Predictor-based practical fixed-time adaptive sliding mode formation control of a time-varying delayed uncertain fully-actuated surface vessel using RBFNN
| Autor: | Zhipeng Shen, Qun Wang, Yu Wang, Haomiao Yu |
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| Rok vydání: | 2022 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science Applied Mathematics 020208 electrical & electronic engineering Stability (learning theory) Terminal sliding mode Process (computing) 02 engineering and technology Computer Science Applications 020901 industrial engineering & automation Transformation (function) Control and Systems Engineering Control theory Convergence (routing) 0202 electrical engineering electronic engineering information engineering State (computer science) Electrical and Electronic Engineering Instrumentation Time base generator |
| Zdroj: | ISA Transactions. 125:166-178 |
| ISSN: | 0019-0578 |
| Popis: | This paper focuses on fixed-time formation control (FTFC) of a fully-actuated surface vessel (FASV) considering complex unknowns, including fully unknown dynamics and disturbances, input saturation and time-varying delays. First, using prediction idea to address time delay, a novel state predictor (SP) strategy combining with state transformation (ST) technique is devised for each FASV to predict the evolution of system states such that fixed-time stability can be ensured while solving the delay problem. Besides, the uncertainties in the transformed system are attentively considered. In addition, aiming to distinctly identify complex unknowns, predictor-based neural network is injected into the foregoing delay processing method. Finally, using time base generator (TBG), a new adaptive terminal sliding mode (ATSM) is incorporated into FTFC strategy which in turn contributes to decreasing control inputs and acquiring smooth convergence process. Simulation results and comparisons are thoroughly provided to testify the effectiveness and superiority of the designed FTFC scheme. |
| Databáze: | OpenAIRE |
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