding mode control system with bounded disturbances was derived. Finally, the effectiveness and superiority of the proposed method was verified by a series of simulation experiments.

Method of Dynamic Positioning State Estimation Based on MCMC Particle Filter

YANG Yi-fei, BAO Wei-er

2019, 26(11): 2036-2040.

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State estimation is an important part of the control part of the dynamic positioning system. In order to keep the ship in the target position, the accurate ship status information is needed. In view of the strong nonlinear problems such as extended Kalman filter and unscented Kalman filter, which are filtered by Gauss approximation and cannot adapt to ship motion, an improved particle filter algorithm based on Bayesian estimation is adopted. In order to reduce the degeneracy and impoverishment of standard particle filtering, MCMC(Markov Chain Monte Carlo) is introduced to construct Markov chains to produce samples from target distribution, and to reduce the correlation between particles. The simulation results show that the improved particle filter can separate the low-frequency motion information from the measurement information containing high-frequency motion information and noise. The filtering accuracy is higher and the stability is better.

Status and Prospect of Earth Pressure Balance Control Modeling for Shield’s

Sealed Cabin

XU Sheng, LIU Xuan-yu, HUANG Yue-yang

2019, 26(11): 2041-2046.

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In order to achieve the automation and intellectualization of shield tunneling process and to ensure the safety of construction, it is necessary to build an effective pressure control model of sealed cabin, which is one of the hottest and the most difficult problems in shield technology. Firstly, the research status of sealed cabin’s pressure control model of shield was described from two aspects of mechanism modeling and field data modeling, and the representative models were analyzed. Then, this paper summarized the problems that existed in model and needed to be solved urgently were summarized, such as modeling without considering the characteristics of time-lag, non-linear and time-varying of earth pressure balance system, which leaded to poor robustness and stability of model. Finally, the feasible methods of solving existing problems were proposed, and the prospect of future research on the control model of earth pressure in sealed cabin was given.

Soft Sensor for Intensive Aquaculture Process Based on GA-SVR

LI Kang, WANG Wei, Lin Shao-han

2019, 26(11): 2047-2051.

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As ammonia nitrogen concentration measurement in the intensive aquaculture process is difficult online, large investment and low precision, a soft sensor modeling method based on GA and SVR is proposed. Firstly, according to the analysis of aquiculture water quality influence factors, water temperature, dissolved oxygen, pH and conductivity are collected and used as auxiliary variables. Then, GA is used to optimize penalty parameter C and kernel function parameter g in SVR. Finally, the SVR model is used to predict the ammonia nitrogen concentration in water during aquaculture process. The predicted results were compared with BP, RBF neural network and SVR model based on grid search. The experimental results show that the soft-sensing method based on GA-SVR has better prediction accuracy, it can also provide effective operation guidance for the control and optimization of the intensive aquaculture process.

Fractional Sliding Mode Control of Mine Motor Based on Load Observation

HAN Tian-liang, MIU Zhong-cui, YU Xian-fei, ZHANG Wen-bin

2019, 26(11): 2052-2060.

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Mine electric locomotive is an important transport machine of the mine ground and underground tunnel, its working conditions are complex and has a poor operating environment. In order to improve the traction motor speed control performance and improve the system anti-disturbance and the robustness to the parameter variation, the speed controller of the control system is proposed, which uses fractional sliding mode to replace the traditional PI control. Aiming at load changes in the running process of the electric locomotive, the load torque observer is designed to observe the load torque in real-time, the observed value is integrated into the control strategy, which can improve the speed of the load mutation control. Simulation results show that the load observer can observe the load torque quickly and accurately. The sliding mode speed control with load observer has a strong anti-jamming effect on the load disturbance and effectively restrains the inherent chattering of the sliding mode control.

3 - DOF Helicopter Control Based on Variable Universe Fuzzy PID

DAI Bo, ZHOU Ze-yu, CHEN Ya-feng, LI Yan-fei

2019, 26(11): 2061-2066.

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Fuzzy control is an effective method to solve the stability of 3-degree of freedom helicopter system, but when the fuzzy rules are determined, there is a problem of local optimization only for a certain working area, and the fuzzy controller can't guarantee the high-precision tracking of the system trajectory. The variable domain fuzzy PID controller is designed. By adjusting the domain through the expansion factor, it adjusts the domain on the basis of the initial fuzzy rules, increases the application scope of the rules, obtains higher control accuracy, achieves the self-adaptive effect, and realizes the global optimization of the system. The experiment shows that the variable domain fuzzy control can better suppress the steady-state error, improve the response speed and realize the stable operation of the system.

