Meng Joo Er

Dalian Maritime University

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Ning Wang

Dalian Maritime University

Yi Zhou

Beijing Institute of Aeronautical Materials

Shiqian Wu

Wuhan University of Science and Technology

Rajasekar Venkatesan

Nanyang Technological University

Yongping Pan

National University of Singapore

Mahardhika Pratama

Nanyang Technological University

Yang Gao

Guangdong Academy of Sciences

Huibin Gong

Dalian Maritime University

Shuailin Lv

Dalian Maritime University

Amin Torabi Jahromi

Persian Gulf University

Cooperative Institutions

Nanyang Technological University

119

Dalian Maritime University

Chinese Academy of Sciences

National University of Singapore

Harbin Institute of Technology

Huazhong University of Science and Technology

Tsinghua University

Dalian University of Technology

Xi'an Jiaotong University

Shanghai Jiao Tong University

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Approximate Output Regulation of Discrete-Time Stochastic Multiagent Systems Subject to Heterogeneous and Unknown Dynamics

IEEE Transactions on Systems Man and Cybernetics Systems (2022)

Shaobao Li Feng Wang Meng Joo Er Zhenyu Yang

The output regulation approach has been extensively applied for the cooperative control of heterogeneous multiagent systems (MASs) with known dynamics, but rarely for MASs subject to stochastic and unknown dynamics. One challenging problem is that it is still unclear how to construct the regulator equations of stochastic MASs with unknown dynamics for dynamic exosystem compensation. Toward this end, this article develops a novel distributed control scheme for the approximate output regulation of discrete-time stochastic MASs subject to heterogeneous and unknown nonlinear dynamics. A distributed observer is designed for the exosystem-state estimation of agents, based upon which nonlinear regulator equations are constructed for the feedforward control design, thereby achieving distributed exosystem compensation. A high-order neural network is deployed to approximately solve the nonlinear regulator equations with unknown nonlinearity, and an adaptive control law is developed for the approximate output regulation of discrete-time stochastic MASs. Stability analysis shows that the closed-loop system is semiglobally uniformly ultimately bounded. Simulation results demonstrate the effectiveness and efficiency of the proposed control law.

Feed forward

Internal model

10.1109/tsmc.2022.3145354

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Citations (8)

Self-Constructing Adaptive Robust Fuzzy Neural Tracking Control of Surface Vehicles With Uncertainties and Unknown Disturbances

IEEE Transactions on Control Systems Technology (2014)

Ning Wang Meng Joo Er

In this paper, a novel self-constructing adaptive robust fuzzy neural control (SARFNC) scheme for tracking surface vehicles, whereby a self-constructing fuzzy neural network (SCFNN) is employed to approximate system uncertainties and unknown disturbances, is proposed. The salient features of the SARFNC scheme are as follows: 1) unlike the predefined-structure approaches, the SCFNN is able to online self-construct dynamic-structure fuzzy neural approximator by generating and pruning fuzzy rules, and achieve accurate approximation; 2) an adaptive approximation-based controller (AAC) is designed by combining sliding-mode control with SCFNN approximation using improved projection-based adaptive laws, which avoid parameter drift and singularity in membership functions simultaneously; 3) to compensate for approximation errors, a robust supervisory controller (RSC) is presented to enhance the robustness of the overall SARFNC control system; and 4) the SARFNC consisting of AAC and RSC can achieve an excellent tracking performance, whereby tracking errors and their first derivatives are globally uniformly ultimately bounded. Simulation studies and comprehensive comparisons with traditional adaptive control schemes demonstrate remarkable performance and superiority of the SARFNC scheme in terms of tracking errors and online approximation.

