A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search

Vol. 6,  No. 2, 2019

column
  Previous Issue | Next Issue 
Display Method:
REVIEWS
Advances in Control Technologies for Wastewater Treatment Processes:Status, Challenges, and Perspectives
Abdelhamid Iratni, Ni-Bin Chang
2019, 6(2): 337-363. doi: 10.1109/JAS.2019.1911372
Abstract(5342) HTML (1644) PDF(521)
Abstract:
This paper presents a thorough review of control technologies that have been applied to wastewater treatment processes in the environmental engineering regime in the past four decades. It aims to provide a comprehensive technological review for both water engineering professionals and control specialists, giving rise to a suite of up-to-date pathways to impact this field in light of the classified technology hubs. The assessment was conducted with respect to linear control, linearizing control, nonlinear control, and artificial intelligence-based control. The application domain of each technology hub was summarized into a set of comparative tables for a holistic assessment. Challenges and perspectives were offered to these field engineers to help orient their future endeavor.
Adaptive and Predictive Control Strategies for Wind Turbine Systems: A Survey
Magdi S. Mahmoud, Mojeed O. Oyedeji
2019, 6(2): 364-378. doi: 10.1109/JAS.2019.1911375
Abstract(2640) HTML (1269) PDF(266)
Abstract:
The wind turbine (WT) is a renewable energy conversion device for transformation of kinetic energy from the wind to mechanical energy for subsequent use in different forms. This paper focuses on wind turbine control design strategies. The content is divided into the following parts: 1) An overview of the recent advances that have been made in the application of adaptive and model predictive control strategies for wind turbines. 2) Summarizes some important aspects of modeling of wind turbines for control studies. 3) Provides an outlook on the application of adaptive model predictive control for uncertain systems to stimulate new research interests for wind turbine systems. We provide an overall picture of the research results with evaluation of the merits/demerits.
PAPERS
Global Optimum-Based Search Differential Evolution
Yang Yu, Shangce Gao, Yirui Wang, Yuki Todo
2019, 6(2): 379-394. doi: 10.1109/JAS.2019.1911378
Abstract(1808) HTML (1056) PDF(126)
Abstract:
In this paper, a global optimum-based search strategy is proposed to alleviate the situation that the differential evolution (DE) usually sticks into a stagnation, especially on complex problems. It aims to reconstruct the balance between exploration and exploitation, and improve the search efficiency and solution quality of DE. The proposed method is activated by recording the number of recently consecutive unsuccessful global optimum updates. It takes the feedback from the global optimum, which makes the search strategy not only refine the current solution quality, but also have a change to find other promising space with better individuals. This search strategy is incorporated with various DE mutation strategies and DE variations. The experimental results indicate that the proposed method has remarkable performance in enhancing search efficiency and improving solution quality.
Inverse Optimal Control of Evolution Systems and Its Application to Extensible and Shearable Slender Beams
K. D. Do, A. D. Lucey
2019, 6(2): 395-409. doi: 10.1109/JAS.2019.1911381
Abstract(1749) HTML (990) PDF(68)
Abstract:
An optimal (practical) stabilization problem is formulated in an inverse approach and solved for nonlinear evolution systems in Hilbert spaces. The optimal control design ensures global well-posedness and global practical $\mathcal{K}_{\infty}$-exponential stability of the closed-loop system, minimizes a cost functional, which appropriately penalizes both state and control in the sense that it is positive definite (and radially unbounded) in the state and control, without having to solve a Hamilton-Jacobi-Belman equation (HJBE). The Lyapunov functional used in the control design explicitly solves a family of HJBEs. The results are applied to design inverse optimal boundary stabilization control laws for extensible and shearable slender beams governed by fully nonlinear partial differential equations.
