In order to improve the control precision, this paper proposes a control method based on RBF neural network and. Firstly discrete models is identification by ...
Many studies have shown that using RBF neural network to control the three parameters of the PID controller adaptively, and its overshoots were less than BP ...
To improve the adaptive capability of the NPID controller, the RBF-NPID control algorithm is proposed. The learning ability of RBF neural network is used to ...
The results show that the proposed control method has faster response time, higher control precision compared with the traditional PID control methods; ...
Bibliographic details on The study and simulation of PID control based on RBF neural network.
It is also revealed from simulation results that the proposed control algorithm is valid for DC motor and also provides the theoretical and experimental basis.
In this paper, an adaptive PID controller of active suspension systems based on RBF neural network (RBF-NN) is developed. A quarter-car suspension system with ...
Compared with traditional PID control, the simulation results show RBF- PID control has better dynamic performance and stabilization. ResearchGate Logo.
The simulation results shows that the PID controller designed based on the RBF neural networks has good control performance on the steam generator level control ...
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In this section, using the PID control principle based on RBF neural network makes simulation for DC motor in MATLAB. Parameters of the system for simulation ...