Data Driven Pre Tuning Adaptive Subspace Model Predictive Control

doi:10.16182/j.issn1004731x.joss.201801044

Abstract

Abstract: The problem of predictive control is investigated for power plant superheated steam temperature system with the characteristics of large delay, large inertia and time-varying. The data driven pre tuning adaptive subspace model predictive control (PTA-MPC) method, which combines the advantages of subspace identification and state space predictive control, is proposed. The state space models of multiple conditions are obtained by subspace identification with the input signal in persistent excitation. The predictive control law is derived with the state space models, and the controller parameters are optimized by using particle swarm optimization (PSO) algorithm. Based on the least square parameter estimation, state space model parameters and predictive controller parameters are smoothed. The steps of PTA-MPC algorithm in cascade control system are presented. A simulation example illustrates the effectiveness of this method.

Key words: subspace identification, state space model, predictive control, pre tuning adaptive

CLC Number:

TP391.9

Han Pu, Liu Miao, Jia Hao. Data Driven Pre Tuning Adaptive Subspace Model Predictive Control[J]. Journal of System Simulation, 2018, 30(1): 332-340.

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