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. 7,  No. 1, 2020

Display Method:
Networked Control Systems: A Survey of Trends and Techniques
Xian-Ming Zhang, Qing-Long Han, Xiaohua Ge, Derui Ding, Lei Ding, Dong Yue, Chen Peng
2020, 7(1): 1-17. doi: 10.1109/JAS.2019.1911651
Abstract(8559) HTML (4168) PDF(761)
Networked control systems are spatially distributed systems in which the communication between sensors, actuators, and controllers occurs through a shared band-limited digital communication network. Several advantages of the network architectures include reduced system wiring, plug and play devices, increased system agility, and ease of system diagnosis and maintenance. Consequently, networked control is the current trend for industrial automation and has ever-increasing applications in a wide range of areas, such as smart grids, manufacturing systems, process control, automobiles, automated highway systems, and unmanned aerial vehicles. The modelling, analysis, and control of networked control systems have received considerable attention in the last two decades. The ‘control over networks’ is one of the key research directions for networked control systems. This paper aims at presenting a survey of trends and techniques in networked control systems from the perspective of ‘control over networks’, providing a snapshot of five control issues: sampled-data control, quantization control, networked control, event-triggered control, and security control. Some challenging issues are suggested to direct the future research.
Big Data Analytics in Telecommunications: Literature Review and Architecture Recommendations
Hira Zahid, Tariq Mahmood, Ahsan Morshed, Timos Sellis
2020, 7(1): 18-38. doi: 10.1109/JAS.2019.1911795
Abstract(5707) HTML (1508) PDF(204)
This paper focuses on facilitating state-of-the-art applications of big data analytics (BDA) architectures and infrastructures to telecommunications (telecom) industrial sector. Telecom companies are dealing with terabytes to petabytes of data on a daily basis. IoT applications in telecom are further contributing to this data deluge. Recent advances in BDA have exposed new opportunities to get actionable insights from telecom big data. These benefits and the fast-changing BDA technology landscape make it important to investigate existing BDA applications to telecom sector. For this, we initially determine published research on BDA applications to telecom through a systematic literature review through which we filter 38 articles and categorize them in frameworks, use cases, literature reviews, white papers and experimental validations. We also discuss the benefits and challenges mentioned in these articles. We find that experiments are all proof of concepts (POC) on a severely limited BDA technology stack (as compared to the available technology stack), i.e., we did not find any work focusing on full-fledged BDA implementation in an operational telecom environment. To facilitate these applications at research-level, we propose a state-of-the-art lambda architecture for BDA pipeline implementation (called LambdaTel) based completely on open source BDA technologies and the standard Python language, along with relevant guidelines. We discovered only one research paper which presented a relatively-limited lambda architecture using the proprietary AWS cloud infrastructure. We believe LambdaTel presents a clear roadmap for telecom industry practitioners to implement and enhance BDA applications in their enterprises.
A Stable Analytical Solution Method for Car-Like Robot Trajectory Tracking and Optimization
Keyvan Majd, Mohammad Razeghi-Jahromi, Abdollah Homaifar
2020, 7(1): 39-47. doi: 10.1109/JAS.2019.1911816
Abstract(8886) HTML (3451) PDF(212)
In this paper, the car-like robot kinematic model trajectory tracking and control problem is revisited by exploring an optimal analytical solution which guarantees the global exponential stability of the tracking error. The problem is formulated in the form of tracking error optimization in which the quadratic errors of the position, velocity, and acceleration are minimized subject to the rear-wheel car-like robot kinematic model. The input-output linearization technique is employed to transform the nonlinear problem into a linear formulation. By using the variational approach, the analytical solution is obtained, which is guaranteed to be globally exponentially stable and is also appropriate for real-time applications. The simulation results demonstrate the validity of the proposed mechanism in generating an optimal trajectory and control inputs by evaluating the proposed method in an eight-shape tracking scenario.