A Method of Batch-to-batch Adaptive Optimization Based on T-PLS

SHEN Jian , CHU Fei , DAI Wei , JIA Run-da, MA Xiao-pin

2019, 26(11): 2067-2072.

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In this paper, an adaptive batch optimization method based on T-PLS is proposed to apply to the quality control and optimization of batch process. Due to the complexity of batch process, the model can’t perfectly match with the plant and result to NCO mismatch, in which the optimization result is just suboptimal. The gradient correction method is used to compensate the differences between the model and the plant. At the

same time, in order to expand the effective range of control variables, T-PLS statistics (,,) are

introduced as a hard constraint. Finally, the simulation is carried out to verify the effectiveness of the proposed method.

Route Planning Based on Programmed Cell Death Evolutionary Algorithm

ZHANG Xiao, LIU Zuo-jun, CHEN Ling-ling, YANG Peng

2019, 26(11): 2073-2077.

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The mechanism of programmed cell death is simulated in evolutionary algorithm. Three artificial control genes are introduced for the optimization of the traditional genetic algorithm. The advanced achievement of biology is absorbed in this new evolutionary algorithm. It can effectively overcome the premature problem of genetic algorithm. Besides, it can also obtain both the optimal solution and several sub optimal solutions. The the feasibility and effect of the algorithm is tested in the vehicle route planning.

Control Strategy of Torque Distribution for Optimizing the Comprehensive Performance of Distributed Drive Electric Vehicle

HUANG Kai-qi, LUO Liang-quan , CHENG Jian, LIU Xi-Ping

2019, 26(11): 2078-2087.

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For tackling the problem of the reduction in longitudinal driving torque and the undesired yaw moment when the distributed drive electric vehicle enters the working mode of acceleration slip regulation as well as the motor failure, a torque distribution strategy to reduce the tire utilization rate and improve the longitudinal drive performance is proposed in this paper. The objective function that includes the properties of tire utilization and longitudinal drive is established based on the generalized yaw moment decision, and it is solved by the global sequential quadratic programming (SQP) under the constraints of driving anti-skid, motor failure, maximum driving torque and pavement attachment. A joint control model of distributed drive electric vehicle is established by Carsim and Simulink. The simulation results show that the strategy can improve wheel attachment margin and the vehicle dynamic performance under the premise of ensuring the stability of the vehicle.

Design of LPV Fault Tolerant Controller for Wind Turbine Based on Combined Zero-process

WU Ding-hui, ZHENG Yang, HUANG Xu

2019, 26(11): 2086-2092.

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A fault tolerant method of combined double-layer convex polyhedron linear parameter varying (LPV) based on the zero processing is proposed in addressing the problem of pitch-angle sensors malfunction. The traditional LPV model is convert to the double-layer LPV model by optimizing its convex polyhedron vertices at first. Then, the combined thinking and zero processing are employed to design each sub-controller of the system, and the final controller is obtained from the combination of these sub-controllers. This approach solves the fault-tolerant control problem of pitch angles malfunction with reducing the linear deviation of LPV model in a concise way, which guarantees the stable operation of the wind turbine.

The Selection Method of Network Public Opinion Prediction Based on Fuzzy Information Aggregation

QIN Li-na

2019, 26(11): 2093-2098.

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For the network public opinion prediction model selection problems in which the attribute values are in the form of hesitant fuzzy information and the input arguments are associated with each other, a novel hesitant fuzzy Heronian geometric mean (HFHGM) operator is proposed on the basis of Archimedean norm and Heronian geometric mean, whose properties are studied in detail. Then, some special cases of the HFHGM operator are discussed and the hesitant fuzzy weighted Heronian geometric mean (HFWHGM) operator is introduced. In addition, a new hesitant fuzzy multi-attribute decision making method based on HFWHGM operator is developed, which can capture the interrelationships among the input arguments and enable decision maker to select different parameters to make decision in accordance with their own risk preferences attitude. Finally, a numerical example about the network public opinion prediction model selection is provided to illustrate the effectiveness of the developed method.

The Stability of Coupled Four Rotor Unmanned Aerial Vehicle based on ADRC Controller

SHI Yan-xia, QIAO Jia, XUE Long

2019, 26(11): 2099-2103.

