Model prediction control parameter analysis and optimization method and system for multivariate fractional order system

Abstract

The invention discloses a model prediction control parameter analysis and optimization method and system for a multivariate fractional order system. The method comprises the following steps: first, collecting input data and output data of a controlled system, performing discretization to obtain a multivariate first-order plus fractional-order lag model of the controlled system through a system identification method and transforming the model into a state space equation form; secondly, predicting the future output of the multivariate first-order plus fractional-order lag model through the statespace equation, and obtaining an analytical expression of the control signal of a model prediction controller; and finally, deriving to obtain a closed-loop transfer function of the controlled systemand performing decoupling and analysis to transform the optimization problem into a pole placement problem so as to realize analysis and optimization of the model prediction control parameter. The method of the invention can achieve the purpose of improvement of the control system accuracy during control, fast tracking and interference resistance; and meanwhile, the online calculated amount and the storage content of the prediction control parameter optimization can be reduced.

Description

technical field [0001] The invention belongs to the technical field of model predictive control, and in particular relates to a model predictive control parameter analysis and optimization method and system for multivariable fractional-order systems. Background technique [0002] Model predictive control has become the main application control in the field of process control because of its low requirements on the model, strong robustness, strong anti-interference, and the ability to analytically deal with the control constraints of the system within the framework of optimal control theory. One of the methods. [0003] Predictive control is quite different from algorithms such as traditional PID control and optimal control. In particular, it has many controller parameters that need to be selected in combination with actual control requirements. As the controlled system becomes more and more complex, there are often elements such as model uncertainty, input-output disturban...

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Problems solved by technology

In addition, for some systems with fractional-order lags, such as some wastewater treatment systems, HVAC systems, etc., since when the lag time of the system is large relative to the time constant, approximating the fractional-order lag to an integer lag will make the system closed loop Performance deterioration seriously affects system performance and quality, so it is necessary to study parameter tuning methods specifically for fractional-order lag systems

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