finite control set model predictive control (fcs mpc)

In this paper, two predictive control methods for dual output multilevel rectifier are compared. The investigated structure is a seven-level PUC rectifier, which was selected based on high reliability and low cost. Increasing the number of input voltage levels helps to reduce the amount of harmonics and consequently reduce the size of power filters. On the other hand, current conduction in this converter is performed continuously and the problems of current discontinuity such as complexity in analysis and the requirement to use large induction filters on the DC side are solved. First, the design is accomplished based on the FCS-MPC method and two different output voltages with ratios of 1 and 3 are obtained. Also, the control of output DC voltages and unity input power factor is well provided. Then, to achieve better stability, the MPC method based on Lyapunov theory has been utilized. In this method, the target variables are defined in the Lyapunov function and the cost function is derived from the same Lyapunov function. The advantages of this approach compared to the conventional MPC method are no need for gain adjustment, easier implementation and fewer sensors (the load current is estimated using the PUC7 rectifier mathematical model). The simulation of both FCS-MPC and predictive control based on Lyapunov method is carried out using Matlab/Simulink and the results of both methods are presented and compared with each other. Finally, it can be seen that in the Lyapunov-based method, the tracking of the reference current is smoother and with less fluctuations, and the seven-level rectifier voltage also has a more regular and sinusoidal waveform.

Brushless dc motors (BLDC) are widely used in industrial applications due to their simple structure, high efficiency and long lifetime. The drive of these motors also has a fast transient response and has high quality waveforms in steady state. In this paper, the direct power control using the model predictive method with finite control set (DP-FCS-MPC) is presented in BLDC motor drive and compared with the conventional current control method based on FCS-MPC. This comparison is made under the same operating conditions and includes the steady state operation of the BLDC motor. The simulations performed in PLECS software show the performance of both methods in BLDC motor speed control under sudden load changes. Nevertheless, it is shown that the direct power control using model predictive method with finite control set has better performance in terms of torque ripple reduction, less speed and torque fluctuations, less active and reactive power ripple, and current waveforms with less harmonic distortions.

this paper presents finite control set-model predictive control (FCS-MPC) in the low and high speed range for the control of brushless DC (BLDC) motor. In the proposed control method, by removing the mechanical sensor and using the sliding mode observer (SMO), the electromotive force (BACK-EMF) as well as the speed and position of the rotor are accurately estimated. Then through fcs-mpc to improve speed control performance and reduce chattering, by minimizes a cost function, predicts the future behavior of the  system variables. The method is simulated with MATLAB software and the output speed and back-emf obtained from the control model control method with a limited control set are compared with the slip model observer method. The simulation results show that the control method used improves the speed convergence accuracy and reduces the steady-state error and the steady-state fluctuations.

In this paper, two schemes of model predictive control (MPC) method, named finite control set model predictive control (FCS-MPC) and dead-beat model predictive control (DB-MPC) as a continuous control set model predictive control (CCS-MPC) are applied and compared to control the current of a permanent magnet synchronous machine in energy recovery mode for the use of electric vehicles. The FCS-MPC strategy selects the optimal voltage vector and applies the control pulses directly to the inverter without using any modulators. In other side, DB-MPC is implemented through space vector pulse width modulation (SVPWM). The performance and results of both types of control strategies are extracted and compared using MATLAB Simulink software. The comparisons are made mainly in steady state and transient modes. Both control strategies are applied to a permanent magnet synchronous machine with the same parameters and with the same operating mode. The results show that the current steady state fluctuation is further reduced in the DB-MPC strategy and the transient state response is faster in the FCS-MPC strategy.