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It is difficult to establish a system model for the internal uncertainty factors in the four rotor UAV system, and strong coupling and external environment interference increase the difficulty of vehicle control, a four rotor UAV control scheme based on active disturbance rejection control(ADRC) is proposed. By analyzing the mathematical model of the four rotor UAV system, using the ADRC strategy, the coupling between each loop, the internal uncertainties and external disturbances are regarded as the expansion state, the disturbance is suppressed by feedback compensation to make the system approximate to the series integrator type, and then which is compensated by nonlinear state feedback controller. Compared with the traditional PID simulation data, the ADRC has better control effect on the four rotor UAV system, and the control strategy has better robustness and dynamic performance.

A Circular Target Localization Method from Degraded Images

WU Jian, FU Lian-lian

2019, 26(11): 2104-2108.

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Circular target localization from a digital image is an important task in computer vision. In this paper, a new algorithm to locate circular objects from degraded gray scale images is proposed. A new type of difference named contour difference is used in this method to detect circular object. The contour difference is calculated directly on the image space, not on the edge map. This procedure needs neither any pre-processing, such as deblurring and denoising, nor edge detection on the input picture. By the new operator, the task of circular target localization becomes a peak searching problem in the space of contour difference data. The experiments show that the proposed method outperforms the circular Hough transform and the recent EDCircles methods on the accuracy in the degraded background, and it is robust to noise and blurs.

The Intrusion Detection Model Based on Hesitant Trapezoid Fuzzy Algorithm

JIANG Ming-fu

2019, 26(11): 2109-2114.

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For the intrusion detection model selection problem under the hesitant trapezoidal fuzzy information, an approach based on the hesitant trapezoid fuzzy decision making algorithm is proposed to select the intrusion detection model. First, considering the decision information is hesitant trapezoidal fuzzy number and the attributes are interrelated, based on the hesitant trapezoid fuzzy operational laws, the hesitant trapezoid fuzzy weighted average (HTrFEWA) operator and the hesitant trapezoid fuzzy weighted geometric (HTrFEWG) operator are presented. Then, for the fact that the ordered position of the hesitant trapezoid fuzzy set has different weights, the hesitant trapezoid fuzzy ordered weighted average (HTrFEOWA) operator and the hesitant trapezoid fuzzy ordered weighted geometric (HTrFEOWG) operator are proposed, which is followed by the discussion of their desirable properties. Finally, a new method for multi-attribute decision making is investigated based on the HTrFEWA operator and HTrFEWG operator, and applies the example for the selection of intrusion detection model to demonstrate the method’s practicality and effectiveness.

A Design Method for Deep Belief Network Based on Reinforcement Learning

XING Hai-xia, CHENG Le

2019, 26(11): 2115-2120.

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In recent years, deep learning-based deep belief network (DBN) has achieved successful applications in artificial intelligence and big data prediction analysis. However, too many hidden layers in DBN easily leads to a poor learning accuracy of supervised fine-tuning method (BP algorithm), even failure because of gradient diffusion, and robustness is poor. For this problem, an improved DBN based on reinforcement learning (RL-DBN) is proposed. First, adaptive contrastive divergence (ACD) algorithm is used to fast pre-train the hidden layers of DBN so that the better initial weight can be achieved, then the RL algorithm is used to replace BP algorithm to fine-tune DBN so that higher accuracy and better robustness can be achieved. The experimental results show that, compared with several existing similar models, the proposed RL-DBN achieves better performance in learning rate, accuracy and robustness.

Research on Dynamic Access Selection of RSU in VANET

ZHANG Ying, LI Pei-song, XIONG Wei

2019, 26(11): 2121-2129.

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Real-time communication problems of vehicle to vehicle and vehicle to infrastructure in VANET (Vehicular Ad hoc Network, VANET) have been taken more and more attention by researchers. The high speed mobility of vehicles and the unreliability of wireless communication greatly reduced the efficiency of data transmission. In order to solve this problem, the attention is focused on the dynamic access selection of RSU (Road Side Unit, RSU) in VANET and a new RSU selection algorithm is proposed to allocate vehicles access to related RSUs. The conventional access method is based on the signal strength of vehicles received from related RSU, but it does not take full account of the high speed mobility of vehicles which will lead to a vast variability of number of vehicles connected to RSU. In this paper, a method of real-time monitoring and forecasting is used to adjust certain vehicle connect on reasonable RSU to make the load of each RSU as much as balance, which will minimize the variability of the number of vehicles connected to each RSU, and ensure the RSU can be fully utilized. Simulation results show that the proposed algorithm can reduce the message collision, enhance the probability of collision free transmission, and improve the successful packet transmission rate effectively.

