Network-Based T–S Fuzzy Dynamic Positioning Controller Design for Unmanned Marine Vehicles

Abstract

This paper is concerned with a Takagi-Sugeno (T-S) fuzzy dynamic positioning controller design for an unmanned marine vehicle (UMV) in network environments. Network-based T-S fuzzy dynamic positioning system (DPS) models for the UMV are first established. Then, stability and stabilization criteria are derived by taking into consideration an asynchronous difference between the normalized membership function of the T-S fuzzy DPS and that of the controller. The proposed stabilization criteria can stabilize states of the UMV. The dynamic positioning performance analysis verifies the effectiveness of the networked modeling and the controller design.

Resilient control of networked control systems under deception attacks: A memory‐event‐triggered communication scheme

“…For given parameters m, , , , and matrix sequence K , ∈ {1, . … , m}, system (6) is asymptotically stable if there exist matrices P > 0, Q 1 > 0, Q 2 > 0, R 1 > 0, R 2 > 0, W > 0 and N, M, T, S with appropriate dimensions such that…”

“…teleoperation control, artificial vehicles, industrial automation, and unmanned marine vehicles. [1][2][3][4][5][6][7][8][9] In spite of the benefits of NCSs, there are still many tough challenges in NCSs which has to be dealt with. In recent years, great efforts have been paid to NCSs and a rich body of pioneer research studies has emerged to solve the modeling, control, filtering, and estimation problems in NCSs (see other works [10][11][12][13][14][15][16][17][18] and the references therein).…”

Guaranteed cost control of distributed parameter networked control systems with time‐varying delay

“…and then applying free weighted technologies (18), (19), (23) and inequalities (16), (17), (22) to equality (15), it can be obtained that…”

“…However, in practical NCS, there inevitably exist time delay, quantization, data packet dropouts and other uncertainties because of the introduction of the communication network, which could cause negative impact on control systems, even lead to instability. Thus, increasing research interests have recently been paid to the study of the stability and stabilization with uncertainties and time‐varying delays, such as …”

Robust event‐triggered model predictive control for cyber‐physical systems under denial‐of‐service attacks

“…Cyber‐physical systems (CPSs) where sampled signals and control inputs are generally transmitted via shared communication networks connect the cyber layer to the physical plant flexibly. Since the networks are vulnerable to cyber attacks, the analysis and control of critical infrastructures have witnessed increasing research interests, such as power‐grid, autonomous vehicles, and water distribution systems . The research areas on security of CPSs in the literature involve attack detection and identification, optimal attack scheduling, remote state estimation, resilient control under attacks, and so on.…”