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Vol. 7,  No. 2, 2020

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REVIEWS
Artificial Intelligence Applications in the Development of Autonomous Vehicles: A Survey
Yifang Ma, Zhenyu Wang, Hong Yang, Lin Yang
2020, 7(2): 315-329. doi: 10.1109/JAS.2020.1003021
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The advancement of artificial intelligence (AI) has truly stimulated the development and deployment of autonomous vehicles (AVs) in the transportation industry. Fueled by big data from various sensing devices and advanced computing resources, AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in motion. To achieve goal of full automation (i.e., self-driving), it is important to know how AI works in AV systems. Existing research have made great efforts in investigating different aspects of applying AI in AV development. However, few studies have offered the research community a thorough examination of current practices in implementing AI in AVs. Thus, this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research avenue. Specifically, it intends to analyze their use of AIs in supporting the primary applications in AVs: 1) perception; 2) localization and mapping; and 3) decision making. It investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their implementation. Based on the exploration of current practices and technology advances, this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies: 1) high definition maps, big data, and high performance computing; 2) augmented reality (AR)/virtual reality (VR) enhanced simulation platform; and 3) 5G communication for connected AVs. This paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.
Data-Driven Based Fault Prognosis for Industrial Systems: A Concise Overview
Kai Zhong, Min Han, Bing Han
2020, 7(2): 330-345. doi: 10.1109/JAS.2019.1911804
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Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs, which is vital for ensuring the stability, safety and long lifetime of degrading industrial systems. According to the results of fault prognosis, the maintenance strategy for underlying industrial systems can realize the conversion from passive maintenance to active maintenance. With the increased complexity and the improved automation level of industrial systems, fault prognosis techniques have become more and more indispensable. Particularly, the data-driven based prognosis approaches, which tend to find the hidden fault factors and determine the specific fault occurrence time of the system by analysing historical or real-time measurement data, gain great attention from different industrial sectors. In this context, the major task of this paper is to present a systematic overview of data-driven fault prognosis for industrial systems. Firstly, the characteristics of different prognosis methods are revealed with the data-based ones being highlighted. Moreover, based on the different data characteristics that exist in industrial systems, the corresponding fault prognosis methodologies are illustrated, with emphasis on analyses and comparisons of different prognosis methods. Finally, we reveal the current research trends and look forward to the future challenges in this field. This review is expected to serve as a tutorial and source of references for fault prognosis researchers.
Review of Antiswing Control of Shipboard Cranes
Yuchi Cao, Tieshan Li
2020, 7(2): 346-354. doi: 10.1109/JAS.2020.1003024
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Shipboard cranes are extensively utilized in numerous fields such as cargo transferring and offshore engineering. The control of shipboard cranes, especially the antiswing control of payloads, has attracted much research attention due to their typical underactuation characteristics and complicated dynamics. Through comparisons of the traditional land-fixed cranes, a brief review on modeling and dynamics analysis is presented to illustrate the tremendous challenges and difficulties in controller design for shipboard cranes. A comprehensive review and brief analysis of shipboard crane control strategies are further presented. Some future research directions are also put forward for reference. It is expected that the paper will be useful for improving existing control schemes and generating novel control approaches for shipboard crane systems.
Research Progress of Parallel Control and Management
Gang Xiong, Xisong Dong, Hao Lu, Dayong Shen
2020, 7(2): 355-367. doi: 10.1109/JAS.2019.1911792
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Based on ACP (artificial systems, computational experiments, and parallel execution) methodology, parallel control and management has become a popularly systematic and complete solution for the control and management of complex systems. This paper focuses on summarizing comprehensive review of the research literature of parallel control and management achieved in the recent years including the theoretical framework, core technologies, and the application demonstration. The future research, application directions, and suggestions are also discussed.
