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The servo-motor possesses a strongly nonlinear property due to the effect of the stimulating input voltage,load-torque and environmental operating conditions. So it is rather difficult to derive a traditional mathematical model which is capable of expressing both its dynamics and steady-state characteristics. A neural network-based adaptive control strategy is proposed in this paper. In this method,two neural networks have been adopted for system identification (NNI) and control (NNC),respectively. Then,the commonly-used specialized learning has been modified,by taking the NNI output as the approximation output of the servo-motor during the weights training to get sensitivity information. Moreover,the rule for choosing the learning rate is given on the basis of the analysis of Lyapunov stability. Finally,an example of applying the proposed control strategy on a servo-motor is presented to show its effectiveness.
The servo-motor possesses a strongly nonlinear property due to the effect of the stimulating input voltage, load-torque and environmental operating conditions. So it is rather difficult to derive a traditional mathematical model which is capable of expressing both its dynamics and steady-state characteristics of A neural network-based adaptive control strategy is proposed in this paper. In this method, two neural networks have been adopted for system identification (NNI) and control (NNC), respectively. Then, the commonly-used specialized learning has been modified, by taking the NNI output as the approximation output of the servo-motor during the weights training to get sensitivity information. Finally, an example for choosing the learning rate is given on the basis of the analysis of Lyapunov stability. of applying the proposed control strategy on a servo-motor is presented to its its effectiveness.