This invention discloses a weighing adaptive
vibration control system and method, belonging to the field of weighing technology. The invention collects vibrator state information, weighing sensor output signals, sliding mode surface values, errors, and their derivatives. Then, based on the
system dynamics model and control objectives, the sliding mode surface form and control law are determined. Simultaneously, a neural network
multilayer perceptron structure is employed. The input layer receives the sliding mode surface values, errors, and their derivative signals; the
hidden layer processes the input information through
nonlinear transformation and
feature extraction; and the output layer outputs the switching
gain and sliding mode surface coefficients. Finally, the neural network is trained using an
online learning algorithm. During
system operation, based on the output error of the weighing sensor, the neural network weights and biases are adjusted using an optimization
algorithm to continuously optimize the sliding mode controller parameters. The sliding mode controller generates precise control signals based on the optimized parameters to accurately control the vibration of the vibrator, achieving efficient and stable operation of the weighing process under vibration interference and improving weighing performance.