Research on Model Predictive Control with Dual Feedback Based on State Extension

Abstract

Abstract: Depending on the online implementation demand of Model Predictive Control (MPC) for the receding horizon control characteristic, a control strategy of dual feedback structure based on state extension was proposed. The control strategy was aimed to improve the online computational capability about the control system to achieve the control target without any change of the system information. Based on the analysis of the characteristic of MPC, the extension and conversion method of state variable was proposed according to the relationship between state variable, control variable, output variable and system model. And the structure of the control system after state extension was manifested as a dual feedback style. Thus, the control horizon of MPC system was decreased because of the introduction of the output feedback without any constraint and treatment to the system information. The decrease of the control horizon will conducive to improve the online computational capability of the control system remarkably. The feasibility and validity of the proposed method was verified by an example of maximum power point tracking (MPPT) control of photovoltaic system.

Key words: model predictive control, state extension, dual feedback, receding horizon

CLC Number:

TP273+.1

Zho Xilin, Yin Lijuan. Research on Model Predictive Control with Dual Feedback Based on State Extension[J]. Journal of System Simulation, 2015, 27(3): 591-597.

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