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Abstract

An improved active vibration control system using feedback and pseudo-feedforward sensor inputs is provided for solving the problem of random and repetitive active vibration control and noise cancellation in a system. In a first embodiment of the invention, an artificial neural network is used for learning the dynamics of a structure and for providing output signals that follow the state variables of the structure. In one implementation of the neural network, a plurality of neurons obtain biasing inputs derived from sensor inputs, as well as inputs from the other neurons in the network. Further, each neuron obtains a feedback input from itself. Each input to a neuron is weighted using a weighting function derived on-line. The neural network supplies structure parameters and state variables to an optimal controller which derives and provides a control signal to the actuators so as to counteract vibrations and/or noise sensed in the system. In a second embodiment an optimal controller utilizing a modified generalized predictive control algorithm is used to to consider the limitations on the physical characteristics of the actuator(s), on-line, in terms of the output level and the rate of change of the output in the system. Additional embodiments wherein an optimized control signal is sent to the actuator(s) to minimize vibration incident to the structure are provided.

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First Claim