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This paper proposes an adaptive algorithm of neural nets with a special perturbation for a real time velocity control system of a VVVF(Variable Voltage Variable Frequency) hydraulic elevator. The weight vector of the neural network is adaptively adjusted by the LMS (Least Mean Square) with perturbation, so it is not necessary to know the nonlinear continuous function of the control system. The nonlinear velocity control system is considered as the controller output function in an adaptive controller model. The experimental results obtained from the VVVF hydraulic elevator showed that the neural nets controller using the perturbation algorithm proposed are much stabler and faster in dynamic response compared with the conventional PID (Proportion Integration Derivation) controller.
This paper proposes an adaptive algorithm of neural nets with a special perturbation for a real time velocity control system of a VVVF (Variable Voltage Variable Frequency) hydraulic elevator. The weight vector of the neural network is adaptively adjusted by the LMS (Least Mean Square) with perturbation, so it is not necessary to know the nonlinear continuous function of the control system. The experimental results obtained from the VVVF hydraulic elevator showed that the neural nets controller using the perturbation algorithm proposed are much stabler and faster in dynamic response compared with the conventional PID (Proportion Integration Derivation) controller.