A New Robust Adaptive Neural Network Backstepping Control for Single Machine Infinite Power System With TCSC
Yanhong Luo, Shengnan Zhao, Dongsheng Yang, Huaguang Zhang
2020, 7(1): 48-56. doi: 10.1109/JAS.2019.1911798
Abstract(1852) HTML (589) PDF(127)
For a single machine infinite power system with thyristor controlled series compensation (TCSC) device, which is affected by system model uncertainties, nonlinear time-delays and external unknown disturbances, we present a robust adaptive backstepping control scheme based on the radial basis function neural network (RBFNN). The RBFNN is introduced to approximate the complex nonlinear function involving uncertainties and external unknown disturbances, and meanwhile a new robust term is constructed to further estimate the system residual error, which removes the requirement of knowing the upper bound of the disturbances and uncertainty terms. The stability analysis of the power system is presented based on the Lyapunov function, which can guarantee the uniform ultimate boundedness (UUB) of all parameters and states of the whole closed-loop system. A comparison is made between the RBFNN-based robust adaptive control and the general backstepping control in the simulation part to verify the effectiveness of the proposed control scheme.
Algorithms to Compute the Largest Invariant Set Contained in an Algebraic Set for Continuous-Time and Discrete-Time Nonlinear Systems
Laura Menini, Corrado Possieri, Antonio Tornambè
2020, 7(1): 57-69. doi: 10.1109/JAS.2019.1911819
Abstract(1309) HTML (590) PDF(74)
In this paper, some computational tools are proposed to determine the largest invariant set, with respect to either a continuous-time or a discrete-time system, that is contained in an algebraic set. In particular, it is shown that if the vector field governing the dynamics of the system is polynomial and the considered analytic set is a variety, then algorithms from algebraic geometry can be used to solve the considered problem. Examples of applications of the method (spanning from the characterization of the stability to the computation of the zero dynamics) are given all throughout the paper.
Asynchronous Observer Design for Switched Linear Systems: A Tube-Based Approach
Minghao Han, Ruixian Zhang, Lixian Zhang, Ye Zhao, Wei Pan
2020, 7(1): 70-81. doi: 10.1109/JAS.2019.1911822
Abstract(2002) HTML (586) PDF(106)
This paper proposes a tube-based method for the asynchronous observation problem of discrete-time switched linear systems in the presence of amplitude-bounded disturbances. Sufficient stability conditions of the nominal observer error system under mode-dependent persistent dwell-time (MPDT) switching are first established. Taking the disturbances into account, a novel asynchronous MPDT robust positive invariant (RPI) set and an asynchronous MPDT generalized RPI (GRPI) set are determined for the difference system between the nominal and disturbed observer error systems. Further, the global uniform asymptotical stability of the observer error system is established in the sense of converging to the asynchronous MPDT GRPI set, i.e., the cross section of the tube of the observer error system. Finally, the proposed results are validated on a space robot manipulator example.
Deep Imitation Learning for Autonomous Vehicles Based on Convolutional Neural Networks
Parham M. Kebria, Abbas Khosravi, Syed Moshfeq Salaken, Saeid Nahavandi
2020, 7(1): 82-95. doi: 10.1109/JAS.2019.1911825
Abstract(13227) HTML (960) PDF(206)
Providing autonomous systems with an effective quantity and quality of information from a desired task is challenging. In particular, autonomous vehicles, must have a reliable vision of their workspace to robustly accomplish driving functions. Speaking of machine vision, deep learning techniques, and specifically convolutional neural networks, have been proven to be the state of the art technology in the field. As these networks typically involve millions of parameters and elements, designing an optimal architecture for deep learning structures is a difficult task which is globally under investigation by researchers. This study experimentally evaluates the impact of three major architectural properties of convolutional networks, including the number of layers, filters, and filter size on their performance. In this study, several models with different properties are developed, equally trained, and then applied to an autonomous car in a realistic simulation environment. A new ensemble approach is also proposed to calculate and update weights for the models regarding their mean squared error values. Based on design properties, performance results are reported and compared for further investigations. Surprisingly, the number of filters itself does not largely affect the performance efficiency. As a result, proper allocation of filters with different kernel sizes through the layers introduces a considerable improvement in the performance. Achievements of this study will provide the researchers with a clear clue and direction in designing optimal network architectures for deep learning purposes.
