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IEEE transactions on automation science and engineering
Institute of Electrical and Electronics Engineers
IEEE transactions on automation science and engineering

Institute of Electrical and Electronics Engineers

季刊

1545-5955

IEEE transactions on automation science and engineering/Journal IEEE transactions on automation science and engineeringEISCIISTP
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    IEEE Transactions on Automation Science and Engineering Information for Authors

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    Table of Contents

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    IEEE Transactions on Automation Science and Engineering Publication Information

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    IEEE Robotics and Automation Society Information

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    Robust Control of Multi-Line Re-Entrant Manufacturing Plants via Stochastic Continuum Models

    Chunyang ZhangQing GaoMichael V. BasinJinhu Lü...
    4923-4935页
    查看更多>>摘要：This paper investigates the robust intelligent control problem of multi-line re-entrant manufacturing plants. The control system is designed with a hierarchical architecture, where a nonlinear stochastic hyperbolic partial differential equation (PDE) is used to describe the system dynamics and a robust controller is designed to exponentially drive the manufacturing plants to a desired operation mode with steady feeding and production rates. The developed robust control scheme is shown to be practically implementable through convex optimization techniques. Numerical experiments are presented to demonstrate the feasibility and advantages of the proposed approach. Note to Practitioners—The motivation of this work originates from the need to develop an intelligent robust control strategy for a class of practical complex re-entrant manufacturing plants, for instance, the semiconductor wafer factory and the chemical production lines with numerous process procedures. Discrete-model-based algorithms have been extensively employed in this field due to their excellent convenience and great accuracy. However, when dealing with coupled multi-line re-entrant manufacturing plants with nonlinearities, traditional discrete-model-based methods lack rigorous theoretical analysis and, more importantly, suffer from the curse of dimensionality in many cases. To equip the re-entrant manufacturing plant with a desired operation mode that enjoys significant robustness against stochastic noises, we propose a continuum-model-based intelligent robust control strategy. The proposed method is practically useful in the sense that it can be conveniently applied to various industrial scenarios with re-entrant characteristics and the control design problem can be well solved via available convex optimization algorithms.
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    Optimal Path Planning for a Convoy-Support Vehicle Pair Through a Repairable Network

    Abhay Singh BhadoriyaChristopher M. MontezSivakumar RathinamSwaroop Darbha...
    4936-4947页
    查看更多>>摘要：In this article, we consider a multi-agent path planning problem in a partially impeded environment. The impeded environment is represented by a graph with select road segments (edges) in disrepair impeding vehicular movement in the road network. A primary vehicle, which we refer to as a convoy, wishes to travel from a starting location to a destination while minimizing some accumulated cost. The convoy may traverse an impeded edge for an additional cost (associated with repairing the edge) than if it were unimpeded. A support vehicle, which we refer to as a service vehicle, is simultaneously deployed to assist the convoy by repairing edges, reducing the cost for the convoy to traverse those edges. The convoy is permitted to wait at any vertex to allow the service vehicle to complete repairing an edge. The service vehicle is permitted to terminate its path at any vertex. The goal is then to find a pair of paths so the convoy reaches its destination while minimizing the total time (cost) the two vehicles are active, including any time the convoy waits. We refer to this problem as the Assisted Shortest Path Problem (ASPP). We present a generalized permanent labeling algorithm (GPLA) to find an optimal solution for the ASPP. We also introduce additional modifications to the labeling algorithm to significantly improve the computation time and refer to the modified labeling algorithm as GPLA*. Computational results are presented to illustrate the effectiveness of GPLA* in solving the ASPP. Note to Practitioners—One motivation for this work is to improve the efficiency of autonomous warehouse operations, where multiple robots need to coordinate their plans. Take for example two robots operating in a warehouse where one robot is moving goods and the second robot is making repairs or clearing obstructions (fallen goods, objects left by workers, etc.) along the way. The presented algorithm’s underlying structure is relatively simple and the algorithm itself does not require special software or solvers. The algorithm generates sub-optimal solutions as it progresses and terminates with the optimal solution. A large class of problems involving asynchronous actions between two or more agents can be handled using the presented algorithm or an extension of it. In this paper we restrict ourselves to two agents. A limitation of the presented algorithm and its possible extensions is the memory required as the graph representing the problem grows in size. We compare our work against an algorithm with similar approach (centralized $A^{*}$ ) and show that the presented algorithm is superior in both memory and computational time. We also present results on relatively large graphs to show the algorithm has practical value. This work can also be applied to rescue missions for people to escape wildfires, flooding or other natural disasters. A robotic agent can scout ahead for impacted pathways and assist victim(s) find the best path to escape to safety.
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    Robust High-Order Control Barrier Functions-Based Optimal Control for Constrained Nonlinear Systems With Safety-Stability Perspectives

