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

Vol. 6,  No. 5, 2019

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Guided Crowd Evacuation: Approaches and Challenges
Min Zhou, Hairong Dong, Petros A. Ioannou, Yanbo Zhao, Fei-Yue Wang
2019, 6(5): 1081-1094. doi: 10.1109/JAS.2019.1911672
Abstract(5187) HTML (1054) PDF(213)
Evacuation leaders and/or equipment provide route and exit information for people and guide them to the expected destinations, which could make crowd evacuation more efficient in case of emergency. The purpose of this paper is to provide an overview of recent advances in guided crowd evacuation. Different guided crowd evacuation approaches are classified according to guidance approaches and technologies. A comprehensive analysis and comparison of crowd evacuation with static signage, dynamic signage, trained leader, mobile devices, mobile robot and wireless sensor networks are presented based on a single guidance mode perspective. In addition, the different evacuation guidance systems that use high-tech means such as advanced intelligent monitoring techniques, AI techniques, computer technology and intelligent inducing algorithms are reviewed from a system’s perspective. Future researches in the area of crowd evacuation are also discussed.
Modelling of a Human Driver’s Interaction with Vehicle Automated Steering Using Cooperative Game Theory
Xiaoxiang Na, David J. Cole
2019, 6(5): 1095-1107. doi: 10.1109/JAS.2019.1911675
Abstract(2567) HTML (1000) PDF(141)
The introduction of automated driving systems raised questions about how the human driver interacts with the automated system. Non-cooperative game theory is increasingly used for modelling and understanding such interaction, while its counterpart, cooperative game theory is rarely discussed for similar applications despite it may be potentially more suitable. This paper describes the modelling of a human driver’s steering interaction with an automated steering system using cooperative game theory. The distributed Model Predictive Control approach is adopted to derive the driver’s and the automated steering system’s strategies in a Pareto equilibrium sense, namely their cooperative Pareto steering strategies. Two separate numerical studies are carried out to study the influence of strategy parameters, and the influence of strategy types on the driver’s and the automated system’s steering performance. It is found that when a driver interacts with an automated steering system using a cooperative Pareto steering strategy, the driver can improve his/her performance in following a target path through increasing his/her effort in pursuing his/her own interest under the driver-automation cooperative control goal. It is also found that a driver’s adoption of cooperative Pareto steering strategy leads to a reinforcement in the driver’s steering angle control, compared to the driver’s adoption of non-cooperative Nash strategy. This in turn enables the vehicle to return from a lane-change maneuver to straight-line driving swifter.
Data-Driven Global Robust Optimal Output Regulation of Uncertain Partially Linear Systems
Adedapo Odekunle, Weinan Gao, Yebin Wang
2019, 6(5): 1108-1115. doi: 10.1109/JAS.2019.1911678
Abstract(2136) HTML (960) PDF(95)
In this paper, a data-driven control approach is developed by reinforcement learning (RL) to solve the global robust optimal output regulation problem (GROORP) of partially linear systems with both static uncertainties and nonlinear dynamic uncertainties. By developing a proper feedforward controller, the GROORP is converted into a global robust optimal stabilization problem. A robust optimal feedback controller is designed which is able to stabilize the system in the presence of dynamic uncertainties. The closed-loop system is ensured to be input-to-output stable regarding the static uncertainty as the external input. This robust optimal controller is numerically approximated via RL. Nonlinear small-gain theory is applied to show the input-to-output stability for the closed-loop system and thus solves the original GROORP. Simulation results validates the efficacy of the proposed methodology.
Quadratic Stabilization of Switched Uncertain Linear Systems: A Convex Combination Approach
Yufang Chang, Guisheng Zhai, Bo Fu, Lianglin Xiong
2019, 6(5): 1116-1126. doi: 10.1109/JAS.2019.1911681
Abstract(2759) HTML (889) PDF(93)
We consider quadratic stabilization for a class of switched systems which are composed of a finite set of continuoustime linear subsystems with norm bounded uncertainties. Under the assumption that there is no single quadratically stable subsystem, if a convex combination of subsystems is quadratically stable, then we propose a state-dependent switching law, based on the convex combination of subsystems, such that the entire switched linear system is quadratically stable. When the state information is not available, we extend the discussion to designing an output-dependent switching law by constructing a robust Luenberger observer for each subsystem.