Memory Analysis for Memristors and Memristive Recurrent Neural Networks
Gang Bao, Yide Zhang, Zhigang Zeng
2020, 7(1): 96-105. doi: 10.1109/JAS.2019.1911828
Abstract(1814) HTML (517) PDF(91)
Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operational amplifiers. Memristive neural networks are constructed by replacing resistors with memristors. This paper focuses on the memory analysis, i.e. the initial value computation, of memristors. Firstly, we present the memory analysis for a single memristor based on memristors' mathematical models with linear and nonlinear drift. Secondly, we present the memory analysis for two memristors in series and parallel. Thirdly, we point out the difference between traditional neural networks and those that are memristive. Based on the current and voltage relationship of memristors, we use mathematical analysis and SPICE simulations to demonstrate the validity of our methods.
A Delay-Dependent Anti-Windup Compensator for Wide-Area Power Systems With Time-Varying Delays and Actuator Saturation
Maddela Chinna Obaiah, Bidyadhar Subudhi
2020, 7(1): 106-117. doi: 10.1109/JAS.2019.1911558
Abstract(1283) HTML (514) PDF(70)
In this paper, a delay-dependent anti-windup compensator is designed for wide-area power systems to enhance the damping of inter-area low-frequency oscillations in the presence of time-varying delays and actuator saturation using an indirect approach. In this approach, first, a conventional wide-area damping controller is designed by using $ H_{\infty} $ output feedback with regional pole placement approach without considering time-varying delays and actuator saturation. Then to mitigate the effect of both time-varying delays and actuator saturation, an add-on delay-dependent anti-windup compensator is designed. Based on generalized sector conditions, less conservative delay-dependent sufficient conditions are derived in the form of a linear matrix inequality (LMI) to guarantee the asymptotic stability of the closed-loop system in the presence of time-varying delays and actuator saturation by using Lyapunov-Krasovskii functional and Jensen integral inequality. Based on sufficient conditions, the LMI-based optimization problem is formulated and solved to obtain the compensator gain which maximizes the estimation of the region of attraction and minimizes the upper bound of $ L_{2} $-gain. Nonlinear simulations are performed first using MATLAB/Simulink on a two-area four-machine power system to evaluate the performance of the proposed controller for two operating conditions, e.g., 3-phase to ground fault and generator 1 terminal voltage variation. Then the proposed controller is implemented in real-time on an OPAL-RT digital simulator. From the results obtained it is verified that the proposed controller provides sufficient damping to the inter-area oscillations in the presence of time-varying delays and actuator saturation and maximizes the estimation of the region of attraction.
Sliding Mode Control of Coupled Tank Systems Using Conditional Integrators
Sankata Bhanjan Prusty, Sridhar Seshagiri, Umesh Chandra Pati, Kamala Kanta Mahapatra
2020, 7(1): 118-125. doi: 10.1109/JAS.2019.1911831
Abstract(1426) HTML (511) PDF(119)
For the problem of set point regulation of the liquid level in coupled tank systems, we present a continuous sliding mode control (SMC) with a " conditional integrator”, which only provides integral action inside the boundary layer. For a special choice of the controller parameters, our design can be viewed as a PID controller with anti-windup and achieves robust regulation. The proposed controller recovers the transient response performance without control chattering. Both full-state feedback as well as output-feedback designs are presented in this work. Our output-feedback design uses a high-gain observer (HGO) which recovers the performance of a state-feedback design where plant parameters are assumed to be known. We consider both interacting as well as non-interacting tanks and analytical results for stability and transient performance are presented in both the cases. The proposed controller continuous SMC with conditional integrators (CSMCCI) provides superior results in terms of the performance measures as well as performance indices than ideal SMC, continuous SMC (CSMC) and continuous SMC with conventional integrator (CSMCI). Experimental results demonstrate good tracking performance in spite of unmodeled dynamics and disturbances.