Shared Control of Highly Automated Vehicles Using Steer-By-Wire Systems
Chao Huang, Fazel Naghdy, Haiping Du, Hailong Huang
2019, 6(2): 410-423. doi: 10.1109/JAS.2019.1911384
Abstract(1694) HTML (1040) PDF(91)
Abstract:
A shared control of highly automated Steer-by-Wire system is proposed for cooperative driving between the driver and vehicle in the face of driver's abnormal driving. A fault detection scheme is designed to detect the abnormal driving behaviour and transfer the control of the car to the automatic system designed based on a fault tolerant model predictive control (MPC) controller driving the vehicle along an optimal safe path. The proposed concept and control algorithm are tested in a number of scenarios representing intersection, lane change and different types of driver's abnormal behaviour. The simulation results show the feasibility and effectiveness of the proposed method.
Adaptive Decentralized Output-Constrained Control of Single-Bus DC Microgrids
Jiangkai Peng, Bo Fan, Jiajun Duan, Qinmin Yang, Wenxin Liu
2019, 6(2): 424-432. doi: 10.1109/JAS.2019.1911387
Abstract(1608) HTML (1034) PDF(84)
Abstract:
A single-bus DC microgrid can represent a wide range of applications. Control objectives of such systems include high-performance bus voltage regulation and proper load sharing among multiple distributed generators (DGs) under various operating conditions. This paper presents a novel decentralized control algorithm that can guarantee both the transient voltage control performance and realize the predefined load sharing percentages. First, the output-constrained control problem is transformed into an equivalent unconstrained one. Second, a two-step backstepping control algorithm is designed based on the transformed model for bus-voltage regulation. Since the overall control effort can be split proportionally and calculated with locally-measurable signals, decentralized load sharing can be realized. The control design requires neither accurate parameters of the output filters nor load measurement. The stability of the transformed systems under the proposed control algorithm can indirectly guarantee the transient bus voltage performance of the original system. Additionally, the high-performance control design is robust, flexible, and reliable. Switch-level simulations under both normal and fault operating conditions demonstrate the effectiveness of the proposed algorithm.
Reinforcement Learning for Linear Continuous-time Systems: an Incremental Learning Approach
Tao Bian, Zhong-Ping Jiang
2019, 6(2): 433-440. doi: 10.1109/JAS.2019.1911390
Abstract(2820) HTML (1079) PDF(183)
Abstract:
In this paper, we introduce a novel reinforcement learning (RL) scheme for linear continuous-time dynamical systems. Different from traditional batch learning algorithms, an incremental learning approach is developed, which provides a more efficient way to tackle the on-line learning problem in real-world applications. We provide concrete convergence and robust analysis on this incremental-learning algorithm. An extension to solving robust optimal control problems is also given. Two simulation examples are also given to illustrate the effectiveness of our theoretical result.
Unsupervised Electric Motor Fault Detection by Using Deep Autoencoders
Emanuele Principi, Damiano Rossetti, Stefano Squartini, Francesco Piazza
2019, 6(2): 441-451. doi: 10.1109/JAS.2019.1911393
Abstract(2219) HTML (1087) PDF(182)
Abstract:
Fault diagnosis of electric motors is a fundamental task for production line testing, and it is usually performed by experienced human operators. In the recent years, several methods have been proposed in the literature for detecting faults automatically. Deep neural networks have been successfully employed for this task, but, up to the authors' knowledge, they have never been used in an unsupervised scenario. This paper proposes an unsupervised method for diagnosing faults of electric motors by using a novelty detection approach based on deep autoencoders. In the proposed method, vibration signals are acquired by using accelerometers and processed to extract Log-Mel coefficients as features. Autoencoders are trained by using normal data only, i.e., data that do not contain faults. Three different autoencoders architectures have been evaluated: the multi-layer perceptron (MLP) autoencoder, the convolutional neural network autoencoder, and the recurrent autoencoder composed of long short-term memory (LSTM) units. The experiments have been conducted by using a dataset created by the authors, and the proposed approaches have been compared to the one-class support vector machine (OC-SVM) algorithm. The performance has been evaluated in terms area under curve (AUC) of the receiver operating characteristic curve, and the results showed that all the autoencoder-based approaches outperform the OC-SVM algorithm. Moreover, the MLP autoencoder is the most performing architecture, achieving an AUC equal to 99.11%
An Intelligent Optimization Approach to Non-stationary Interference Suppression for Wireless Networks
Lichuan Liu
2019, 6(2): 452-459. doi: 10.1109/JAS.2019.1911396
Abstract(1434) HTML (835) PDF(45)
Abstract:
In this paper, we propose a new technique to effectively suppress non-stationary interference signal for wireless networks. This technique combines a new scheme of interference signal estimation with the intelligent optimization projection. In order to capture interference signal's subspace, a time-varying method is used to estimate the non-stationary interference. Orthogonal polynomials are used for the basis function instead of the power of the time to reduce the computational complexity. The interference is then removed from the corrupted signal by subspace projection, resulting in less distortion to the desired signal. The performance of this approach is validated by computer simulation.
