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. 8,  No. 8, 2021

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2021, 8(8): 1355-1364. doi: 10.1109/JAS.2021.1004075
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A convex optimization model predicts an output from an input by solving a convex optimization problem. The class of convex optimization models is large, and includes as special cases many well-known models like linear and logistic regression. We propose a heuristic for learning the parameters in a convex optimization model given a dataset of input-output pairs, using recently developed methods for differentiating the solution of a convex optimization problem with respect to its parameters. We describe three general classes of convex optimization models, maximum a posteriori (MAP) models, utility maximization models, and agent models, and present a numerical experiment for each.
2021, 8(8): 1365-1379. doi: 10.1109/JAS.2021.1004078
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Among the promising application of autonomous surface vessels (ASVs) is the utilization of multiple autonomous tugs for manipulating a floating object such as an oil platform, a broken ship, or a ship in port areas. Considering the real conditions and operations of maritime practice, this paper proposes a multi-agent control algorithm to manipulate a ship to a desired position with a desired heading and velocity under the environmental disturbances. The control architecture consists of a supervisory controller in the higher layer and tug controllers in the lower layer. The supervisory controller allocates the towing forces and angles between the tugs and the ship by minimizing the error in the position and velocity of the ship. The weight coefficients in the cost function are designed to be adaptive to guarantee that the towing system functions well under environmental disturbances, and to enhance the efficiency of the towing system. The tug controller provides the forces to tow the ship and tracks the reference trajectory that is computed online based on the towing angles calculated by the supervisory controller. Simulation results show that the proposed algorithm can make the two autonomous tugs cooperatively tow a ship to a desired position with a desired heading and velocity under the (even harsh) environmental disturbances.
2021, 8(8): 1380-1388. doi: 10.1109/JAS.2021.1004081
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This work presents a novel approach combining radial basis function (RBF) interpolation with Galerkin projection to efficiently solve general optimal control problems. The goal is to develop a highly flexible solution to optimal control problems, especially nonsmooth problems involving discontinuities, while accounting for trajectory accuracy and computational efficiency simultaneously. The proposed solution, called the RBF-Galerkin method, offers a highly flexible framework for direct transcription by using any interpolant functions from the broad class of global RBFs and any arbitrary discretization points that do not necessarily need to be on a mesh of points. The RBF-Galerkin costate mapping theorem is developed that describes an exact equivalency between the Karush–Kuhn–Tucker (KKT) conditions of the nonlinear programming problem resulted from the RBF-Galerkin method and the discretized form of the first-order necessary conditions of the optimal control problem, if a set of discrete conditions holds. The efficacy of the proposed method along with the accuracy of the RBF-Galerkin costate mapping theorem is confirmed against an analytical solution for a bang-bang optimal control problem. In addition, the proposed approach is compared against both local and global polynomial methods for a robot motion planning problem to verify its accuracy and computational efficiency.
2021, 8(8): 1389-1400. doi: 10.1109/JAS.2021.1004084
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There are about 253 million people with visual impairment worldwide. Many of them use a white cane and/or a guide dog as the mobility tool for daily travel. Despite decades of efforts, electronic navigation aid that can replace white cane is still research in progress. In this paper, we propose an RGB-D camera based visual positioning system (VPS) for real-time localization of a robotic navigation aid (RNA) in an architectural floor plan for assistive navigation. The core of the system is the combination of a new 6-DOF depth-enhanced visual-inertial odometry (DVIO) method and a particle filter localization (PFL) method. DVIO estimates RNA’s pose by using the data from an RGB-D camera and an inertial measurement unit (IMU). It extracts the floor plane from the camera’s depth data and tightly couples the floor plane, the visual features (with and without depth data), and the IMU’s inertial data in a graph optimization framework to estimate the device’s 6-DOF pose. Due to the use of the floor plane and depth data from the RGB-D camera, DVIO has a better pose estimation accuracy than the conventional VIO method. To reduce the accumulated pose error of DVIO for navigation in a large indoor space, we developed the PFL method to locate RNA in the floor plan. PFL leverages geometric information of the architectural CAD drawing of an indoor space to further reduce the error of the DVIO-estimated pose. Based on VPS, an assistive navigation system is developed for the RNA prototype to assist a visually impaired person in navigating a large indoor space. Experimental results demonstrate that: 1) DVIO method achieves better pose estimation accuracy than the state-of-the-art VIO method and performs real-time pose estimation (18 Hz pose update rate) on a UP Board computer; 2) PFL reduces the DVIO-accrued pose error by 82.5% on average and allows for accurate wayfinding (endpoint position error ≤ 45 cm) in large indoor spaces.