Suppression of Chaotic Behaviors in a Complex Biological System by Disturbance Observer-based Derivative-Integral Terminal Sliding Mode
Dianwei Qian, Hui Ding, SukGyu Lee, Hyansu Bae
2020, 7(1): 126-135. doi: 10.1109/JAS.2019.1911834
Abstract(1155) HTML (487) PDF(64)
Coronary artery systems are a kind of complex biological systems. Their chaotic phenomena can lead to serious health problems and illness development. From the perspective of engineering, this paper investigates the chaos suppression problem. At first, nonlinear dynamics of coronary artery systems are presented. To suppress the chaotic phenomena, the method of derivative-integral terminal sliding mode control is adopted. Since coronary artery systems suffer from uncertainties, the technique of disturbance observer is taken into consideration. The stability of such a control system that integrates the derivative-integral terminal sliding mode controller and the disturbance observer is proven in the sense of Lyapunov. To verify the feasibility and effectiveness of the proposed strategy, simulation results are illustrated in comparison with a benchmark.
Position Control of a Flexible Manipulator Using a New Nonlinear Self-Tuning PID Controller
Santanu Kumar Pradhan, Bidyadhar Subudhi
2020, 7(1): 136-149. doi: 10.1109/JAS.2017.7510871
Abstract(1082) HTML (471) PDF(111)
In this paper, a new nonlinear self-tuning PID controller (NSPIDC) is proposed to control the joint position and link deflection of a flexible-link manipulator (FLM) while it is subjected to carry different payloads. Since, payload is a critical parameter of the FLM whose variation greatly influences the controller performance. The proposed controller guarantees stability under change in payload by attenuating the non-modeled higher order dynamics using a new nonlinear autoregressive moving average with exogenous-input (NARMAX) model of the FLM. The parameters of the FLM are identified on-line using recursive least square (RLS) algorithm and using minimum variance control (MVC) laws the control parameters are updated in real-time. This proposed NSPID controller has been implemented in real-time on an experimental set-up. The joint tracking and link deflection performances of the proposed adaptive controller are compared with that of a popular direct adaptive controller (DAC). From the obtained results, it is confirmed that the proposed controller exhibits improved performance over the DAC both in terms of accurate position tracking and quick damping of link deflections when subjected to variable payloads.
Self-triggered Consensus Control for Linear Multi-agent Systems With Input Saturation
Yanxu Su, Qingling Wang, Changyin Sun
2020, 7(1): 150-157. doi: 10.1109/JAS.2019.1911837
Abstract(1452) HTML (472) PDF(94)
In this paper, we study the consensus problem for a class of linear multi-agent systems (MASs) with consideration of input saturation under the self-triggered mechanism. In the context of discrete-time systems, a self-triggered strategy is developed to determine the time interval between the adjacent triggers. The triggering condition is designed by using the current sampled consensus error. Furthermore, the consensus control protocol is designed by means of a state feedback approach. It is shown that the considered multi-agent systems can reach consensus with the presented algorithm. Some sufficient conditions are proposed in the form of linear matrix inequalities (LMIs) to show the positively invariant property of the domain of attraction (DOA). Moreover, some sufficient conditions of controller synthesis are provided to enlarge the volume of the DOA and obtain the control gain matrix. A numerical example is simulated to demonstrate the effectiveness of the theoretical analysis results.
Distributed $H_{2}/H_\infty$ Filter Design for Discrete-Time Switched Systems
Alyazidi Nezar M., Mahmoud Magdi S.
