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Adaptive control design for MIMO switched nonlinear systems with full state constraints

Tipo de material: TextoTextoSeries ; International Journal of Adaptive Control and Signal Processing, 33(10), p.1583-1600, 2019Trabajos contenidos:
  • Chen, A
  • Liu, L
  • Liu, Y. J
Tema(s): Recursos en línea: Resumen: This paper presents an adaptive fuzzy control approach of multiple?input-multiple?output (MIMO)switched uncertain systems, which involve time?varying full state constraints (TFSCs)and unknown disturbances. In the design procedure, the fuzzy logic systems are adopted to approximate the unknown functions in the systems. The adaptive fuzzy controller is set up by backstepping technique. According to the tangent barrier Lyapunov function (BLF?Tan), a novel adaptive MIMO switched nonlinear control algorithm is designed. Under the rule of arbitrary switchings and the proposed control laws, it is demonstrated that all signals in the resulted system are semiglobally uniformly ultimately bounded (SGUUB)and the tracking error converges to a small neighborhood of zero with TFSCs. Furthermore, the simulation example validates the effectiveness of presented control strategy
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This paper presents an adaptive fuzzy control approach of multiple?input-multiple?output (MIMO)switched uncertain systems, which involve time?varying full state constraints (TFSCs)and unknown disturbances. In the design procedure, the fuzzy logic systems are adopted to approximate the unknown functions in the systems. The adaptive fuzzy controller is set up by backstepping technique. According to the tangent barrier Lyapunov function (BLF?Tan), a novel adaptive MIMO switched nonlinear control algorithm is designed. Under the rule of arbitrary switchings and the proposed control laws, it is demonstrated that all signals in the resulted system are semiglobally uniformly ultimately bounded (SGUUB)and the tracking error converges to a small neighborhood of zero with TFSCs. Furthermore, the simulation example validates the effectiveness of presented control strategy

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