Optimal Matching Control of a Low Energy Charged Particle Beam in Particle Accelerators
Zhigang Ren, Tehuan Chen, Zongze Wu
2019, 6(2): 460-470. doi: 10.1109/JAS.2018.7511270
Abstract(1591) HTML (781) PDF(43)
Abstract:
Particle accelerators are devices used for research in scientific problems such as high energy and nuclear physics. In a particle accelerator, the shape of particle beam envelope is changed dynamically along the forward direction. Thus, this reference direction can be considered as an auxiliary "time" beam axis. In this paper, the optimal beam matching control problem for a low energy transport system in a charged particle accelerator is considered. The beam matching procedure is formulated as a finite "time" dynamic optimization problem, in which the Kapchinsky-Vladimirsky (K-V) coupled envelope equations model beam dynamics. The aim is to drive any arbitrary initial beam state to a prescribed target state, as well as to track reference trajectory as closely as possible, through the control of the lens focusing strengths in the beam matching channel. We first apply the control parameterization method to optimize lens focusing strengths, and then combine this with the time-scaling transformation technique to further optimize the drift and lens length in the beam matching channel. The exact gradients of the cost function with respect to the decision parameters are computed explicitly through the state sensitivity-based analysis method. Finally, numerical simulations are illustrated to verify the effectiveness of the proposed approach.
Minimum Dwell Time for Global Exponential Stability of a Class of Switched Positive Nonlinear Systems
Ruicheng Ma, Shuang An
2019, 6(2): 471-477. doi: 10.1109/JAS.2018.7511264
Abstract(1385) HTML (841) PDF(52)
Abstract:
This paper will investigate global exponential stability analysis for a class of switched positive nonlinear systems under minimum dwell time switching, whose nonlinear functions for each subsystem are constrained in a sector field by two odd symmetric piecewise linear functions and whose system matrices for each subsystem are Metzler. A class of multiple time-varying Lyapunov functions is constructed to obtain the computable sufficient conditions on the stability of such switched nonlinear systems within the framework of minimum dwell time switching. All present conditions can be solved by linear/nonlinear programming techniques. An example is provided to demonstrate the effectiveness of the proposed result.
Receding Horizon Estimation for Linear Discrete-time Systems with Multi-channel Observation Delays
Chunyan Han, Chaochao Li, Fang He, Yue Liu
2019, 6(2): 478-484. doi: 10.1109/JAS.2018.7511261
Abstract(1401) HTML (862) PDF(95)
Abstract:
This paper investigates the receding horizon state estimation for the linear discrete-time system with multi-channel observation delays. The receding horizon estimation is designed by the reorganized observation technique and the linear unbiased estimation method. The estimation gains are developed by solving a set of Riccati equations, and a stability result about the state estimation is shown. Finally, an example is given to illustrate the efficiency of the receding horizon state estimation.
