Multivariable nonlinear system adaptive equalization multi-model decomposition and control method

A nonlinear system and self-adaptive equalization technology, applied in self-adaptive control, comprehensive factory control, general control system, etc., can solve problems such as system decomposition or controller design, complex scheduling, and complex system, so as to improve decomposition efficiency and quality, simplify the controller structure, and improve the effect of closed-loop performance

Active Publication Date: 2020-09-22
HOHAI UNIV CHANGZHOU
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Problems solved by technology

When the system is a multivariable system, each variable is coupled with each other, which will make the system much more complicated
Whether it is the selection of scheduling variables, system decomposition, controller design, scheduling, etc., it is much more complicated than a single-input single-output system.

Method used

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  • Multivariable nonlinear system adaptive equalization multi-model decomposition and control method
  • Multivariable nonlinear system adaptive equalization multi-model decomposition and control method
  • Multivariable nonlinear system adaptive equalization multi-model decomposition and control method

Examples

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Embodiment 1

[0030] Consider the multivariable nonlinear system as an inverted conical tank system, such as figure 1 As shown, two of the input flows are F i and F j , the initial temperature is T i = 350K. The flow rate of the coolant is q c temperature is T ci , output temperature T co Between 289 and 313K. The dynamic equation of the system is as follows:

[0031]

[0032] where R and H are the radius and height of the cone, respectively. Each parameter is R=0.798m, H=1m, κ=50m 5 / 2 min -1 , Fj=10cm 3 min -1 ,T ci =289K,T co = 313K. The control objective of the system is to operate F i and q c So that the liquid level h and temperature T can meet the control requirements. Obviously, it can be seen from formula (1) that the nonlinearity of the system is very strong, and a single linear controller cannot meet the requirements.

[0033] Adopt the self-adaptive balanced multi-model decomposition algorithm of the multivariable system proposed by the present invention, s...

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Abstract

The invention discloses a multivariable nonlinear system adaptive equalization multi-model decomposition and control method. Aiming at the complexity of a non-linear multivariable system, the method comprises the following steps: firstly, gridding the multivariable non-linear system by utilizing a gridding algorithm based on gap measurement; secondly, decomposing the multivariable nonlinear systemby utilizing a multi-model decomposition algorithm based on clearance measurement; and based on each obtained sub-model, designing a prediction controller by using a prediction control algorithm, andfinally performing weighted synthesis by using a trapezoidal weighting mode to perform global control on the system so as to obtain the adaptive equalization multi-model decomposition and multi-modelprediction controller of the multivariable nonlinear system. According to the invention, the steps of decomposing and controlling the system are greatly simplified, and the efficiency is improved.

Description

technical field [0001] The invention discloses a self-adaptive equilibrium multi-model decomposition and control method of a multi-variable nonlinear system. Background technique [0002] All process control systems in reality are nonlinear, and when the system works near the operating point, a linear controller can suffice. However, for a process control nonlinear system with a relatively wide operating range, a linear controller cannot meet the requirements. Especially for multi-variable nonlinear process control systems, the control difficulty is even greater. The multi-model control method based on the decomposition-synthesis principle can effectively decompose complex nonlinear control problems into a combination of several simple linear control problems; solve the nonlinear control problems by solving these linear control problems. The multi-model control method is widely used in the field of nonlinear control because of its simplification. Since the predictive cont...

Claims

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042Y02P90/02
Inventor 杜静静陈俊风李建姜学平
Owner HOHAI UNIV CHANGZHOU
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