Robustness

Tracking error

10.1109/tcst.2014.2359880

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Citations (164)

Real-time hybrid adaptive fuzzy control of a SCARA robot

Microprocessors and Microsystems (2001)

Meng Joo Er Moo Teng Lim Hui Song Lim

SCARA

Supervisor

10.1016/s0141-9331(01)00127-2

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Citations (13)

Segmentation of Ship Satellite Images with Transfer Learning and Attention Mechanism

Lin Li Meng Joo Er Qianying Li Yani Zhang

As an integrated part of national marine supervision system, ship monitoring plays a critical role in ensuing efficient exploitation of marine resources and safety of territorial sea. In this paper semantic segmentation networks are trained using high-resolution satellite ship data to facilitate more detailed ship inspection. Using the UNet network, the network performance is optimized by improving the structure of the convolutional an attention mechanism is incorporated to enhance useful features and suppress useless features so as to improve segmentation accuracy. The encoder module obtains high-level semantic information of Synthetic Aperture Radar (SAR) remote sensing of ship images layer by layer, and the decoder module recovers spatial information step by step. Experimental results show that this method can effectively enhance accuracy of image segmentation and retain more image details.

Transfer of learning

10.1109/icoias56028.2022.9931268

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Citations (1)

A GUI-Based Automatic Sonar Image Segmentation System

Wenjie Lu Huipu Xu Meng Joo Er

In this paper, realization of a GUI-based sonar image segmentation systems employing the improved level-set method (LSM) is presented. Two coarse segmentation strategies, namely cluster connectivity analysis (KCA) and cluster morphology analysis (KMA) are proposed. A GUI that includes general image preprocessing and advanced image processing functions is designed and developed. The proposed segmentation strategies are embedded into the GUI to realize one-click processing without human intervention. Experimental results demonstrate the superior performance of the proposed strategies.

Realization (probability)

Segmentation-based object categorization

10.1109/icoias53694.2021.00018

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Optimal State-Space Realizations of Digital Controllers with Roundoff Noise Consideration

The proceedings of the IEEE Conference on Control Applications/The proceedings of the ... IEEE Conference on Control Applications (2007)

Jinxin Hao Meng Joo Er

The effect of roundoff noise in a state-space digital controller is investigated for a discrete-time control system. An analytical expression of the roundoff noise gain is obtained, with which the optimal realization problem is solved on the set of fully parametrized realizations. A sparse structure is derived and analyzed in terms of roundoff noise performance. The problem of finding optimized sparse structures is solved. A design example is given, which shows that the optimized sparse realization beats the fully parametrized optimal realization in terms of roundoff noise gain and computation efficiency.

Realization (probability)

Round-off error

10.1109/cca.2007.4389197

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Distributed Adaptive Fuzzy Control for Output Consensus of Heterogeneous Stochastic Nonlinear Multi-Agent Systems

IEEE Transactions on Fuzzy Systems (2017)

Shaobao Li Meng Joo Er Jie Zhang

This paper investigates the output consensus problem of heterogeneous stochastic nonlinear multiagent systems with directed communication topologies, with a view of making the outputs of a group of follower agents track the output of a leader. Fuzzy logic systems are applied to approximate the unknown nonlinear functions of agents. A special case that all followers can get access to the leader is first considered, and a novel decentralized adaptive fuzzy control law based on the output regulation framework is presented. Next, the proposed control scheme is further applied to design the distributed adaptive fuzzy control law for a more general case that only part of agents can get access to the leader. By applying Lyapunov stability analysis, it is shown that the outputs of followers will achieve consensus to a sufficient small bound of the output of the leader under the proposed control law. Finally, simulation results demonstrate that the proposed control law is effective and efficient. The developed distributed control scheme can be widely applied to solve the cooperative control problem of practical autonomous systems with uncertain dynamics such as synchronization of mechanical systems with vibration, formation control of autonomous underwater vehicles, etc.

Lyapunov stability

Decentralised system

10.1109/tfuzz.2017.2710949

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Citations (54)

Path Planning of Autonomous Underwater Vehicle Departing from Port Based on Improved Deep Q-Network

Chen Li Meng Joo Er Tianhe Liu Guohong Jiang

Port (circuit theory)

10.1109/icoias61634.2023.00043

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Citations (0)

Online Adaptive Fuzzy Neural Identification and Control of Nonlinear Dynamic Systems