Accurate and Robust Eye Center Localization via Fully Convolutional Networks
Yifan Xia, Hui Yu, Fei-Yue Wang
2019, 6(5): 1127-1138. doi: 10.1109/JAS.2019.1911684
Abstract(2009) HTML (662) PDF(114)
Eye center localization is one of the most crucial and basic requirements for some human-computer interaction applications such as eye gaze estimation and eye tracking. There is a large body of works on this topic in recent years, but the accuracy still needs to be improved due to challenges in appearance such as the high variability of shapes, lighting conditions, viewing angles and possible occlusions. To address these problems and limitations, we propose a novel approach in this paper for the eye center localization with a fully convolutional network (FCN), which is an end-to-end and pixels-to-pixels network and can locate the eye center accurately. The key idea is to apply the FCN from the object semantic segmentation task to the eye center localization task since the problem of eye center localization can be regarded as a special semantic segmentation problem. We adapt contemporary FCN into a shallow structure with a large kernel convolutional block and transfer their performance from semantic segmentation to the eye center localization task by fine-tuning. Extensive experiments show that the proposed method outperforms the state-of-the-art methods in both accuracy and reliability of eye center localization. The proposed method has achieved a large performance improvement on the most challenging database and it thus provides a promising solution to some challenging applications.
Resilient Fixed-Order Distributed Dynamic Output Feedback Load Frequency Control Design for Interconnected Multi-Area Power Systems
Ali Azarbahram, Amir Amini, Mahdi Sojoodi
2019, 6(5): 1139-1151. doi: 10.1109/JAS.2019.1911687
Abstract(1857) HTML (894) PDF(64)
The paper proposes a novel $ H_\infty$ load frequency control (LFC) design method for multi-area power systems based on an integral-based non-fragile distributed fixed-order dynamic output feedback (DOF) tracking-regulator control scheme. To this end, we consider a nonlinear interconnected model for multi-area power systems which also include uncertainties and time-varying communication delays. The design procedure is formulated using semi-definite programming and linear matrix inequality (LMI) method. The solution of the proposed LMIs returns necessary parameters for the tracking controllers such that the impact of model uncertainty and load disturbances are minimized. The proposed controllers are capable of receiving all or part of subsystems information, whereas the outputs of each controller are local. These controllers are designed such that the resilient stability of the overall closed-loop system is guaranteed. Simulation results are provided to verify the effectiveness of the proposed scheme. Simulation results quantify that the distributed (and decentralized) controlled system behaves well in presence of large parameter perturbations and random disturbances on the power system.
Deducing Complete Selection Rule Set for Driver Nodes to Guarantee Network's Structural Controllability
Xichen Wang, Yugeng Xi, Wenzhen Huang, Shuai Jia
2019, 6(5): 1152-1165. doi: 10.1109/JAS.2017.7510724
Abstract(1007) HTML (493) PDF(41)
Structural controllability is critical for operating and controlling large-scale complex networks. In real applications, for a given network, it is always desirable to have more selections for driver nodes which make the network structurally controllable. Different from the works in complex network field where structural controllability is often used to explore the emergence properties of complex networks at a macro level, in this paper, we investigate it for control design purpose at the application level and focus on describing and obtaining the solution space for all selections of driver nodes to guarantee structural controllability. In accord with practical applications, we define the complete selection rule set as the solution space which is composed of a series of selection rules expressed by intuitive algebraic forms. It explicitly indicates which nodes must be controlled and how many nodes need to be controlled in a node set and thus is particularly helpful for freely selecting driver nodes. Based on two algebraic criteria of structural controllability, we separately develop an input-connectivity algorithm and a relevancy algorithm to deduce selection rules for driver nodes. In order to reduce the computational complexity, we propose a pretreatment algorithm to reduce the scale of network's structural matrix efficiently, and a rearrangement algorithm to partition the matrix into several smaller ones. A general procedure is proposed to get the complete selection rule set for driver nodes which guarantee network's structural controllability. Simulation tests with efficiency analysis of the proposed algorithms are given and the result of applying the proposed procedure to some real networks is also shown, and these all indicate the validity of the proposed procedure.
