Optimal Finite Control Set Model Predictive Control Strategies for Induction Motor Drives
The finite control set model predictive control (FCS-MPC) for motor drives has been vigorously investigated during the past decade for control of current, torque, stator flux linkage, and other performances in a motor drive system. Model predictive torque control (MPTC) and flux control (MPFC) are two popular categories of FCS-MPC for motor drives. In FCS-MPTC, a finite number of possible voltage vectors are evaluated by a cost function in an iterative prediction loop. The cost function includes several control objectives, such as minimization of torque and flux errors, the neutral-point voltage of multi-level inverters and inverter switching frequency. The control algorithm determines the optimal voltage vector that minimizes the pre-defined cost function. Each variable included in the cost function has a weighting factor according to different magnitudes and units. Weighting factor tuning is a non-trivial and complicated task, particularly when the control algorithm has two control-objectives which are prime variables i.e., torque and flux. In such a case, the control algorithm may choose a global optimal solution for torque which will be a sub-optimal solution for flux. In general, a conventional FCS-MPTC algorithm has a large computational burden due to a large number of available voltage vectors as in multi-level inverters or discrete SVM MPC technique and B) the presence of weighting factors in the cost function with several control objectives. This thesis developed a two-stage optimization-based FCS-MPC algorithm for an induction motor drive that uses reduced voltage control sets (RVCS) to evaluate the pre-defined cost function in the prediction loop. The proposed algorithm lowers the computational burden of the controller in two cascaded stages for a three-level neutral-point clamped voltage source inverter (3L-NPC VSI) fed IM drive. The voltage vector selection in the first stage of the proposed two-stage algorithm is executed using two different approaches: six long voltage vectors in the first stage and three long voltage vectors in the first stage. The first approach presented in Chapter 3 evaluates all six long voltage vectors in the first stage to obtain a long optimal voltage vector. In the second stage, the nearest 11 voltage vectors of the optimal long voltage vector are evaluated to reach the final optimal voltage vector. The cost function values from both stages are compared to each other to select the final optimal voltage vector. In chapter 4, the second approach evaluates three long voltage vectors instead of 6 in the first stage. It uses the sign of stator flux deviation and the position of stator flux to select the voltage vectors in the first stage. The second stage of this flux-error based approach is the same as Chapter 3. The proposed FCS-MPC two-stage optimization algorithms of Chapter 3-5 for an IM drive reduces the computational burden of the conventional algorithm which evaluates all available 27 voltage vectors. In Chapter 3, the proposed algorithm’s cost function combined torque and flux as prime control variables along with some other constraints i.e. neutral point voltage balance, switching frequency reduction, over current protection. This proposed algorithm reduced the computational burden of the conventional algorithm from 27 to 17 but suffered from sub-optimal decisions. The sub-optimality occurs when the reduced control set of the voltage vectors does not include the optimal vector and therefore the algorithm does not select the same voltage vector as the conventional all voltage vector-based MPC would have selected. The issue from Chapters 3 and 4 led to the development of a low-complexity finite control set model predictive flux control (FCS-MPFC) algorithm presented in Chapter 5 which eliminates the flux weighting factor in the cost function. A reference stator flux vector calculator (RSFVC) with an inner proportional-integral torque regulator is incorporated into the proposed two-stage optimization-based algorithm to convert the torque and flux amplitude references into an equivalent stator flux reference vector. The stator flux reference vector is then included in the cost function for flux deviation calculation. This allowed the elimination of flux weighting factor and elimination of flux-error based look-up table used in the first stage (the second approach) and solved the sub-optimality issue of the two-stage optimization-based FCS-MPTC algorithms. The developed FCS-MPFC algorithm experimentally demonstrated that this algorithm can be used for an induction motor drive to accurately control the electromagnetic torque, stator flux, and NPV with zero sub-optimal decision and reduced computation burden. Hence the original performance of the conventional FCS-MPFC is preserved with a lower computational burden. Operation with conventional FCS-MPC results in high torque and flux ripples in a 2-level inverter drive. Recently, MPC algorithms with integrated discrete space vector modulation (DSVM) for two-level inverter fed drives have been reported. DSVM technique creates a number of virtual voltage vectors using the real available voltage vectors. This allows for better optimization and minimization of the torque and flux errors/ripple due to the virtually created sub-divisions in the space vector region. However, all conventional DSVM integrated with FCS-MPTC yields a very high and complex calculation load. Therefore, these techniques are not practical for a cost-effective drive system. This thesis also proposes a simplified DSVM based FCS-MPFC algorithm for the two-level inverter (2L-VSI) fed IM drive system. This technique applies optimal voltage vectors that yield the desired zero sub-optimality and therefore the performance of the original DSVM-algorithm is retained. Lastly, a switching frequency reduction technique for the developed DSVM based FCS-MPFC is proposed which generates a minimum number of switching transitions by an effective synthesis of the virtual voltage vectors. This reduced switching is effective in any operating region for the 2L-VSI fed IM drive. This achieves low switching frequency of the inverter while preserving the same performances of the drive as the conventional algorithm. All the proposed methods which achieved zero sub-optimality have been experimentally tested in parallel with their conventional forms to detect the sub-optimality and compared with recently published algorithms to demonstrate their superior performances in a wide operating range.
Cost function, Model predictive control, Induction motor drive, Inverters, Optimization, Computational burden, Sub-optimal, Flux control, Nonlinear control
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