Abstract
This paper presents a robust continuous control set model predictive control (CCS-MPC) method to control the output voltage of a three-phase inverter in uninterruptible power supplies (UPS). A robust disturbance observer (DOB) is proposed to estimate the load current of the three-phase UPS without a steady-state error, taking the effect of model uncertainties into account. A CCS-MPC is designed using the DOB for reference voltage tracking purpose, and input constraints are considered in the controller design to calculate the optimal control input. Model uncertainties are defined using polytopic uncertainty class, and a linear matrix inequality (LMI) optimization method is used to compute the optimal observer gain matrix. Another robust controller (RC) is designed based on the DOB and compared with CCS-MPC. The effectiveness of the proposed method (the DOB based CCS-MPC) is evaluated for resistive, inductive, and nonlinear loads then compared with other control methods using a three-phase 5-KVA laboratory experiment UPS system.
Highlights
The main control objective for an uninterruptible power supply (UPS) is to regulate the output voltage in presence of parameter uncertainties and disturbances
The deadbeat control method combined with state and disturbance observers, was successfully applied to a single-phase uninterruptible power supplies (UPS) inverter to compensate for the model uncertainties and load current disturbances [1]
The comparative results are given in terms of the output voltage total harmonic distortion (THD) and transient response using the three different control methods (i.e., model predictive control (MPC)-disturbance observer (DOB), robust controller (RC)-DOB, and RC-LOB) in order to evaluate the performance of the proposed method
Summary
The main control objective for an uninterruptible power supply (UPS) is to regulate the output voltage in presence of parameter uncertainties and disturbances. The effect of the disturbances and uncertainties should be decreased to obtain a good voltage tracking performance [1] Control methods, such as deadbeat control [1,2], repetitive control [3,4], robust control [5,6,7,8], and model predictive control [9,10,11,12,13,14,15], have been used in conjunction with different observers to solve the aforementioned problems. A parameter estimation method with a deadbeat controller [2] can compensate for the white noise caused by sensed variables This method requires a high sampling frequency, and is vulnerable to plant-model mismatch. The repetitive control method can provide satisfactory performances in the case of periodic errors if the internal model of the system is well defined [3,4]
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