Predictive control method based on nuclear norm subspace method and augmented vector method

Abstract

The invention discloses a predictive control method based on a nuclear norm subspace method and an augmented vector method. The method comprises on the basis of MPC, using an ADMM algorithm to construct a CD player mechanical arm system model, and solving a nuclear norm optimization problem by collecting the historical input and output data of the ADMM algorithm to the system model so as to obtaina group of optimized output values; setting a measurement input value, and constructing the state space model of a system by the measurement input value and an output value by means of a subspace method algorithm technique; establishing a Hankel matrix based on the measurement input value and the output value, and constructing a discrete state space model of the system based on the augmented vector method; calculating the multi-step predicted output value of the discrete state space model by a multi-step iterative method; constructing the cost function of the system and setting the referencetrajectory of the system; calculating the optimal control amount of the system, and obtaining the output value of a controller in the system. The system model avoids a large demand for actually measured data and shortens calculation time.

Description

technical field [0001] The invention belongs to the field of predictive control, in particular to a predictive control method based on a nuclear norm subspace method and an augmented vector method. Background technique [0002] The state-space theory has achieved brilliant results in the fields of aerospace and aviation, but when this design method is applied to some complex fields, such as industry, it will be found that there is still a considerable gap between the perfect theory and the practice of control systems. In the prior art, model predictive control (Model Predictive Control, MPC) is usually applied to industry. MPC is a model-based control algorithm, which only focuses on the function of the model. As long as the information set has the predictive function, no matter what form it has , can be used as a predictive model. The rolling optimization of MPC generally determines the future control role through the optimum of the set performance index. This performance...

AI Technical Summary

Problems solved by technology

Introduced unequal prediction length and control length, the system design is flexible and convenient, has the characteristics of prediction model, rolling optimization and online feedback correction, and has good control performance and robustness; but in the case of less measurement data, through MPC cannot get a suitable system model, which leads to a long overall calculation time, making the system unable to track the operation of the set parameter values

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