A Composite State Convergence Scheme for Bilateral Teleoperation Systems
Muhammad Usman Asad, Umar Farooq, Jason Gu, Ghulam Abbas, Rong Liu, Valentina E. Balas
2019, 6(5): 1166-1178. doi: 10.1109/JAS.2019.1911690
Abstract(2175) HTML (870) PDF(53)
State convergence is a novel control algorithm for bilateral teleoperation of robotic systems. First, it models the teleoperation system on state space and considers all the possible interactions between the master and slave systems. Second, it presents an elegant design procedure which requires a set of equations to be solved in order to compute the control gains of the bilateral loop. These design conditions are obtained by turning the master-slave error into an autonomous system and imposing the desired dynamic behavior of the teleoperation system. Resultantly, the convergence of master and slave states is achieved in a well-defined manner. The present study aims at achieving a similar convergence behavior offered by state convergence controller while reducing the number of variables sent across the communication channel. The proposal suggests transmitting composite master and slave variables instead of full master and slave states while keeping the operator’s force channel intact. We show that, with these composite and force variables; it is indeed possible to achieve the convergence of states in a desired way by strictly following the method of state convergence. The proposal leads to a reduced complexity state convergence algorithm which is termed as composite state convergence controller. In order to validate the proposed scheme in the absence and presence of communication time delays, MATLAB simulations and semi-real time experiments are performed on a single degree-of-freedom teleoperation system.
On Time-Constant Robust Tuning of Fractional Order [Proportional Derivative] Controllers
Vahid Badri, Mohammad Saleh Tavazoei
2019, 6(5): 1179-1186. doi: 10.1109/JAS.2017.7510667
Abstract(2061) HTML (475) PDF(39)
This paper deals with analyzing a newly introduced method for tuning of fractional order [proportional derivative] (FO[PD]) controllers to be used in motion control. By using this tuning method, not only the phase margin and gain crossover frequency are adjustable, but also robustness to variations in the plant time-constant is guaranteed. Conditions on the values of control specifications (desired phase margin and gain crossover frequency) for solution existence in this tuning method are found. Also, the number of solutions is analytically determined in this study. Moreover, experimental verifications are presented to indicate the applicability of the obtained results.
Investigation of Knowledge Transfer Approaches to Improve the Acoustic Modeling of Vietnamese ASR System
Danyang Liu, Ji Xu, Pengyuan Zhang, Yonghong Yan
2019, 6(5): 1187-1195. doi: 10.1109/JAS.2019.1911693
Abstract(1217) HTML (508) PDF(42)
It is well known that automatic speech recognition (ASR) is a resource consuming task. It takes sufficient amount of data to train a state-of-the-art deep neural network acoustic model. As for some low-resource languages where scripted speech is difficult to obtain, data sparsity is the main problem that limits the performance of speech recognition system. In this paper, several knowledge transfer methods are investigated to overcome the data sparsity problem with the help of high-resource languages. The first one is a pre-training and fine-tuning (PT/FT) method, in which the parameters of hidden layers are initialized with a well-trained neural network. Secondly, the progressive neural networks (Prognets) are investigated. With the help of lateral connections in the network architecture, Prognets are immune to forgetting effect and superior in knowledge transferring. Finally, bottleneck features (BNF) are extracted using cross-lingual deep neural networks and serves as an enhanced feature to improve the performance of ASR system. Experiments are conducted in a low-resource Vietnamese dataset. The results show that all three methods yield significant gains over the baseline system, and the Prognets acoustic model performs the best. Further improvements can be obtained by combining the Prognets model and bottleneck features.
Stochastic Iterative Learning Control With Faded Signals
Ganggui Qu, Dong Shen
2019, 6(5): 1196-1208. doi: 10.1109/JAS.2019.1911696
Abstract(1392) HTML (490) PDF(68)
Stochastic iterative learning control (ILC) is designed for solving the tracking problem of stochastic linear systems through fading channels. Consequently, the signals used in learning control algorithms are faded in the sense that a random variable is multiplied by the original signal. To achieve the tracking objective, a two-dimensional Kalman filtering method is used in this study to derive a learning gain matrix varying along both time and iteration axes. The learning gain matrix minimizes the trace of input error covariance. The asymptotic convergence of the generated input sequence to the desired input value is strictly proved in the mean-square sense. Both output and input fading are accounted for separately in turn, followed by a general formulation that both input and output fading coexists. Illustrative examples are provided to verify the effectiveness of the proposed schemes.
