Improved PID optimal control algorithm based on RBFNN and BAS

A control algorithm and optimization technology, applied to controllers with specific characteristics, electric controllers, etc., can solve problems such as increasing costs, increasing hardware complexity, and unfavorable promotion and application

Active Publication Date: 2020-06-16
SOUTHEAST UNIV
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Problems solved by technology

[0004] Improving the real-time control ability of RBFNN on the global error can also be improved on the hardware, such as computers using high-performance CPUs and GPUs. This solution can improve the update iteration speed of the algorithm to a certain extent, but increases the complexity of the hardware. , which increases the cost, which is not conducive to the promotion and application in practice

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  • Improved PID optimal control algorithm based on RBFNN and BAS
  • Improved PID optimal control algorithm based on RBFNN and BAS
  • Improved PID optimal control algorithm based on RBFNN and BAS

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Embodiment Construction

[0073] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0074] Such as figure 1 As shown, the present invention discloses an improved PID optimization control algorithm based on RBFNN and BAS, comprising the following steps:

[0075] Step 1), use the RBF neural network to identify the system and initially adjust the PID parameters, first design its performance evaluation function E(k), and its process expression:

[0076]

[0077] Among them, k is the sampling time, e(k) represents the theoretical output value y at time k c (k) and measured output value y r (k) difference, such as figure 2 shown.

[0078] Step 2), use the traditional RBF neural network to identify the system, use the gradient descent method to upda...

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Abstract

The invention discloses an improved PID optimal control algorithm based on an RBFNN and a BAS. Firstly, aiming at a real-time control system with undefined parameters, an RBFNN model is designed, andonline system parameter identification is carried out; secondly, according to the control requirement of a real-time system, an RBF-PID model based on a local error is designed; rough setting is carried out on PID parameters to obtain a PID parameter range suitable for the system, a BAS-PID model based on ITAE optimization indexes is designed on the basis, and optimal adjustment is carried out onthe PID parameters by using the BAS-PID model to obtain optimal PID parameters. The real-time system with unknown parameters is controlled through the method, and the control effect is better than that of a self-adaptive RBFNN method, a traditional PID method and the like.

Description

technical field [0001] The invention belongs to the field of system automatic control, and in particular relates to an improved PID (proportional-integral-differential) optimal control algorithm based on RBFNN (radial basis neural network) and BAS (beetle whisker search method). Background technique [0002] In industrial process control, the PID controller controlled according to the proportion (P), integral (I) and differential (D) of the deviation is the most widely used automatic controller, which is simple, stable and easy to implement. However, with the increasing complexity of the system and the continuous change of system parameters and working environment, the traditional PID controller is difficult to meet the precise control requirements. Therefore, it is necessary to design a new controller to meet the complex control requirements. [0003] In recent years, some complex algorithms have been applied to process control, such as sliding mode control, fuzzy control,...

Claims

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Application Information

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IPC IPC(8): G05B11/42
CPCG05B11/42
Inventor 陈熙源刘建国
Owner SOUTHEAST UNIV
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