Uncertain system model predictive control parameter setting method based on machine learning

Abstract

The invention discloses an uncertain system model predictive control parameter setting method based on machine learning. The method comprises the steps of 1) obtaining an m-dimensional output weight matrix Q and an n-dimensional input weight matrix R in a cost function of system model predictive control; (2) obtaining an output sequence and mn*L3 groups of performance indexes, wherein each group of performance indexes is a group of column vectors constructed by output overshoot and adjustment time, worst overshoot and worst adjustment time are respectively solved for each group Q and R, and then the solved worst overshoot and worst adjustment time are stored in a matrix F; 3) constructing an RBF neural network, and calculating an optimal performance index by using the established RBF neural network; 4) constructing a BP neural network, and solving a performance label by using the BP neural network; and 5) taking the performance label as an optimization basis, and adopting a PSO optimization algorithm to adjust the predictive control parameters of the uncertain system model, so that the method can more accurately set the predictive control parameters of the uncertain system model.

Description

technical field [0001] The invention relates to a parameter tuning method for predictive control parameters of an uncertain system model, in particular to a parameter tuning method for predictive control parameters of an uncertain system model based on machine learning. Background technique [0002] Model predictive control (Model Predictive Control, MPC) is an advanced control strategy, and its mechanism can be described as: at each sampling moment, according to the current measurement information obtained, a finite time-domain open-loop optimization problem is solved online, and the obtained The first element of the control sequence acts on the plant. At the next sampling instant, the above process is repeated: the optimization problem is refreshed with new measurements and re-solved. [0003] Predictive control is quite different from algorithms such as traditional PID control and optimal control. In particular, it has many controller parameters, which need to be selecte...

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Problems solved by technology

[0004] In addition, the controlled object model in industrial applications is generally obtained by identifying the input and output data obtained during the actual operation of the system. Due to the time-varying characteristics of the system working conditions and the existence of interference such as measurement noise, the obtained model is different from the actual controlled object. There is generally an error between them, that is, there is model uncertainty, which requires the control system to have good robust performance

The parameter tuning of an uncertain system introduces additional complexity, and often can only be tuned based on expert experience about the system, which greatly increases the cost of tuning

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