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For uncertainty nonlinear system, a dynamic neural sliding mode (SM) controller is designed in this paper. A union upper bound of uncertainty is constructed. The union upper bound is from the uncertainty and disturbance of the system, and it is unknown. The designed dynamic neural SM controller can ensure the system asymptotic stability. Using Radial Basis Function Neural Networks (RBFNN) to learn adaptively the union upper boundary of the uncertainty and verifying its validity by theoretical analysis and simulation examples. The design scheme of the adaptive learning upper bound reduces the condition of theoretical analysis of SMC, effectively suppresses the chattering. |