2020, 7(1): 158-168. doi: 10.1109/JAS.2019.1911630
Abstract(939) HTML (449) PDF(37)
This paper addresses an infinite horizon distributed $\pmb{H}_{\bf 2}/\pmb{H}_{\bf\infty}$ filtering for discrete-time systems under conditions of bounded power and white stochastic signals. The filter algorithm is designed by computing a pair of gains namely the estimator and the coupling. Herein, we implement a filter to estimate unknown parameters such that the closed-loop multi-sensor accomplishes the desired performances of the proposed $\pmb H_{\bf 2}$ and $\pmb{H_{\bf\infty}}$ schemes over a finite horizon. A switched strategy is implemented to switch between the states once the operation conditions have changed due to disturbances. It is shown that the stability of the overall filtering-error system with $\pmb{H}_{\bf 2}/\pmb{H}_{\bf\infty}$ performance can be established if a piecewise-quadratic Lyapunov function is properly constructed. A simulation example is given to show the effectiveness of the proposed approach.
Fractionally Delayed Kalman Filter
Abhinoy Kumar Singh
2020, 7(1): 169-177. doi: 10.1109/JAS.2019.1911840
Abstract(1243) HTML (490) PDF(84)
The conventional Kalman filter is based on the assumption of non-delayed measurements. Several modifications appear to address this problem, but they are constrained by two crucial assumptions: 1) the delay is an integer multiple of the sampling interval, and 2) a stochastic model representing the relationship between delayed measurements and a sequence of possible non-delayed measurements is known. Practical problems often fail to satisfy these assumptions, leading to poor estimation accuracy and frequent track-failure. This paper introduces a new variant of the Kalman filter, which is free from the stochastic model requirement and addresses the problem of fractional delay. The proposed algorithm fixes the maximum delay (problem specific), which can be tuned by the practitioners for varying delay possibilities. A sequence of hypothetically defined intermediate instants characterizes fractional delays while maximum likelihood based delay identification could preclude the stochastic model requirement. Fractional delay realization could help in improving estimation accuracy. Moreover, precluding the need of a stochastic model could enhance the practical applicability. A comparative analysis with ordinary Kalman filter shows the high estimation accuracy of the proposed method in the presence of delay.
Type-2 Fuzzy Control for Driving State and Behavioral Decisions of Unmanned Vehicle
Xuanming Zhao, Hong Mo, Kefu Yan, Lingxi Li
2020, 7(1): 178-186. doi: 10.1109/JAS.2019.1911810
Abstract(1296) HTML (441) PDF(53)
In this paper, interval type-2 fuzzy sets, fuzzy comprehensive evaluation and the fuzzy control rules are synthesized to realize the control of unmanned vehicle in driving state and behavioral decisions. Compared to the type-1 fuzzy set, type-2 fuzzy sets have more advantages in handling the model based on uncertainties, linguistic information because the membership functions are fuzzy sets. Different membership functions are established for each factor when the unmanned vehicle is driving at different speed intervals. In addition, a new evaluation method is developed to analyze unmanned vehicle’s driving state. Finally, a set of dynamic fuzzy rules are sorted out, which can be applied to the unmanned vehicle’s behavioral decision-making and provide a new idea to related research.
Design of a Robust Optimal Decentralized PI Controller Based on Nonlinear Constraint Optimization For Level Regulation: An Experimental Study
Soumya Ranjan Mahapatro, Bidyadhar Subudhi, Sandip Ghosh
2020, 7(1): 187-199. doi: 10.1109/JAS.2019.1911516
Abstract(1950) HTML (858) PDF(65)
This paper presents the development of a new robust optimal decentralized PI controller based on nonlinear optimization for liquid level control in a coupled tank system. The proposed controller maximizes the closed-loop bandwidth for specified gain and phase margins, with constraints on the overshoot ratio to achieve both closed-loop performance and robustness. In the proposed work, a frequency response fitting model reduction technique is initially employed to obtain a first order plus dead time (FOPDT) model of each higher order subsystem. Furthermore, based on the reduced order model, a proposed controller is designed. The stability and performance of the proposed controller are verified by considering multiplicative input and output uncertainties. The performance of the proposed optimal robust decentralized control scheme has been compared with that of a decentralized PI controller. The proposed controller is implemented in real-time on a coupled tank system. From the obtained results, it is shown that the proposed optimal decentralized PI controller exhibits superior control performance to maintain the desired level, for both the nominal as well as the perturbed case as compared to a decentralized PI controller.