PAPERS
Influence of Data Clouds Fusion From 3D Real-Time Vision System on Robotic Group Dead Reckoning in Unknown Terrain
Mykhailo Ivanov, Oleg Sergyienko, Vera Tyrsa, Lars Lindner, Wendy Flores-Fuentes, Julio Cesar Rodríguez-Quiñonez, Wilmar Hernandez, Paolo Mercorelli
2020, 7(2): 368-385. doi: 10.1109/JAS.2020.1003027
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This paper proposes the solution of tasks set required for autonomous robotic group behavior optimization during the mission on a distributed area in a cluttered hazardous terrain. The navigation scheme uses the benefits of the original real-time technical vision system (TVS) based on a dynamic triangulation principle. The method uses TVS output data with fuzzy logic rules processing for resolution stabilization. Based on previous researches, the dynamic communication network model is modified to implement the propagation of information with a feedback method for more stable data exchange inside the robotic group. According to the comparative analysis of approximation methods, in this paper authors are proposing to use two-steps post-processing path planning aiming to get a smooth and energy-saving trajectory. The article provides a wide range of studies and computational experiment results for different scenarios for evaluation of common cloud point influence on robotic motion planning.
Effect of a Traffic Speed Based Cruise Control on an Electric Vehicle’s Performance and an Energy Consumption Model of an Electric Vehicle
Anil K. Madhusudhanan, Xiaoxiang Na
2020, 7(2): 386-394. doi: 10.1109/JAS.2020.1003030
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This paper proposes a cruise control system (CCS) to improve an electric vehicle’s range, which is a significant hurdle in market penetration of electric vehicles. A typical driver or a conventional adaptive cruise control (ACC) controls an electric vehicle (EV) such that it follows a lead vehicle or drives close to the speed limit. This driving behaviour may cause the EV to cruise significantly above the average traffic speed. It may later require the EV to slow down due to the traffic ripples, wasting a part of the EV’s kinetic energy. In addition, the EV will also waste higher speed dependent dissipative energies, which are spent to overcome the aerodynamic drag force and rolling resistance. This paper proposes a CCS to address this issue. The proposed CCS controls an EV’s speed such that it prevents the vehicle from speeding significantly above the average traffic speed. In addition, it maintains a safe inter-vehicular distance from the lead vehicle. The design and simulation analysis of the proposed CCS were in a MATLAB simulation environment. The simulation environment includes an energy consumption model of an EV, which was developed using data collected from an electric bus operation in London. In the simulation analysis, the proposed system reduced the EV’s energy consumption by approximately 36.6% in urban drive cycles and 15.4% in motorway drive cycles. Finally, the experimental analysis using a Nissan e-NV200 on two urban routes showed approximately 30.8% energy savings.
Proximity Based Automatic Data Annotation for Autonomous Driving
Chen Sun, Jean M. Uwabeza Vianney, Ying Li, Long Chen, Li Li, Fei-Yue Wang, Amir Khajepour, Dongpu Cao
2020, 7(2): 395-404. doi: 10.1109/JAS.2020.1003033
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The recent development in autonomous driving involves high-level computer vision and detailed road scene understanding. Today, most autonomous vehicles employ expensive high quality sensor-set such as light detection and ranging (LIDAR) and HD maps with high level annotations. In this paper, we propose a scalable and affordable data collection and annotation framework, image-to-map annotation proximity (I2MAP), for affordance learning in autonomous driving applications. We provide a new driving dataset using our proposed framework for driving scene affordance learning by calibrating the data samples with available tags from online database such as open street map (OSM). Our benchmark consists of 40 000 images with more than 40 affordance labels under various day time and weather even with very challenging heavy snow. We implemented sample advanced driver-assistance systems (ADAS) functions by training our data with neural networks (NN) and cross-validate the results on benchmarks like KITTI and BDD100K, which indicate the effectiveness of our framework and training models.
Stability in Probability and Inverse Optimal Control of Evolution Systems Driven by Lévy Processes
Khac Duc Do
2020, 7(2): 405-419. doi: 10.1109/JAS.2020.1003036
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This paper first develops a Lyapunov-type theorem to study global well-posedness (existence and uniqueness of the strong variational solution) and asymptotic stability in probability of nonlinear stochastic evolution systems (SESs) driven by a special class of Lévy processes, which consist of Wiener and compensated Poisson processes. This theorem is then utilized to develop an approach to solve an inverse optimal stabilization problem for SESs driven by Lévy processes. The inverse optimal control design achieves global well-posedness and global asymptotic stability of the closed-loop system, and minimizes a meaningful cost functional that penalizes both states and control. The approach does not require to solve a Hamilton-Jacobi-Bellman equation (HJBE). An optimal stabilization of the evolution of the frequency of a certain genetic character from the population is included to illustrate the theoretical developments.