    Jinzhu PengHaijing WangShuai DingJing Liang...
    4948-4958页
    查看更多>>摘要：In this article, we propose a robust high-order control barrier functions (HoCBFs)-based optimal control method for nonlinear systems with state constraints to achieve safety-stability perspectives. First, a kind of HoCBFs is presented for constrained nonlinear systems to address state constraints with high relative degrees. Second, the robustness property of the HoCBFs is analyzed based on the asymptotic stability of the forward invariant set. Specifically, a robust HoCBFs-based Lyapunov function is constructed to prove the uniform asymptotic stability of the set associated with the HoCBFs. In this way, a new sufficient condition is obtained for the stability analysis of the forward invariant set by using the inequalities of high-order derivatives of Lyapunov function. Third, a robust HoCBFs-based optimal control scheme is proposed for the constrained nonlinear system to achieve the safety-stability perspectives of constraints satisfaction and system stabilization, where the robust HoCBFs are combined with control Lyapunov functions (CLFs) to satisfy the small control property (SCP) in solving a quadratic program (QP). Furthermore, the proposed optimal control scheme is shown to be Lipschitz continuous and has no initial condition restrictions. Finally, two examples are presented to demonstrate the control performance of the proposed scheme. Note to Practitioners—The motivation of this article is that constraints exist widely in actual control systems, and the lack of constraint satisfaction in control systems may inevitably lead to safety defects, which usually degrade the control performances or even damage the entire system. In this article, a robust HoCBFs-based optimal control scheme is proposed for constrained nonlinear systems. The theoretical derivation demonstrates that the proposed control scheme can achieve safety-stability perspectives, which ensure system stabilization and task-oriented performance without violating the state constraints. The satisfactory control performances of the simulation on a constrained robotic manipulator show the potential practical application on a real robotic system.
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    RoboEC2: A Novel Cloud Robotic System With Dynamic Network Offloading Assisted by Amazon EC2

    Boyi LiuLujia WangMing Liu
    4959-4973页
    查看更多>>摘要：Deep neural networks (DNNs) are increasingly utilized in robotic tasks. However, resource-constrained mobile robots often do not have sufficient onboard computing resources or power reserves to run the most accurate and state-of-the-art DNNs. Cloud robotics has the benefit of enabling robots to offload DNNs to cloud servers, which is considered a promising technology to address the issue. However, comprehensive issues exist, including flexibility, convenience, offloading policy, and especially network robustness in its implementations and deployments. Although it is essential to promote cloud robotics to be practical, a cloud robotic system that addresses these issues comprehensively has never been proposed. Accordingly, in this work, we present RoboEC2, a novel cloud robotic system with dynamic network offloading implemented assisted by Amazon EC2. To realize the goal, we present a cloud-edge cooperation framework based on ROS and Amazon Web Services (AWS) and a network offloading approach with a dynamic splitting way. RoboEC2 is capable of executing its network offloading program in any conditions, including disconnected. We model the DNN offloading problem in RoboEC2 to a specific multi-objective optimization problem and address it by proposing the Spotlight Criteria Algorithm (SCA). RoboEC2 is flexible, convenient, and robust. It is the first cloud robotic system with no constraints on time, location, or computing power. Finally, We demonstrate RoboEC2 with analyses and experiments that it performs better in comprehensive metrics compared with the state-of-the-art approach. We open-source the system at https://github.com/RoboEC2/RoboEC2. Note to Practitioners—RoboEC2 is a work that combines cloud computing and robotics. As the deep learning models are becoming larger, robots are becoming more and more difficult to run the state-of-the-art models locally. It has become one of the major problems in robotics. RoboEC2 was proposed to address this problem. It enables more robotics researchers to equip their robots with the power of cloud computing. To be honest, it is very difficult for us to complete this work that is a robotic system with cloud computing. We need to address a lot of difficulties such as network, the cloud platform, algorithms, robot platforms, and conduct various robotic tasks. We have spent more than one year on this system and overcome countless difficulties to complete it. All of what we do is to make robotics developer easier strengthen their robots with cloud. Whether you are an autonomous driving engineer, robotic arm developer, SLAM researcher, mobile robotics researcher, or any other developer working on robotics applications based on ROS and deep learning models, you can use RoboEC2 to make them perform better. You don’t need to worry about networking, because RoboEC2 has solved it perfectly. You don’t need to worry about the serious algorithms in the system, because we provide easily used interact files for you to configure. You just need to tell RoboEC2 which metrics your robotics application needs to focus on. With RoboEC2, all the robotic researchers/developers are capable of enhancing their robotic applications with cloud computing in just a few simple steps and executing them in any network conditions. So, why not?
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    A Correlation Analysis-Based Multivariate Alarm Method With Maximum Likelihood Evidential Reasoning

