Litcius/Paper detail

Fault Tolerant Control for Dynamic Positioning of Unmanned Marine Vehicles Based on T-S Fuzzy Model With Unknown Membership Functions

Li‐Ying Hao, He Zhang, Tieshan Li, Bin Lin, C. L. Philip Chen

2021IEEE Transactions on Vehicular Technology173 citationsDOI

Abstract

This paper proposes a novel fault tolerant control strategy for dynamic positioning of unmanned marine vehicles using the quantized feedback sliding mode control technique. Due to the complex ocean environment, the unmanned marine vehicles are modeled as the Takagi-Sugeno fuzzy system with unknown membership functions. When the membership functions are not available, traditional sliding mode control technique becomes infeasible. To tackle this difficulty, a novel quantized sliding mode control strategy based on switching mechanism is designed to compensate for thruster faults effects. In addition, the phenomenon of time-varying delay leads to conservativeness of the existing dynamic quantization parameter adjustment strategy. Then a larger quantization parameter adjustment range, by taking time delay and fault factor into account, is given. Combining the novel sliding mode controller design and the improved dynamic quantization parameter adjustment strategy, the dynamic positioning of unmanned marine vehicles with thruster faults and quantization can be maintained. Finally, the effectiveness of the proposed method is verified through the simulation comparison results.

Topics & Concepts

Control theory (sociology)Quantization (signal processing)Dynamic positioningFuzzy logicSliding mode controlMode (computer interface)Fault (geology)Control engineeringEngineeringComputer scienceFuzzy control systemVehicle dynamicsController (irrigation)Control (management)Nonlinear systemArtificial intelligenceAlgorithmAutomotive engineeringSeismologyOperating systemAgronomyMarine engineeringQuantum mechanicsBiologyPhysicsGeologyAdaptive Control of Nonlinear SystemsDistributed Control Multi-Agent SystemsStability and Control of Uncertain Systems