Tracking Control of Uncertain Nonlinear Systems With Unknown Constant Input Delay
Ashish Kumar Jain, Shubhendu Bhasin
2020, 7(2): 420-425. doi: 10.1109/JAS.2019.1911807
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A robust delay compensator has been developed for a class of uncertain nonlinear systems with an unknown constant input delay. The control law consists of feedback terms based on the integral of past control values and a novel filtered tracking error, capable of compensating for input delays. Suitable Lyapunov-Krasovskii functionals are used to prove global uniformly ultimately bounded (GUUB) tracking, provided certain sufficient gain conditions, dependent on the bound of the delay, are satisfied. Simulation results illustrate the performance and robustness of the controller for different values of input delay.
Securing Parked Vehicle Assisted Fog Computing With Blockchain and Optimal Smart Contract Design
Xumin Huang, Dongdong Ye, Rong Yu, Lei Shu
2020, 7(2): 426-441. doi: 10.1109/JAS.2020.1003039
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Vehicular fog computing (VFC) has been envisioned as an important application of fog computing in vehicular networks. Parked vehicles with embedded computation resources could be exploited as a supplement for VFC. They cooperate with fog servers to process offloading requests at the vehicular network edge, leading to a new paradigm called parked vehicle assisted fog computing (PVFC). However, each coin has two sides. There is a follow-up challenging issue in the distributed and trustless computing environment. The centralized computation offloading without tamper-proof audit causes security threats. It could not guard against false-reporting, free-riding behaviors, spoofing attacks and repudiation attacks. Thus, we leverage the blockchain technology to achieve decentralized PVFC. Request posting, workload undertaking, task evaluation and reward assignment are organized and validated automatically through smart contract executions. Network activities in computation offloading become transparent, verifiable and traceable to eliminate security risks. To this end, we introduce network entities and design interactive smart contract operations across them. The optimal smart contract design problem is formulated and solved within the Stackelberg game framework to minimize the total payments for users. Security analysis and extensive numerical results are provided to demonstrate that our scheme has high security and efficiency guarantee.
Operator-Based Robust Nonlinear Free Vibration Control of a Flexible Plate With Unknown Input Nonlinearity
Guang Jin, Mingcong Deng
2020, 7(2): 442-450. doi: 10.1109/JAS.2020.1003042
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In this paper, a robust nonlinear free vibration control design using an operator based robust right coprime factorization approach is considered for a flexible plate with unknown input nonlinearity. With considering the effect of unknown input nonlinearity from the piezoelectric actuator, operator based controllers are designed to guarantee the robust stability of the nonlinear free vibration control system. Simultaneously, for ensuring the desired tracking performance and reducing the effect of unknown input nonlinearity, operator based tracking compensator and estimation structure are given, respectively. Finally, both simulation and experimental results are shown to verify the effectiveness of the proposed control scheme.
Which is the Best PID Variant for Pneumatic Soft Robots? An Experimental Study
Ameer Hamza Khan, Zili Shao, Shuai Li, Qixin Wang, Nan Guan
2020, 7(2): 451-460. doi: 10.1109/JAS.2020.1003045
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This paper presents an experimental study to compare the performance of model-free control strategies for pneumatic soft robots. Fabricated using soft materials, soft robots have gained much attention in academia and industry during recent years because of their inherent safety in human interaction. However, due to structural flexibility and compliance, mathematical models for these soft robots are nonlinear with an infinite degree of freedom (DOF). Therefore, accurate position (or orientation) control and optimization of their dynamic response remains a challenging task. Most existing soft robots currently employed in industrial and rehabilitation applications use model-free control algorithms such as PID. However, to the best of our knowledge, there has been no systematic study on the comparative performance of model-free control algorithms and their ability to optimize dynamic response, i.e., reduce overshoot and settling time. In this paper, we present comparative performance of several variants of model-free PID-controllers based on extensive experimental results. Additionally, most of the existing work on model-free control in pneumatic soft-robotic literature use manually tuned parameters, which is a time-consuming, labor-intensive task. We present a heuristic-based coordinate descent algorithm to tune the controller parameter automatically. We presented results for both manual tuning and automatic tuning using the Ziegler–Nichols method and proposed algorithm, respectively. We then used experimental results to statistically demonstrate that the presented automatic tuning algorithm results in high accuracy. The experiment results show that for soft robots, the PID-controller essentially reduces to the PI controller. This behavior was observed in both manual and automatic tuning experiments; we also discussed a rationale for removing the derivative term.