A Long-Range Generalized Predictive Control Algorithm for a DFIG Based Wind Energy System
J. S. Solís-Chaves, Lucas L. Rodrigues, C. M. Rocha-Osorio, Alfeu J. Sguarezi Filho
2019, 6(5): 1209-1219. doi: 10.1109/JAS.2019.1911699
Abstract(2213) HTML (511) PDF(82)
This paper presents a new Long-range generalized predictive controller in the synchronous reference frame for a wind energy system doubly-fed induction generator based. This controller uses the state space equations that consider the rotor current and voltage as state and control variables, to execute the predictive control action. Therefore, the model of the plant must be transformed into two discrete transference functions, by means of an auto-regressive moving average model, in order to attain a discrete and decoupled controller, which makes it possible to treat it as two independent single-input single-output systems instead of a magnetic coupled multiple-input multiple-output system. For achieving that, a direct power control strategy is used, based on the past and future rotor currents and voltages estimation. The algorithm evaluates the rotor current predictors for a defined prediction horizon and computes the new rotor voltages that must be injected to controlling the stator active and reactive powers. To evaluate the controller performance, some simulations were made using Matlab/Simulink. Experimental tests were carried out with a small-scale prototype assuming normal operating conditions with constant and variable wind speed profiles. Finally, some conclusions respect to the dynamic performance of this new contro-ller are summarized.
Composite Nonlinear Bilateral Control for Teleoperation Systems With External Disturbances
Zhenhua Zhao, Jun Yang, Shihua Li, Wen-Hua Chen
2019, 6(5): 1220-1229. doi: 10.1109/JAS.2018.7511273
Abstract(922) HTML (492) PDF(31)
This paper presents a new composite nonlinear bilateral control method based on the nonlinear disturbance observer (NDOB) for teleoperation systems with external disturbances. By introducing the estimations of NDOB and systems' nominal nonlinear dynamics into controller design, a NDOB based composite nonlinear bilateral controller is constructed to attenuate the influence of disturbance and uncertain nonlinearities. As compared with the existing bilateral control methods which usually achieve force haptic (i.e., contact force tracking) through a passive way, the newly proposed method has two major merits: 1) asymptotical convergence of both position and force tracking errors is guaranteed; 2) disturbance influence on force tracking error dynamics is rejected through the direct feedforward compensation of disturbance estimation. Simulations on a nonlinear teleoperation system are carried out and the results validate the effectiveness of the proposed controller.
A Software-in-the-Loop Implementation of Adaptive Formation Control for Fixed-Wing UAVs
Jun Yang, Ximan Wang, Simone Baldi, Satish Singh, Stefano Farì
2019, 6(5): 1230-1239. doi: 10.1109/JAS.2019.1911702
Abstract(2898) HTML (968) PDF(134)
This paper discusses the design and software-in-the-loop implementation of adaptive formation controllers for fixed-wing unmanned aerial vehicles (UAVs) with parametric uncertainty in their structure, namely uncertain mass and inertia. In fact, when aiming at autonomous flight, such parameters cannot assumed to be known as they might vary during the mission (e.g. depending on the payload). Modeling and autopilot design for such autonomous fixed-wing UAVs are presented. The modeling is implemented in Matlab, while the autopilot is based on ArduPilot, a popular open-source autopilot suite. Specifically, the ArduPilot functionalities are emulated in Matlab according to the Ardupilot documentation and code, which allows us to perform software-in-the-loop simulations of teams of UAVs embedded with actual autopilot protocols. An overview of realtime path planning, trajectory tracking and formation control resulting from the proposed platform is given. The software-inthe-loop simulations show the capability of achieving different UAV formations while handling uncertain mass and inertia.
Solving Multi-Area Environmental/Economic Dispatch by Pareto-Based Chemical-Reaction Optimization Algorithm
Junqing Li, Quanke Pan, Peiyong Duan, Hongyan Sang, Kaizhou Gao
2019, 6(5): 1240-1250. doi: 10.1109/JAS.2017.7510454
Abstract(1112) HTML (478) PDF(46)
In this study, we present a Pareto-based chemical-reaction optimization (PCRO) algorithm for solving the multi-area environmental/economic dispatch optimization problems. Two objectives are minimized simultaneously, i.e., total fuel cost and emission. In the proposed algorithm, each solution is represented by a chemical molecule. A novel encoding mechanism for solving the multi-area environmental/economic dispatch optimization problems is designed to dynamically enhance the performance of the proposed algorithm. Then, an ensemble of effective neighborhood approaches is developed, and a self-adaptive neighborhood structure selection mechanism is also embedded in PCRO to increase the search ability while maintaining population diversity. In addition, a grid-based crowding distance strategy is introduced, which can obviously enable the algorithm to easily converge near the Pareto front. Furthermore, a kinetic-energy-based search procedure is developed to enhance the global search ability. Finally, the proposed algorithm is tested on sets of the instances that are generated based on realistic production. Through the analysis of experimental results, the highly effective performance of the proposed PCRO algorithm is favorably compared with several algorithms, with regards to both solution quality and diversity.