Stable Model Order Reduction Method for Fractional-Order Systems Based on Unsymmetric Lanczos Algorithm
Zhe Gao
2019, 6(2): 485-492. doi: 10.1109/JAS.2019.1911399
Abstract(1391) HTML (798) PDF(59)
Abstract:
This study explores a stable model order reduction method for fractional-order systems. Using the unsymmetric Lanczos algorithm, the reduced order system with a certain number of matched moments is generated. To obtain a stable reduced order system, the stable model order reduction procedure is discussed. By the revised operation on the tridiagonal matrix produced by the unsymmetric Lanczos algorithm, we propose a reduced order modeling method for a fractional-order system to achieve a satisfactory fitting effect with the original system by the matched moments in the frequency domain. Besides, the bound function of the order reduction error is offered. Two numerical examples are presented to illustrate the effectiveness of the proposed method.
Adaptive Robust Control for a Lower Limbs Rehabilitation Robot Running Under Passive Training Mode
Xiaolong Chen, Han Zhao, Shengchao Zhen, Hao Sun
2019, 6(2): 493-502. doi: 10.1109/JAS.2019.1911402
Abstract(1465) HTML (886) PDF(98)
Abstract:
This paper focuses on the problem of the adaptive robust control of a lower limbs rehabilitation robot (LLRR) that is a nonlinear system running under passive training mode. In reality, uncertainties including modeling error, initial condition deviation, friction force and other unknown external disturbances always exist in a LLRR system. So, it is necessary to consider the uncertainties in the unilateral man-machine dynamical model of the LLRR we described. In the dynamical model, uncertainties are (possibly fast) time-varying and bounded. However, the bounds are unknown. Based on the dynamical model, we design an adaptive robust control with an adaptive law that is leakage type based and on the framework of Udwadia-Kalaba theory to compensate for the uncertainties and to realize tracking control of the LLRR. Furthermore, the effectiveness of designed control is shown with numerical simulations.
Design of a Proportional-Integral-Derivative Controller for an Automatic Generation Control of Multi-area Power Thermal Systems Using Firefly Algorithm
K. Jagatheesan, B. Anand, Sourav Samanta, Nilanjan Dey, Amira S. Ashour, Valentina E. Balas
2019, 6(2): 503-515. doi: 10.1109/JAS.2017.7510436
Abstract(1770) HTML (828) PDF(97)
Abstract:
Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is mea sured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control, automatic generation control (AGC) plays a crucial role. In this paper, multi-area (Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative (PID) controller as a supplemen tary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm (FFA). The experimental results demonstrated the comparison of the proposed system performance (FFA-PID) with optimized PID controller based genetic algorithm (GA PID) and particle swarm optimization (PSO) technique (PSO PID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error (ITAE) cost function with one percent step load perturbation (1% SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.
A Memetic Algorithm With Competition for the Capacitated Green Vehicle Routing Problem
Ling Wang, Jiawen Lu
2019, 6(2): 516-526. doi: 10.1109/JAS.2019.1911405
Abstract(1481) HTML (827) PDF(75)
Abstract:
In this paper, a memetic algorithm with competition (MAC) is proposed to solve the capacitated green vehicle routing problem (CGVRP). Firstly, the permutation array called traveling salesman problem (TSP) route is used to encode the solution, and an effective decoding method to construct the CGVRP route is presented accordingly. Secondly, the k-nearest neighbor (kNN) based initialization is presented to take use of the location information of the customers. Thirdly, according to the characteristics of the CGVRP, the search operators in the variable neighborhood search (VNS) framework and the simulated annealing (SA) strategy are executed on the TSP route for all solutions. Moreover, the customer adjustment operator and the alternative fuel station (AFS) adjustment operator on the CGVRP route are executed for the elite solutions after competition. In addition, the crossover operator is employed to share information among different solutions. The effect of parameter setting is investigated using the Taguchi method of design-of-experiment to suggest suitable values. Via numerical tests, it demonstrates the effectiveness of both the competitive search and the decoding method. Moreover, extensive comparative results show that the proposed algorithm is more effective and efficient than the existing methods in solving the CGVRP.
