Skip to Main Content
Giant Magnetostrictive Material (GMM) has inherent hysteretic nonlinearity, and its hysteretic performance changes with input frequency. Hence, it is difficult for a normal controller to control its actuator precisely. Due to this, a hysteretic compensation control strategy was proposed. Adopting neural network model reference, combine the dynamic model of Giant Magnetostrictive Actuator (GMA) as reference model, with BP neural network. Introducing error feed-back learning scheme - BP into controller and identifier, controller can identify GMA and identifier control it precisely. To accelerate the convergence of the trace error, train the neural network offline.