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Adaptive PID controller based on back propagation(BP) neural network has many merits like that simple algorithm of PID controller and self-study and adaptive functions of neural network. According the requirements of system output performance, the BP neural network can autoadjust its weights to vary kp, kj and kd. The simulation adjust its weights to vary k results of an electro-hydraulic position...
It is always primary object to improve gauge control accuracy in hot strip mills. Limitation based on conventional PID emerges when the controlled object is strong nonlinear, uncertain and multi-constrained features. BP-PID control strategy is introduced in MN-AGC for realizing finished combination of controller parameters. The practical application results have shown the effectiveness of this algorithm...
This paper discussed the neural network in the application of nonlinear system adaptive control. For a typical class of nonlinear system, A BP neural network is designed to control the system based on hyperstability. The nonlinear link was eliminated by introducing its inverse model, for transforming the nonlinearity into a linear system. The neural network controllerpsilas purpose is to optimize...
Considered influence of nonlinear system and disturbance, a new adaptive fault-tolerant control method based on neural network model-following adaptive inversion control is introduced for flight control system in the presence of control surface damage. To restrain modeling uncertainties caused by fault system, neural network PID and inversion controllers are design for fault-tolerant flight control...
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