    Xu WengXiaobin XuJing FengXufeng Shen...
    4974-4986页
    查看更多>>摘要：Correlations among process variables and inconsistencies in alarm decision making are quite common in multivariate alarm analysis, resulting in a large number of false alarms and missed alarms. The greatest challenges in multivariate alarm analysis are therefore analyzing overall correlations among all process variables and making integrated alarm decisions. In this work, a novel correlation analysis-based multivariate alarm method is developed to address these problems. First, a statistical characteristic-driven decision making trial and evaluation laboratory (DEMATEL) is proposed that can analyze the overall correlations among all process variables. Second, the sample space model (SSM) and evidence space model (ESM) can be used to convert process data into reference alarm evidence. Third, online samples are transformed into alarm evidence by matching them with the ESMs and holistically considering the data-level correlations and the evidence-level reliability and weight; the comprehensive alarm evidence is obtained by fusing this matched alarm evidence generated from the information of highly correlated or even colinear variables via maximum likelihood evidential reasoning (MAKER), and thus, more accurate and integrated alarm decisions are made. A real case study shows the superiority of the proposed method, which can therefore be generalized to other multivariate industrial processes. Note to Practitioners—Multivariate industrial processes generally have a large number of process variables, and with the rapid transfer of energy, material, and information, these process variables interact with each other or are even colinear. The focus of this study is to develop a multivariate alarm method for the correlation analysis of process variables and fusion of complementary, redundant and contradictory process information. The information fusion concept takes the place of the conventional alarm mechanism. From the perspective of the precise characterization of process information, process data are transformed into alarm evidence instead of alarm data. In addition, the proposed method can fully consider the overall correlations among all process variables and fuse each piece of process variable information to yield correct and integrated alarm decision results. It is noted that the information fusion concept is universal and can be extended to other real multivariate industrial processes.
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    Offline Data-Driven Adaptive Critic Design With Variational Inference for Wastewater Treatment Process Control

    Junfei QiaoRuyue YangDing Wang
    4987-4998页
    查看更多>>摘要：Wastewater treatment is indispensable to the functioning of urban society, and its optimal control has enormous social benefits. However, precise modelling of the unstable and complex treatment process is challenging yet crucial to the adaptive dynamic programming method. In this article, an adaptive critic algorithm with variational inference is designed to address the optimal control problem of nonlinear discrete-time systems, along with the convergence analysis. Based on the recorded system trajectory, the variational autoencoder is utilized to approximate the behavior policy of the offline dataset without system modelling and online interaction. Through policy iteration learning, the actor-critic structure can amend the policy generated by the variational autoencoder to achieve the optimal control objective. Simulations on a nonlinear system and the wastewater treatment process have verified that the proposed approach outperformed the behavior policy. Driven by the wastewater treatment process data derived from the incremental proportional-integral-derivative controller, the proposed approach can produce an optimal control policy of less tracking error and cost. Note to Practitioners—When dealing with an unknown system with complex dynamics, it is more feasible to improve the acceptable performance of the existing control policy based on the system’s trajectory than to obtain an excelling policy. Motivated by batch reinforcement learning, learning from offline data can avoid the online interaction between the system and the adaptive dynamic programming algorithm, which could lead to exploratory errors during online learning. Specifically, using a model-free adaptive dynamic programming algorithm, the parameters of the controller are instantly updated based on the experience replay buffer sampled from the online trajectory data. However, online exploration determines the update, and there is no guarantee that the system will converge every time. As a specific type of adaptive dynamic programming algorithm, adaptive critic design uses a critic network to approximate the expected future cost and an actor network to generate a control input that minimizes the expected future cost. In this article, using the converged trajectory as the offline dataset, a revised variational autoencoder is used to approximate the behavior policy of the offline dataset. As a generative model, the variational autoencoder considers a random variable that adheres to a prior distribution while producing outputs. Through offline learning, the actor network can amend the approximated policy based on the evaluation from the critic network while being constrained within the limited variation of the generative model. Finally, the objective of the optimal control task can be achieved by following the designated cost design. However, a dataset containing disturbances could impede offline learning, which needs to be addressed.
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