PSORBFD fast adaptive decoupling control method
A technology of decoupling control and decoupling controller, applied in the field of decoupling of coupled systems, can solve problems such as no dynamic characteristics, inability to completely eliminate coupling effects, inability to effectively improve decoupling adjustment capabilities of closed-loop control systems, etc.
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Embodiment 1
[0106] like Image 6 As shown, a PSORBFD fast adaptive decoupling control method described in this embodiment is specifically a PSORBFD adaptive decoupling control method applied to a BOPP film thickness control system, but the decoupling control method is not limited to The BOPP film thickness control system can also be applied to the decoupling control of other multi-channel coupling systems. The PSORBFD fast adaptive decoupling control method described in this embodiment includes the following steps:
[0107] Step S1, system modeling: according to the relationship between the control quantity and the output quantity, determine the transfer function matrix model of the system.
[0108] (1) System modeling
[0109] Considering the three-channel film thickness control system, the thickness of each channel is taken as the measured value, and the temperature of the heating bolt is used as the control quantity to form a three-input and three-output control system. The system con...
Embodiment 2
[0228] Embodiment 2, experimental comparative analysis
[0229] (1) System experiment environment
[0230] In order to verify the effect of the PSORBFD decoupling controller described in Embodiment 1 above, in this embodiment, the following four controllers are used for the film thickness control model to conduct experimental simulations under the MATLAB environment.
[0231] PID: PID controller;
[0232] SFFD: Feedforward-like decoupling controller;
[0233] RBFD: RBF neural network decoupling controller;
[0234] PSORBFD: PSO-Based RBF Decoupling Controller.
[0235] The controlled object model in the experiment of this embodiment is the second-order discrete transfer function model described in formulas (2), (3) and (4). Based on the model setting v 1 =v 2 =v 3 =3, when the system is stable, select the following data at time=401:1400 as training samples, such as Figure 7 shown.
[0236] Set the initial clustering number of RBFD to 20 groups, and the clustering res...
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