BAS-ADAM: An ADAM Based Approach to Improve the Performance of Beetle Antennae Search Optimizer
Ameer Hamza Khan, Xinwei Cao, Shuai Li, Vasilios N. Katsikis, Liefa Liao
2020, 7(2): 461-471. doi: 10.1109/JAS.2020.1003048
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In this paper, we propose enhancements to Beetle Antennae search (BAS) algorithm, called BAS-ADAM, to smoothen the convergence behavior and avoid trapping in local-minima for a highly non-convex objective function. We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation (ADAM) update rule. The proposed algorithm also increases the convergence rate in a narrow valley. A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size. Since ADAM is traditionally used with gradient-based optimization algorithms, therefore we first propose a gradient estimation model without the need to differentiate the objective function. Resultantly, it demonstrates excellent performance and fast convergence rate in searching for the optimum of non-convex functions. The efficiency of the proposed algorithm was tested on three different benchmark problems, including the training of a high-dimensional neural network. The performance is compared with particle swarm optimizer (PSO) and the original BAS algorithm.
Robust Adaptive Attitude Control for Non-rigid Spacecraft With Quantized Control Input
Yun Li, Fan Yang
2020, 7(2): 472-481. doi: 10.1109/JAS.2020.1003000
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In this paper, an adaptive backstepping control scheme is proposed for attitude tracking of non-rigid spacecraft in the presence of input quantization, inertial uncertainty and external disturbance. The control signal for each actuator is quantized by sector-bounded quantizers, including the logarithmic quantizer and the hysteresis quantizer. By describing the impact of quantization in a new affine model and introducing a smooth function and a novel form of the control signal, the influence caused by input quantization and external disturbance is properly compensated for. Moreover, with the aid of the adaptive control technique, our approach can achieve attitude tracking without the explicit knowledge of inertial parameters. Unlike existing attitude control schemes for spacecraft, in this paper, the quantization parameters can be unknown, and the bounds of inertial parameters and disturbance are also not needed. In addition to proving the stability of the closed-loop system, the relationship between the control performance and design parameters is analyzed. Simulation results are presented to illustrate the effectiveness of the proposed scheme.
Takagi-Sugeno Fuzzy Regulator Design for Nonlinear and Unstable Systems Using Negative Absolute Eigenvalue Approach
Ravi V. Gandhi, Dipak M. Adhyaru
2020, 7(2): 482-493. doi: 10.1109/JAS.2019.1911444
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This paper introduces a Takagi-Sugeno (T-S) fuzzy regulator design using the negative absolute eigenvalue (NAE) approach for a class of nonlinear and unstable systems. The open-loop system is initially embodied by the traditional T-S fuzzy model and then, all closed-loop subsystems are combined using the proposed Max-Min operator in place of traditional weighted average operator from the controller side to lessen the coupling virtually and simplify the proposed regulator design. For each virtually decoupled closed-loop subsystem, the composite regulators (i.e., primary and secondary regulators) are designed by the NAE approach based on the enhanced eigenvalue analysis. The Lyapunov function is utilized to guarantee the asymptotic stability of the overall T-S fuzzy control system. The most popular and widely used nonlinear and unstable systems like the electromagnetic levitation system (EMLS) and the inverted cart pendulum (ICP) are simulated for the wide range of the initial conditions and the enormous variation in the disturbance. The transient and steady-state performance of the considered systems using the proposed design are analyzed in terms of the decay rate, settling time and integral errors as IAE, ISE, ITAE, and ITSE to validate the effectiveness of the proposed approach compared to the most popular and traditional parallel distributed compensation (PDC) approach.
An Improved Cooperative Team Spraying Control of a Diffusion Process With a Moving or Static Pollution Source
Juan Chen, Baotong Cui, YangQuan Chen, Bo Zhuang
2020, 7(2): 494-504. doi: 10.1109/JAS.2019.1911519
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This paper is concerned with a control problem of a diffusion process with the help of static mesh sensor networks in a certain region of interest and a team of networked mobile actuators carrying chemical neutralizers. The major contribution of this paper can be divided into three parts: the first is the construction of a cyber-physical system framework based on centroidal Voronoi tessellations (CVTs), the second is the convergence analysis of the actuators location, and the last is a novel proportional integral (PI) control method for actuator motion planning and neutralizing control (e.g., spraying) of a diffusion process with a moving or static pollution source, which is more effective than a proportional (P) control method. An optimal spraying control cost function is constructed. Then, the minimization problem of the spraying amount is addressed. Moreover, a new CVT algorithm based on the novel PI control method, henceforth called PI-CVT algorithm, is introduced together with the convergence analysis of the actuators location via a PI control law. Finally, a modified simulation platform called diffusion-mobile-actuators-sensors-2-dimension-proportional integral derivative (Diff-MAS2D-PID) is illustrated. In addition, a numerical simulation example for the diffusion process is presented to verify the effectiveness of our proposed controllers.
