Neural network PID decoupling control based on chaos particle swarm optimization
| Autor: | Haipeng Pan, Wei-feng Teng, Jia Ren |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Pid neural network
Nonlinear system Quantitative Biology::Neurons and Cognition Artificial neural network Computer science Control theory Computer Science::Neural and Evolutionary Computation MathematicsofComputing_NUMERICALANALYSIS Particle swarm optimization PID controller Multi-swarm optimization Backpropagation Decoupling (electronics) |
| Zdroj: | Proceedings of the 33rd Chinese Control Conference. |
| DOI: | 10.1109/chicc.2014.6895792 |
| Popis: | As a new kind of neural network model, Neural network PID (PIDNN) combines the advantages of PID and neural network. However, the error back propagation algorithm (BP) limits the performance of PIDNN. In order to realize effective control of nonlinear, large delay and strong coupling system, this paper proposes a neural network PID control method based on chaos particle swarm optimization. Using chaos particle swarm algorithm to replace the reverse pass algorithm of original PID neural network, adjusting the weights of PIDNN between each neuron, the algorithm achieved rapid decoupling control effect. The simulation results show that the proposed method in this paper, compared with the original BP algorithm, has more excellent dynamic and steady-state performance. |
| Databáze: | OpenAIRE |
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