Sliding Mode Learning Control of Uncertain Nonlinear Systems with Lyapunov Stability Analysis
Autor: | Erkan Kayacan |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
FOS: Computer and information sciences
Lyapunov stability 0209 industrial biotechnology Computer Science - Machine Learning Adaptive control Computer science 020208 electrical & electronic engineering Stability (learning theory) Mode (statistics) Systems and Control (eess.SY) 02 engineering and technology Electrical Engineering and Systems Science - Systems and Control Machine Learning (cs.LG) Term (time) Nonlinear system 020901 industrial engineering & automation Control theory FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Robust control Instrumentation |
Popis: | This paper addresses the Sliding Mode Learning Control (SMLC) of uncertain nonlinear systems with Lyapunov stability analysis. In the control scheme, a conventional control term is used to provide the system stability in compact space while a type-2 neuro-fuzzy controller (T2NFC) learns system behaviour so that the T2NFC completely takes over overall control of the system in a very short time period. The stability of the sliding mode learning algorithm has been proven in the literature; however, it is restrictive for systems without overall system stability. To address this shortcoming, a novel control structure with a novel sliding surface is proposed in this paper, and the stability of the overall system is proven for nth-order uncertain nonlinear systems. To investigate the capability and effectiveness of the proposed learning and control algorithms, the simulation studies have been carried out under noisy conditions. The simulation results confirm that the developed SMLC algorithm can learn the system behaviour in the absence of any mathematical model knowledge and exhibit robust control performance against external disturbances. |
Databáze: | OpenAIRE |
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