Circular Formation Flight Control for Unmanned Aerial Vehicles With Directed Network and External Disturbance
Yangyang Chen, Rui Yu, Ya Zhang, Chenglin Liu
2020, 7(2): 505-516. doi: 10.1109/JAS.2019.1911669
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This paper proposes a new distributed formation flight protocol for unmanned aerial vehicles (UAVs) to perform coordinated circular tracking around a set of circles on a target sphere. Different from the previous results limited in bidirectional networks and disturbance-free motions, this paper handles the circular formation flight control problem with both directed network and spatiotemporal disturbance with the knowledge of its upper bound. Distinguishing from the design of a common Lyapunov function for bidirectional cases, we separately design the control for the circular tracking subsystem and the formation keeping subsystem with the circular tracking error as input. Then the whole control system is regarded as a cascade connection of these two subsystems, which is proved to be stable by input-to-state stability (ISS) theory. For the purpose of encountering the external disturbance, the backstepping technology is introduced to design the control inputs of each UAV pointing to North and Down along the special sphere (say, the circular tracking control algorithm) with the help of the switching function. Meanwhile, the distributed linear consensus protocol integrated with anther switching anti-interference item is developed to construct the control input of each UAV pointing to east along the special sphere (say, the formation keeping control law) for formation keeping. The validity of the proposed control law is proved both in the rigorous theory and through numerical simulations.
Optimal State Estimation and Fault Diagnosis for a Class of Nonlinear Systems
Hamed Kazemi, Alireza Yazdizadeh
2020, 7(2): 517-526. doi: 10.1109/JAS.2020.1003051
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This study proposes a scheme for state estimation and, consequently, fault diagnosis in nonlinear systems. Initially, an optimal nonlinear observer is designed for nonlinear systems subject to an actuator or plant fault. By utilizing Lyapunov's direct method, the observer is proved to be optimal with respect to a performance function, including the magnitude of the observer gain and the convergence time. The observer gain is obtained by using approximation of Hamilton-Jacobi-Bellman (HJB) equation. The approximation is determined via an online trained neural network (NN). Next a class of affine nonlinear systems is considered which is subject to unknown disturbances in addition to fault signals. In this case, for each fault the original system is transformed to a new form in which the proposed optimal observer can be applied for state estimation and fault detection and isolation (FDI). Simulation results of a single-link flexible joint robot (SLFJR) electric drive system show the effectiveness of the proposed methodology.
A New Design Approach for Nearly Linear Phase Stable IIR Filter using Fractional Derivative
Nikhil Agrawal, Anil Kumar, Varun Bajaj
2020, 7(2): 527-538. doi: 10.1109/JAS.2020.1003054
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In this paper, a new design method for digital infinite impulse response (IIR) filters with nearly linear-phase response is presented using fractional derivative constraints (FDCs). In the proposed method, design problem of an IIR filter is constructed as the minimization of phase error between the desired and designed phase response of an allpass filter (APF) such that the designed lowpass filter (LPF) or highpass filter (HPF) yields less passband (ep), and stopband errors (es) with optimal stopband attenuation (As). In order to have accurate passband (pb) response, FDCs are imposed on appropriate reference frequency, where the optimality of these FDCs are ensured by using a new greedy based sorting mechanism. The simulated results reflect the efficiency of the proposed method in term of improved passband response along with better transition width. However, small reduction in As is observed within the allowable limit, when compared to non-fractional design approach, but the designed filter remains immune to wordlength (WL) effect.