Node Deployment Algorithm Based on Linear Programming in UWSNs

XU Yi-han

2019, 26(11): 2130-2135.

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Aiming at the detection of solid pollutants in waters, a mixed integer linear program-based 3D underwater wireless sensor networks deployment (MILP-UWSNs) algorithm is proposed to minimize the number of deployed underwater sensors with a target field installation while the coverage ratio must be satisfied. First, divide the deployment area into multiple sub cubes(Sub-Cubes), then use the mixed integer linear programming to calculate the minimum size of Sub-Cubes and ensure that the convergence time for deploying all Sub-Cubes is within an acceptable range. Finally, the effectiveness of the MILP-UWSNs algorithm is analyzed, and the MILP-UWSNs algorithm is compared with common similar algorithms. Experimental data shows that the proposed MILP-UWSNs algorithm is scalable and has good performance in terms of deployment cost and monitoring quality.

Frequent Itemset Mining Using Prefix Tree in Big Data Environment

HUANG Cai-juan, LIU Zhuo-hua, SUO Hui, YANG Bin

2019, 26(11): 2136-2140.

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For the problems of low efficiency and scalability in frequent itemset mining, a new distributed FIM algorithm is proposed, and implements it on MapReduce framework. Firstly, the algorithm applies the idea of prefix sequence to construct a tree, by which all frequent itemsets can be found without exhaustive search over the transaction databases. Then, it produces frequent itemsets in a breadth-wide support-based approach. In each MapReduce iteration, the infrequent itemsets will be pruned away. It significantly deducts memory consumption and iteration time of each MapReduce job. Finally, the experimental comparison with different algorithms is performed under different scales of business and support degree. The results show the good efficiency and scalability of sequence-growth especially for dealing with big data and long itemsets.

Hierarchical Model Predictive Control Strategy Based on Model Switching Considering Economic Benefits

WANG Ni, AN Ai-min

2019, 26(11): 2141-2146.

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Hierarchical model predictive control(HMPC) strategy based on model switching is proposed to solve the problem of optimal working point deviation calculated by plant-wide optimization. In the plant-wide optimization layer, SQP algorithm is used to calculate the optimal point under maximize economic benefits. In the control layer, the whole workspace is divided into several subintervals, in the subintervals the corresponding sub models are set up with the minimum gain method, last MPC (model predictive control) controller is designed under sub model selected by output error index to track the optimal point. In a word, HMPC strategy based on mode switching can realize system optimization in the economic and control performance, and the comprehensive economic benefits of the system is improved. In this paper, simulations show that HMPC strategy based on model switching is effective in improving economic benefits. The comparison between the multi-model control strategy and the single model control strategy show that the system control performance is improved by HMPC strategy based on model switching.

Study on the Calculation Method of ω Input Signal in PSS4B Model

LI Song

2019, 26(11): 2147-2151.

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A study on the calculation method of speed input signal in PSS4B model is proposed. The method has less computation and is easy to realize in the excitation regulator. It provides a simple and effective way to implement PSS4B on the generator excitation regulator. Through the RTDS platform to build the model test, the effect of PSS4B on low frequency oscillation in three bands of high, medium and low frequency are compared. The results show that the PSS4B of speed input signal of the algorithm has obvious inhibition effect on the oscillation of each frequency band.

Adaptive Control of Microgrid Power Balance Based on Network State Estimation

ZHU Yi-xian, LIU Lu-deng, CHEN Cun-lin, ZHANG Wei, WANG Yuan-yue, LI Miao, YE Hai-feng

2019, 26(11): 2152-2158.

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In order to realize the effectiveness of adaptive control for microgrid power balance, an adaptive power balance control method based on network state estimation is proposed. The algorithm takes full account of traffic congestion / non congestion performance, and uses real-time network state estimation combined with gain scheduling technology to make the controller adapt to the change of communication network state. Then, the moving average threshold estimator is adopted, and the buffer length and threshold selection method are provided in the case of congestion/non congestion in two PLC communication networks. In addition, a consensus based multi agent control system design procedure is developed, which ensures that all evaluation states converge to reference state values, so as to ensure the fairness of assessment state participation. Finally, simulation results show the effectiveness of the proposed algorithm.

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