Adaptive PSD Neural Control
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For servo motor speed control, the adaptive PSD neural control method demonstrates enhanced robustness compared to single-neuron control approaches. This technique combines the adaptive capabilities of neural networks with the advantages of PSD (Proportional-Sum-Differential) control algorithms, enabling adaptation to varying operating conditions and environmental changes—thereby improving control accuracy and reliability in servo motor applications. The method typically implements online learning through weight adaptation mechanisms, where neuron connection weights are dynamically adjusted based on error feedback using algorithms like gradient descent or reinforcement learning. Additionally, systematic training of the neural components through supervised learning or adaptive tuning techniques further enhances the system's adaptability and robustness, leading to improved overall control system performance.
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