Fixed-time Sliding Mode Formation Control of AUVs Based on a Disturbance Observer
Zhenyu Gao, Ge Guo
2020, 7(2): 539-545. doi: 10.1109/JAS.2020.1003057
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In this paper, we investigate formation tracking control of autonomous underwater vehicles (AUVs) with model parameter uncertainties and external disturbances. The external disturbances due to the wind, waves, and ocean currents are combined with the model parameter uncertainties as a compound disturbance. Then a disturbance observer (DO) is introduced to estimate the compound disturbance, which can be achieved within a finite time independent of the initial estimation error. Based on a DO, a novel fixed-time sliding control scheme is developed, by which the follower vehicle can track the leader vehicle with all the states globally stabilized within a given settling time. The effectiveness and performance of the method are demonstrated by numerical simulations.
Linguistic Single-Valued Neutrosophic Power Aggregation Operators and Their Applications to Group Decision-Making Problems
Harish Garg, Nancy
2020, 7(2): 546-558. doi: 10.1109/JAS.2019.1911522
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Linguistic single-valued neutrosophic set (LSVNS) is a more reliable tool, which is designed to handle the uncertainties of the situations involving the qualitative data. In the present manuscript, we introduce some power aggregation operators (AOs) for the LSVNSs, whose purpose is to diminish the influence of inevitable arguments about the decision-making process. For it, first we develop some averaging power operators, namely, linguistic single-valued neutrosophic (LSVN) power averaging, weighted average, ordered weighted average, and hybrid averaging AOs along with their desirable properties. Further, we extend it to the geometric power AOs for LSVNSs. Based on the proposed work; an approach to solve the group decision-making problems is given along with the numerical example. Finally, a comparative study and the validity tests are present to discuss the reliability of the proposed operators.
A Method for Deploying the Minimal Number of UAV Base Stations in Cellular Networks
Hailong Huang, Chao Huang, Dazhong Ma
2020, 7(2): 559-567. doi: 10.1109/JAS.2019.1911813
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In this paper, we consider the scenario of using unmanned aerial vehicles base stations (UAV-BSs) to serve cellular users. In particular, we focus on finding the minimum number of UAV-BSs as well as their deployment. We propose an optimization model which minimizes the number of UAV-BSs and optimize their positions such that the user equipment (UE) covered ratio is no less than the expectation of network suppliers, the UEs receive acceptable downlink rates, and the UAV-BSs can work in a sustainable manner. We show the NP-hardness of this problem and then propose a method to address it. The method first estimates the range of the number of UAV-BSs and then converts the original problem to one which maximizes the UE served ratio, given the number of UAV-BSs within that range. We present a maximizing algorithm to solve it with the proof of convergence. Extensive simulations based on a realistic dataset have been conducted to demonstrate the effectiveness of the proposed method.
Adaptive Output Regulation of a Class of Nonlinear Output Feedback Systems With Unknown High Frequency Gain
Yuan Jiang, Jiyang Dai
2020, 7(2): 568-574. doi: 10.1109/JAS.2020.1003060
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This paper presents an output feedback design approach based on the adaptive control scheme developed for nonlinearly parameterized systems, to achieve global output regulation for a class of nonlinear systems in output feedback form. We solve the output regulation problem without the knowledge of the sign and the value of the high frequency gain a priori. It is not necessary to have both the limiting assumptions that the exogenous signal ω and the unknown parameter μ belong to a prior known compact set and the high frequency gain has a determinate lower and upper bounds. The effectiveness of the proposed algorithm is shown with the help of an example.
Optimal Neuro-Control Strategy for Nonlinear Systems With Asymmetric Input Constraints
Xiong Yang, Bo Zhao
2020, 7(2): 575-583. doi: 10.1109/JAS.2020.1003063
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In this paper, we present an optimal neuro-control scheme for continuous-time (CT) nonlinear systems with asymmetric input constraints. Initially, we introduce a discounted cost function for the CT nonlinear systems in order to handle the asymmetric input constraints. Then, we develop a Hamilton-Jacobi-Bellman equation (HJBE), which arises in the discounted cost optimal control problem. To obtain the optimal neurocontroller, we utilize a critic neural network (CNN) to solve the HJBE under the framework of reinforcement learning. The CNN’s weight vector is tuned via the gradient descent approach. Based on the Lyapunov method, we prove that uniform ultimate boundedness of the CNN’s weight vector and the closed-loop system is guaranteed. Finally, we verify the effectiveness of the present optimal neuro-control strategy through performing simulations of two examples.