A Local Contrast Fusion Based 3D Otsu Algorithm for Multilevel Image Segmentation
Ashish Kumar Bhandari, Arunangshu Ghosh, Immadisetty Vinod Kumar
2020, 7(1): 200-213. doi: 10.1109/JAS.2019.1911843
Abstract(1788) HTML (544) PDF(112)
To overcome the shortcomings of 1D and 2D Otsu’s thresholding techniques, the 3D Otsu method has been developed. Among all Otsu’s methods, 3D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image; it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional 1D Otsu, 2D Otsu and 3D Otsu methods, as evident from the objective and subjective evaluations.
Text Detection in Natural Scene Images Using Morphological Component Analysis and Laplacian Dictionary
Shuping Liu, Yantuan Xian, Huafeng Li, Zhengtao Yu
2020, 7(1): 214-222. doi: 10.1109/JAS.2017.7510427
Abstract(1142) HTML (428) PDF(58)
Text in natural scene images usually carries abundant semantic information. However, due to variations of text and complexity of background, detecting text in scene images becomes a critical and challenging task. In this paper, we present a novel method to detect text from scene images. Firstly, we decompose scene images into background and text components using morphological component analysis (MCA), which will reduce the adverse effects of complex backgrounds on the detection results. In order to improve the performance of image decomposition, two discriminative dictionaries of background and text are learned from the training samples. Moreover, Laplacian sparse regularization is introduced into our proposed dictionary learning method which improves discrimination of dictionary. Based on the text dictionary and the sparse-representation coefficients of text, we can construct the text component. After that, the text in the query image can be detected by applying certain heuristic rules. The results of experiments show the effectiveness of the proposed method.
A Hyper-Heuristic Framework for Lifetime Maximization in Wireless Sensor Networks With A Mobile Sink
Jinghui Zhong, Zhixing Huang, Liang Feng, Wan Du, Ying Li
2020, 7(1): 223-236. doi: 10.1109/JAS.2019.1911846
Abstract(1065) HTML (452) PDF(34)
Maximizing the lifetime of wireless sensor networks (WSNs) is an important and challenging research problem. Properly scheduling the movements of mobile sinks to balance the energy consumption of wireless sensor network is one of the most effective approaches to prolong the lifetime of wireless sensor networks. However, the existing mobile sink scheduling methods either require a great amount of computational time or lack effectiveness in finding high-quality scheduling solutions. To address the above issues, this paper proposes a novel hyperheuristic framework, which can automatically construct high-level heuristics to schedule the sink movements and prolong the network lifetime. In the proposed framework, a set of low-level heuristics are defined as building blocks to construct high-level heuristics and a set of random networks with different features are designed for training. Further, a genetic programming algorithm is adopted to automatically evolve promising high-level heuristics based on the building blocks and the training networks. By using the genetic programming to evolve more effective heuristics and applying these heuristics in a greedy scheme, our proposed hyper-heuristic framework can prolong the network lifetime competitively with other methods, with small time consumption. A series of comprehensive experiments, including both static and dynamic networks, are designed. The simulation results have demonstrated that the proposed method can offer a very promising performance in terms of network lifetime and response time.