Pythagorean Uncertain Linguistic Variable Hamy Mean Operator and Its Application to Multi-attribute Group Decision Making
Huidong Wang, Shifan He, Chengdong Li, Xiaohong Pan
2019, 6(2): 527-539. doi: 10.1109/JAS.2019.1911408
Abstract(2009) HTML (802) PDF(52)
Abstract:
Pythagorean fuzzy set (PFS) can provide more flexibility than intuitionistic fuzzy set (IFS) for handling uncertain information, and PFS has been increasingly used in multi-attribute decision making problems. This paper proposes a new multi-attribute group decision making method based on Pythagorean uncertain linguistic variable Hamy mean (PULVHM) operator and VIKOR method. Firstly, we define operation rules and a new aggregation operator of Pythagorean uncertain linguistic variable (PULV) and explore some properties of the operator. Secondly, taking the decision makers' hesitation degree into account, a new score function is defined, and we further develop a new group decision making approach integrated with VIKOR method. Finally, an investment example is demonstrated to elaborate the validity of the proposed method. Sensibility analysis and comprehensive comparisons with another two methods are performed to show the stability and advantage of our method.
Hierarchical Visual Attention Model for Saliency Detection Inspired by Avian Visual Pathways
Xiaohua Wang, Haibin Duan
2019, 6(2): 540-552. doi: 10.1109/JAS.2017.7510664
Abstract(1517) HTML (822) PDF(50)
Abstract:
Visual attention is a mechanism that enables the visual system to detect potentially important objects in complex environment. Most computational visual attention models are designed with inspirations from mammalian visual systems. However, electrophysiological and behavioral evidences indicate that avian species are animals with high visual capability that can process complex information accurately in real time. Therefore, the visual system of the avian species, especially the nuclei related to the visual attention mechanism, are investigated in this paper. Afterwards, a hierarchical visual attention model is proposed for saliency detection. The optic tectum neuron responses are computed and the self-information is used to compute primary saliency maps in the first hierarchy. The "winner-take-all" network in the tecto-isthmal projection is simulated and final saliency maps are estimated with the regularized random walks ranking in the second hierarchy. Comparison results verify that the proposed model, which can define the focus of attention accurately, outperforms several state-of-the-art models. This study provides insights into the relationship between the visual attention mechanism and the avian visual pathways. The computational visual attention model may reveal the underlying neural mechanism of the nuclei for biological visual attention.
Optimal Integrated Schedule of Entire Process of Dual-blade Multi-cluster Tools From Start-up to Close-down
Qinghua Zhu, Yan Qiao, Naiqi Wu
2019, 6(2): 553-565. doi: 10.1109/JAS.2019.1911411
Abstract(1514) HTML (846) PDF(30)
Abstract:
Multi-cluster tools are widely used in majority of wafer fabrication processes in semiconductor industry. Smaller lot production, thinner circuit width in wafers, larger wafer size, and maintenance have resulted in a large quantity of their start-up and close-down transient periods. Yet, most of existing efforts have been concentrated on scheduling their steady states. Different from such efforts, this work schedules their transient and steady-state periods subject to wafer residency constraints. It gives the schedulability conditions for the steady-state scheduling of dual-blade robotic multi-cluster tools and a corresponding algorithm for finding an optimal schedule. Based on the robot synchronization conditions, a linear program is proposed to figure out an optimal schedule for a start-up period, which ensures a tool to enter the desired optimal steady state. Another linear program is proposed to find an optimal schedule for a close-down period that evolves from the steady state period. Finally, industrial cases are presented to illustrate how the provided method outperforms the existing approach in terms of system throughput improvement.