Cycle Flow Formulation of Optimal Network Flow Problems and Respective Distributed Solutions
Reza Asadi, Solmaz S. Kia
2019, 6(5): 1251-1260. doi: 10.1109/JAS.2019.1911705
Abstract(1681) HTML (814) PDF(49)
In this paper, we use the cycle basis from graph theory to reduce the size of the decision variable space of optimal network flow problems by eliminating the aggregated flow conservation constraint. We use a minimum cost flow problem and an optimal power flow problem with generation and storage at the nodes to demonstrate our decision variable reduction method. The main advantage of the proposed technique is that it retains the natural sparse/decomposable structure of network flow problems. As such, the reformulated problems are still amenable to distributed solutions. We demonstrate this by proposing a distributed alternating direction method of multipliers (ADMM) solution for a minimum cost flow problem. We also show that the communication cost of the distributed ADMM algorithm for our proposed cycle-based formulation of the minimum cost flow problem is lower than that of a distributed ADMM algorithm for the original arc-based formulation.
Design of a Networked Tracking Control System With a Data-based Approach
Shiwen Tong, Dianwei Qian, Xiaoyu Yan, Jianjun Fang, Wei Liu
2019, 6(5): 1261-1267. doi: 10.1109/JAS.2018.7511093
Abstract(2095) HTML (827) PDF(116)
Tracking control is a very challenging problem in the networked control system (NCS), especially for the process with blurred mechanism and where only input-output data are available. This paper has proposed a data-based design approach for the networked tracking control system (NTCS). The method utilizes the input-output data of the controlled process to establish a predictive model with the help of fuzzy cluster modelling (FCM) technology. Then, the deduced error and error change in the future are treated as inputs of a fuzzy sliding mode controller (FSMC) to obtain a string of future control actions. These candidate control actions in the controller side are delivered to the plant side. Thus, the network induced time delays are compensated by selecting appropriate control action. Simulation outputs prove the validity of the proposed method.
An Incremental Model Transfer Method for Complex Process Fault Diagnosis
Xiaogang Wang, Xiyu Liu, Yu Li
2019, 6(5): 1268-1280. doi: 10.1109/JAS.2019.1911618
Abstract(1232) HTML (474) PDF(77)
Fault diagnosis is an important measure to ensure the safety of production, and all kinds of fault diagnosis methods are of importance in actual production process. However, the complexity and uncertainty of production process often lead to the changes of data distribution and the emergence of new fault classes, and the number of the new fault classes is unpredictable. The reconstruction of the fault diagnosis model and the identification of new fault classes have become core issues under the circumstances. This paper presents a fault diagnosis method based on model transfer learning and the main contributions of the paper are as follows: 1) An incremental model transfer fault diagnosis method is proposed to reconstruct the new process diagnosis model. 2) Breaking the limit of existing method that the new process can only have one more class of faults than the old process, this method can identify M faults more in the new process with the thought of incremental learning. 3) The method offers a solution to a series of problems caused by the increase of fault classes. Experiments based on Tennessee-Eastman process and ore grinding classification process demonstrate the effectiveness and the feasibility of the method.
Finite-time Feedback Stabilization of a Class of Input-delay Systems With Saturating Actuators via Digital Control
Xiangze Lin, Shuaiting Huang, Wanli Zhang, Shihua Li
2019, 6(5): 1281-1290. doi: 10.1109/JAS.2019.1911525
Abstract(1185) HTML (558) PDF(59)
In this paper, the problem of making an input-delay system with saturating actuators finite-time stable by virtue of digital control is investigated. A digital state feedback controller and digital observer-controller compensator are designed for two cases: when the state of the input-delay system are available or when it is unavailable. Sufficient conditions which guarantee finite-time stability of a closed-loop input-delay system are given and the proof procedure is presented in a heuristic way by constructing appropriate comparison functions. The condition can be transformed into the intersection of two curves satisfying some constraints, which reveals the relationship between designed parameters clearly. Finally, simulation results are presented to validate the method proposed in this paper.