A Large Dynamic Range Floating Memristor Emulator With Equal Port Current Restriction
Yifei Pu, Bo Yu
2020, 7(1): 237-243. doi: 10.1109/JAS.2019.1911849
Abstract(1464) HTML (477) PDF(38)
In this paper, a large dynamic range floating memristor emulator (LDRFME) with equal port current restriction is proposed to be achieved by a large dynamic range floating voltage-controlled linear resistor (VCLR). Since real memristors have not been largely commercialized until now, the application of a LDRFME to memristive systems is reasonable. Motivated by this need, this paper proposes an achievement of a LDRFME based on a feasible transistor model. A first circuit extends the voltage range of the triode region of an ordinary junction field effect transistor (JFET). The idea is to use this JFET transistor as a tunable linear resistor. A second memristive non-linear circuit is used to drive the resistance of the first JFET transistor. Then those two circuits are connected together and, under certain conditions, the obtained " resistor” presents a hysteretic behavior, which is considered as a memristive effect. The electrical characteristics of a LDRFME are validated by software simulation and real measurement, respectively.
Robust Control of a Bevel-Tip Needle for Medical Interventional Procedures
Surender Hans, Felix Orlando Maria Joseph
2020, 7(1): 244-256. doi: 10.1109/JAS.2019.1911660
Abstract(1760) HTML (791) PDF(69)
In minimally invasive surgery, one of the main objectives is to ensure safety and target reaching accuracy during needle steering inside the target organ. In this research work, the needle steering approach is determined using a robust control algorithm namely the integral sliding mode control (ISMC) strategy to eliminate the chattering problem associated with the general clinical scenario. In general, the discontinuity component of feedback control input is not appropriate for the needle steering methodology due to the practical limitations of the driving actuators. Thus in ISMC, we have incorporated the replacement of the discontinuous component using a super twisting control (STC) input due to its unique features of chattering elimination and disturbance observation characteristics. In our study, the kinematic model of an asymmetric flexible bevel-tip needle in a soft-tissue phantom is used to evaluate stability analysis. A comparative study based on the analysis of chattering elimination is executed to determine the performance of the proposed control strategy in real-time needle steering with conventional sliding mode control using vision feedback through simulation and experimental results. This validates the efficacy of the proposed control strategy for clinical needle steering.
Novel Stability Criteria for Sampled-Data Systems With Variable Sampling Periods
Hanyong Shao, Jianrong Zhao, Dan Zhang
2020, 7(1): 257-262. doi: 10.1109/JAS.2017.7510376
Abstract(870) HTML (449) PDF(57)
This paper is concerned with a novel Lyapunov-like functional approach to the stability of sampled-data systems with variable sampling periods. The Lyapunov-like functional has four striking characters compared to usual ones. First, it is time-dependent. Second, it may be discontinuous. Third, not every term of it is required to be positive definite. Fourth, the Lyapunov functional includes not only the state and the sampled state but also the integral of the state. By using a recently reported inequality to estimate the derivative of this Lyapunov functional, a sampled-interval-dependent stability criterion with reduced conservatism is obtained. The stability criterion is further extended to sampled-data systems with polytopic uncertainties. Finally, three examples are given to illustrate the reduced conservatism of the stability criteria.
A New Fire Detection Method Using a Multi-Expert System Based on Color Dispersion, Similarity and Centroid Motion in Indoor Environment
Teng Wang, Leping Bu, Zhikai Yang, Peng Yuan, Jineng Ouyang
2020, 7(1): 263-275. doi: 10.1109/JAS.2019.1911546
Abstract(1148) HTML (470) PDF(45)
In this paper, a video fire detection method is proposed, which demonstrated good performance in indoor environment. Three main novel ideas have been introduced. Firstly, a flame color model in RGB and HIS color space is used to extract pre-detected regions instead of traditional motion differential method, as it’s more suitable for fire detection in indoor environment. Secondly, according to the flicker characteristic of the flame, similarity and two main values of centroid motion are proposed. At the same time, a simple but effective method for tracking the same regions in consecutive frames is established. Thirdly, a multi-expert system consisting of color component dispersion, similarity and centroid motion is established to identify flames. The proposed method has been tested on a very large dataset of fire videos acquired both in real indoor environment tests and from the Internet. The experimental results show that the proposed approach achieved a balance between the false positive rate and the false negative rate, and demonstrated a better performance in terms of overall accuracy and F standard with respect to other similar fire detection methods in indoor environment.