A Sliding Mode Approach to Enhance the Power Quality of Wind Turbines Under Unbalanced Voltage Conditions
Mohammad Javad Morshed, Afef Fekih
2019, 6(2): 566-574. doi: 10.1109/JAS.2019.1911414
Abstract(1594) HTML (833) PDF(65)
Abstract:
An integral terminal sliding mode-based control design is proposed in this paper to enhance the power quality of wind turbines under unbalanced voltage conditions. The design combines the robustness, fast response, and high quality transient characteristics of the integral terminal sliding mode control with the estimation properties of disturbance observers. The controller gains were auto-tuned using a fuzzy logic approach. The effectiveness of the proposed design was assessed under deep voltage sag conditions and parameter variations. Its dynamic response was also compared to that of a standard SMC approach. The performance analysis and simulation results confirmed the ability of the proposed approach to maintain the active power, currents, DC-link voltage and electromagnetic torque within their acceptable ranges even under the most severe unbalanced voltage conditions. It was also shown to be robust to uncertainties and parameter variations, while effectively mitigating chattering in comparison with the standard SMC.
Power Aggregation Operators of Simplified Neutrosophic Sets and Their Use in Multi-attribute Group Decision Making
Chunfang Liu, Yuesheng Luo
2019, 6(2): 575-583. doi: 10.1109/JAS.2017.7510424
Abstract(1086) HTML (479) PDF(48)
Abstract:
The simplified neutrosophic set (SNS) is a useful generalization of the fuzzy set that is designed for some practical situations in which each element has different truth membership function, indeterminacy membership function and falsity membership function. In this paper, we develop a series of power aggregation operators called simplified neutrosophic number power weighted averaging (SNNPWA) operator, simplified neutrosophic number power weighted geometric (SNNPWG) operator, simplified neutrosophic number power ordered weighted averaging (SNNPOWA) operator and simplified neutrosophic number power ordered weighted geometric (SNNPOWG) operator. We present some useful properties of the operators and discuss the relationships among them. Moreover, an approach to multi-attribute group decision making (MAGDM) within the framework of SNSs is developed by the above aggregation operators. Finally, a practical application of the developed approach to deal with the problem of investment is given, and the result shows that our approach is reasonable and effective in dealing with uncertain decision making problems.
Graph Regularized $L_p$ Smooth Non-negative Matrix Factorization for Data Representation
Chengcai Leng, Hai Zhang, Guorong Cai, Irene Cheng, Anup Basu
2019, 6(2): 584-595. doi: 10.1109/JAS.2019.1911417
Abstract(1145) HTML (550) PDF(61)
Abstract:
This paper proposes a Graph regularized $L_p$ smooth non-negative matrix factorization (GSNMF) method by incorporating graph regularization and $L_p$ smoothing constraint, which considers the intrinsic geometric information of a data set and produces smooth and stable solutions. The main contributions are as follows: first, graph regularization is added into NMF to discover the hidden semantics and simultaneously respect the intrinsic geometric structure information of a data set. Second, the $L_p$ smoothing constraint is incorporated into NMF to combine the merits of isotropic ($L_{2}$-norm) and anisotropic ($L_{1}$-norm) diffusion smoothing, and produces a smooth and more accurate solution to the optimization problem. Finally, the update rules and proof of convergence of GSNMF are given. Experiments on several data sets show that the proposed method outperforms related state-of-the-art methods.
The Combination of Two Control Strategies for Series Hybrid Electric Vehicles
Can Luo, Zhen Shen, Simos Evangelou, Gang Xiong, Fei-Yue Wang
2019, 6(2): 596-608. doi: 10.1109/JAS.2019.1911420
Abstract(1210) HTML (486) PDF(77)
Abstract:
With most countries paying attention to the environment protection, hybrid electric vehicles have become a focus of automobile research and development due to the characteristics of energy saving and low emission. Power follower control strategy (PFCS) and DC-link voltage control strategy are two sorts of control strategies for series hybrid electric vehicles (HEVs). Combining those two control strategies is a new idea for control strategy of series hybrid electric vehicles. By tuning essential parameters which are the defined constants under DC-link voltage control and under PFCS, the points of minimum mass of equivalent fuel consumption (EFC) corresponding to a series of variables are marked for worldwide harmonized light vehicles test procedure (WLTP). The fuel economy of series HEVs with the combination control schemes performs better compared with individual control scheme. The results show the effects of the combination control schemes for series HEVs driving in an urban environment.