2021, 8(8): 1401-1415. doi: 10.1109/JAS.2021.1004006
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This work investigates the problem of controller design for the inverters in an islanded microgrid. Robust $\mu$-synthesis controllers and local droop controllers are designed to regulate the output voltages of inverters and share power among them, respectively. The designed controllers alleviate the need for additional sensors to measure the states of the system by relying only on output feedback. It is shown that the designed $\mu$-synthesis controller properly damps resonant oscillations, and its performance is robust to the control-loop time delay and parameter uncertainties. The stability of a droop-controlled islanded microgrid including multiple distributed generation (DG) units is analyzed by linearizing the nonlinear power flow model around the nominal operating point and applying theorems from linear algebra. It is indicated that the droop controller stabilizes the microgrid system with dominantly inductive tie-line impedances for all values of resistive-inductive loads, while for the case of resistive-capacitive loads the stability is conditioned on an upper bound on the load susceptances. The robust performance of the designed $\mu$-synthesis controller is studied analytically, compared with the similar analysis in an $H_{\infty}$ control (benchmark) framework, and verified by simulations for a four DG benchmark microgrid. Furthermore, the robustness of the droop controllers is analyzed by Monte Carlo simulations in the presence of local voltage fluctuations and phase differences among neighboring DGs.
2021, 8(8): 1416-1427. doi: 10.1109/JAS.2021.1004054
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Developing and optimizing fuzzy relation equations are of great relevance in system modeling, which involves analysis of numerous fuzzy rules. As each rule varies with respect to its level of influence, it is advocated that the performance of a fuzzy relation equation is strongly related to a subset of fuzzy rules obtained by removing those without significant relevance. In this study, we establish a novel framework of developing granular fuzzy relation equations that concerns the determination of an optimal subset of fuzzy rules. The subset of rules is selected by maximizing their performance of the obtained solutions. The originality of this study is conducted in the following ways. Starting with developing granular fuzzy relation equations, an interval-valued fuzzy relation is determined based on the selected subset of fuzzy rules (the subset of rules is transformed to interval-valued fuzzy sets and subsequently the interval-valued fuzzy sets are utilized to form interval-valued fuzzy relations), which can be used to represent the fuzzy relation of the entire rule base with high performance and efficiency. Then, the particle swarm optimization (PSO) is implemented to solve a multi-objective optimization problem, in which not only an optimal subset of rules is selected but also a parameter ε for specifying a level of information granularity is determined. A series of experimental studies are performed to verify the feasibility of this framework and quantify its performance. A visible improvement of particle swarm optimization (about 78.56% of the encoding mechanism of particle swarm optimization, or 90.42% of particle swarm optimization with an exploration operator) is gained over the method conducted without using the particle swarm optimization algorithm.
2021, 8(8): 1428-1439. doi: 10.1109/JAS.2021.1004057
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Because pixel values of foggy images are irregularly higher than those of images captured in normal weather (clear images), it is difficult to extract and express their texture. No method has previously been developed to directly explore the relationship between foggy images and semantic segmentation images. We investigated this relationship and propose a generative adversarial network (GAN) for foggy image semantic segmentation (FISS GAN), which contains two parts: an edge GAN and a semantic segmentation GAN. The edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation GAN. The semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation images. Experiments on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.
2021, 8(8): 1440-1450. doi: 10.1109/JAS.2021.1004012
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Secure control against cyber attacks becomes increasingly significant in cyber-physical systems (CPSs). False data injection attacks are a class of cyber attacks that aim to compromise CPS functions by injecting false data such as sensor measurements and control signals. For quantified false data injection attacks, this paper establishes an effective defense framework from the energy conversion perspective. Then, we design an energy controller to dynamically adjust the system energy changes caused by unknown attacks. The designed energy controller stabilizes the attacked CPSs and ensures the dynamic performance of the system by adjusting the amount of damping injection. Moreover, with the $L_2$ disturbance attenuation technique, the burden of control system design is simplified because there is no need to design an attack observer. In addition, this secure control method is simple to implement because it avoids complicated mathematical operations. The effectiveness of our control method is demonstrated through an industrial CPS that controls a permanent magnet synchronous motor.
2021, 8(8): 1451-1464. doi: 10.1109/JAS.2021.1003904
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In this paper, we consider distributed convex optimization problems on multi-agent networks. We develop and analyze the distributed gradient method which allows each agent to compute its dynamic stepsize by utilizing the time-varying estimate of the local function value at the global optimal solution. Our approach can be applied to both synchronous and asynchronous communication protocols. Specifically, we propose the distributed subgradient with uncoordinated dynamic stepsizes (DS-UD) algorithm for synchronous protocol and the AsynDGD algorithm for asynchronous protocol. Theoretical analysis shows that the proposed algorithms guarantee that all agents reach a consensus on the solution to the multi-agent optimization problem. Moreover, the proposed approach with dynamic stepsizes eliminates the requirement of diminishing stepsize in existing works. Numerical examples of distributed estimation in sensor networks are provided to illustrate the effectiveness of the proposed approach.
2021, 8(8): 1465-1476. doi: 10.1109/JAS.2021.1004090
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In practical process industries, a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes, which indicates that the measurements coming from different sources are collected at different sampling rates. To build a complete process monitoring strategy, all these multi-rate measurements should be considered for data-based modeling and monitoring. In this paper, a novel kernel multi-rate probabilistic principal component analysis (K-MPPCA) model is proposed to extract the nonlinear correlations among different sampling rates. In the proposed model, the model parameters are calibrated using the kernel trick and the expectation-maximum (EM) algorithm. Also, the corresponding fault detection methods based on the nonlinear features are developed. Finally, a simulated nonlinear case and an actual pre-decarburization unit in the ammonia synthesis process are tested to demonstrate the efficiency of the proposed method.