Unified Smith Predictor Based H∞ Wide-Area Damping Controller to Improve the Control Resiliency to Communication Failure
Mithu Sarkar, Bidyadhar Subudhi, Sandip Ghosh
2020, 7(2): 584-596. doi: 10.1109/JAS.2020.1003066
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Inter-area low frequency oscillation in power system is one of the major problems for bulk power transmission through weak tie lines. Use of wide-area signal is more effective than the local area signal in damping out the inter-area oscillations. Wide area measurement system (WAMS) is convenient to transmit the wide area signal through the communication channel to the remote location. Communication failure is one of the disastrous phenomena in a communication channel. In this paper, a dual input single output (DISO) H∞ controller is designed to build the control resiliency by employing two highest observability ranking wide area signals with respect to the critical damping inter-area mode. The proposed controller can provide sufficient damping to the system and also the system remains stabilized if one of the wide-area signals is lost. The time delay is an unwanted phenomenon that degrades the performance of the controllers. The unified Smith predictor approach is used to design a H∞ controller to handle the time delay. Kundur’s two-area and IEEE-39 bus test systems are considered to verify the effectiveness of the proposed controller. From the simulation results, it is verified that, the proposed controller provides excellent damping performance at normal communication and improves the controller resiliency to counteract the communication failure.
Post-Processing Time-Aware Optimal Scheduling of Single Robotic Cluster Tools
QingHua Zhu, Yan Qiao, NaiQi Wu, Yan Hou
2020, 7(2): 597-605. doi: 10.1109/JAS.2020.1003069
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Integrated circuit chips are produced on silicon wafers. Robotic cluster tools are widely used since they provide a reconfigurable and efficient environment for most wafer fabrication processes. Recent advances in new semiconductor materials bring about new functionality for integrated circuits. After a wafer is processed in a processing chamber, the wafer should be removed from there as fast as possible to guarantee its high-quality integrated circuits. Meanwhile, maximization of the throughput of robotic cluster tools is desired. This work aims to perform post-processing time-aware scheduling for such tools subject to wafer residency time constraints. To do so, closed-form expression algorithms are derived to compute robot waiting time accurately upon the analysis of particular events of robot waiting for single-arm cluster tools. Examples are given to show the application and effectiveness of the proposed algorithms.
Flue Gas Monitoring System With Empirically-Trained Dictionary
Hui Cao, Yajie Yu, Panpan Zhang, Yanxia Wang
2020, 7(2): 606-616. doi: 10.1109/JAS.2019.1911642
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The monitoring of flue gas of the thermal power plants is of great significance in energy conservation and environmental protection. Spectral technique has been widely used in the gas monitoring system for predicting the concentrations of specific gas components. This paper proposes flue gas monitoring system with empirically-trained dictionary (ETD) to deal with the complexity and biases brought by the uninformative spectral data. Firstly, ETD is extracted from the raw spectral data by an alternative optimization between the sparse coding stage and the dictionary update stage to minimize the error of sparse representation. D1, D2 and D3 are three types of ETD obtained by different methods. Then, the predictive model of component concentration is constructed on the ETD. In the experiments, two real flue gas spectral datasets are collected and the proposed method combined with the partial least squares, the background propagation neural network and the support vector machines are performed. Moreover, the optimal parameters are chosen according to the 10-fold root-mean-square error of cross validation. The experimental results demonstrate that the proposed method can be used for quantitative analysis effectively and ETD can be applied to the gas monitoring systems.
Parallel Reinforcement Learning-Based Energy Efficiency Improvement for a Cyber-Physical System
Teng Liu, Bin Tian, Yunfeng Ai, Fei-Yue Wang
2020, 7(2): 617-626. doi: 10.1109/JAS.2020.1003072
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Abstract:
As a complex and critical cyber-physical system (CPS), the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy. Energy management strategy (EMS) is playing a key role to improve the energy efficiency of this CPS. This paper presents a novel bidirectional long short-term memory (LSTM) network based parallel reinforcement learning (PRL) approach to construct EMS for a hybrid tracked vehicle (HTV). This method contains two levels. The high-level establishes a parallel system first, which includes a real powertrain system and an artificial system. Then, the synthesized data from this parallel system is trained by a bidirectional LSTM network. The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning (RL) framework. PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules. Finally, real vehicle testing is implemented and relevant experiment data is collected and calibrated. Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.