Model predictive control based on Lyapunov function and near state vector selection of four-leg inverter / Abdul Mannan Dadu
Due to the evolution of high processing microprocessors, the model predictive control (MPC) has been widely used in power electronic applications. The model predictive control technique utilizes all the available voltage vectors of power inverter to improve the predictive current control performance...
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| Format: | Thesis |
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2018
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| Online Access: | http://studentsrepo.um.edu.my/9119/1/Abdul_Mannan_Dadu.bmp http://studentsrepo.um.edu.my/9119/11/mannan.pdf http://studentsrepo.um.edu.my/9119/ |
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| Summary: | Due to the evolution of high processing microprocessors, the model predictive control (MPC) has been widely used in power electronic applications. The model predictive control technique utilizes all the available voltage vectors of power inverter to improve the predictive current control performance. In spite of simplicity, flexibility and fast dynamic response, the conventional model predictive control (C-MPC) has a drawback of computational burden. The computational burden of C-MPC is expensive due to utilize all available voltage vectors of a power inverter to predict the future behavior of the system. This dissertation has focused on Lyapunov model predictive control (L-MPC) methods, in which Lyapunov control law is employed in the cost function to minimize the error between the desired control variables and the actual control variables of a three-phase four-leg inverter to optimize closed-loop system performance. The proposed control algorithm takes advantage of a predefined Lyapunov control law which minimizes the required calculation time by the Lyapunov model equations just once in each control loop to predict future variables. In this dissertation, a near state vector selection-based model predictive control (NSV-MPC) scheme is also proposed to mitigate the common-mode voltage (CMV) with reduced computational burden. The proposed control technique adopts 6 active voltage vectors in the discrete predictive model among 14 available active vectors based on the position of the future reference vector. The position of reference currents is used to detect the voltage vectors surrounding the reference voltage vector in every sampling period. At last, the influencing factor of CMV is revealed based on switching state combination and then the CMV weighting factor is introduced in the cost function to make balance in the ripple content of load currents and the mitigation of CMV. The switching state pattern is selected according to peak to peak value of CMV and CMV weighting factor is related to peak value of CMV and a user defined co-efficient. The stability of the system is ensured through Lyapunov function with the help of backsteping control method. L-MPC technique improves the digital speed by 23.8% compared to C-MPC and it reduces current tracking error confined within 0.65A and THD in the variation of inverter control parameters of a three-phase four-leg inverter. The CMV can be bounded within one-fourth of the dc-link voltage of a three-phase four-leg inverter using the proposed NSV-MPC technique. MATLAB/Simulink software environment is used for the simulation and the LabVIEW Field programmable gate array (FPGA) rapid prototyping controller is used to validate the proposed control scheme. The results showed that the proposed control techniques had better performance as compared to the C-MPC. |
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