A Self-Organizing RBF Neural Network Based on Distance Concentration Immune Algorithm
Junfei Qiao, Fei Li, Cuili Yang, Wenjing Li, Ke Gu
2020, 7(1): 276-291. doi: 10.1109/JAS.2019.1911852
Abstract(1078) HTML (426) PDF(46)
Radial basis function neural network (RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm (DCIA) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly, the information processing strength (IPS) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm (DCIA-SORBFNN) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIA-SORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors.
Distributed Adaptive Cooperative Tracking of Uncertain Nonlinear Fractional-order Multi-agent Systems
Zhitao Li, Lixin Gao, Wenhai Chen, Yu Xu
2020, 7(1): 292-300. doi: 10.1109/JAS.2019.1911858
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In this paper, the leader-following tracking problem of fractional-order multi-agent systems is addressed. The dynamics of each agent may be heterogeneous and has unknown nonlinearities. By assumptions that the interaction topology is undirected and connected and the unknown nonlinear uncertain dynamics can be parameterized by a neural network, an adaptive learning law is proposed to deal with unknown nonlinear dynamics, based on which a kind of cooperative tracking protocols are constructed. The feedback gain matrix is obtained to solve an algebraic Riccati equation. To construct the fully distributed cooperative tracking protocols, the adaptive law is also adopted to adjust the coupling weight. With the developed control laws, we can prove that all signals in the closed-loop systems are guaranteed to be uniformly ultimately bounded. Finally, a simple simulation example is provided to illustrate the established result.
Guidance Control for Parallel Parking Tasks
Jiyuan Tan, Chunling Xu, Li Li, Fei-Yue Wang, Dongpu Cao, Lingxi Li
2020, 7(1): 301-306. doi: 10.1109/JAS.2019.1911855
Abstract(1183) HTML (542) PDF(77)
Parking into small berths remains difficult for unskilled drivers. Researchers had proposed different automatic parking systems to solve this problem. The first kind of strategies (called parking trajectory planning) designs a detailed reference trajectory that links the start and ending points of a special parking task and let the vehicle track this reference trajectory so as to park into the berth. The second kind of strategies (called guidance control) just characterizes several regimes of driving actions as well as the important switching points in certain rule style and let the vehicle follows the pre-selected series of actions so as to park into the berth. Parking guidance control is simpler than parking trajectory planning. However, no studies thoroughly validated parking guidance control before. In this paper, a new automatic parking method is presented, which could characterize the desired control actions directly. Then the feasibility is examined carefully. Tests show that a simple parking guidance control strategy can work in most parallel parking tasks, if the available parking berth is not too small. This finding helps to build more concise automatic parking systems that can efficiently guide human drivers.
Event-Triggered Sliding Mode Control for Trajectory Tracking of Nonlinear Systems
Aquib Mustafa, Narendra K. Dhar, Nishchal K Verma
2020, 7(1): 307-314. doi: 10.1109/JAS.2019.1911654
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In this paper, an event-triggered sliding mode control approach for trajectory tracking problem of nonlinear input affine system with disturbance has been proposed. A second order robotic manipulator system has been modeled into a general nonlinear input affine system. Initially, the global asymptotic stability is ensured with conventional periodic sampling approach for reference trajectory tracking. Then the proposed approach of event-triggered sliding mode control is discussed which guarantees semi-global uniform ultimate boundedness. The proposed control approach guarantees non-accumulation of control updates ensuring lower bounds on inter-event triggering instants avoiding Zeno behavior in presence of the disturbance. The system shows better performance in terms of reduced control updates, ensures system stability which further guarantees optimization of resource usage and cost. The simulation results are provided for validation of proposed methodology for tracking problem by a robotic manipulator. The number of aperiodic control updates is found to be approximately 44% and 61% in the presence of constant and time-varying disturbances respectively.