Abdelsalam A. Ahmed

Finite-Control Set Model Predictive Control Method for Torque Control of Induction Motors Using a State Tracking Cost Index

This paper presents a novel torque control method for two-level-inverter-fed induction motor drives. The control principle is based on a finite-control set model predictive control (FCS-MPC) using a state tracking cost index. In the online procedure of the proposed FCS-MPC, the optimal voltage vector and its corresponding optimal modulation factor are determined based on the principle of torque and rotor flux error minimization. In this method, a reference state is determined in a systematic way so that the reference torque tracking with maximum torque per ampere and flux-limited operation could be achieved. In addition, a weighting matrix for the state tracking error is optimized in offline using the linear matrix inequality based optimization problem. The efficacy of the proposed FCS-MPC method is proved by the simulation and experimental results at different working circumstances. The comparison of the presented control system with the conventional FCS-MPC and with other reported FCS-MPC with modulation control is made. The proposed algorithm yields fast dynamic performance and minimum torque and current ripples at different speed and torque levels.

A Comparison of Finite Control Set and Continuous Control Set Model Predictive Control Schemes for Speed Control of Induction Motors

This paper presents a comparative study of two predictive speed control schemes for induction machine (IM) in terms of their design and performance. The first control scheme is finite control set-model predictive control (FCS-MPC) with modulation control and the second control scheme is continuous control set-model predictive control (CCS-MPC) with space vector-pulse width modulation. The two schemes adopt the cascaded control approach, which consists of an inner MPC loop for torque control and outer MPC loop for speed control using two individual cost functions. The outer MPC produces the required torque to drive the IM at the reference speed while the reference torque is taken as the input of the inner MPC, which in turn generates control signals for the inverter. The control states of the two MPCs are constrained with the maximum limits of the drive system. The state feedback is achieved with a standard Kalman filter, which estimates the nonmeasured load torque. For a fair comparison, both approaches are applied to the same IM at the same operational circumstances. The control approaches are implemented and validated in an experimental environment using the same sampling frequency on the same test bench (3.7 kW IM drive). The behavior of the control approaches is assessed by applying reference and disturbance steps to the system in different operational modes. Comparison of the predictive schemes leads to the conclusion that the both MPC approaches achieve similar performances. However, the CCS-MPC scheme has a smaller current ripple and is of low computational complexity. The computing duration is not very different for the three tested schemes. CCS-MPC can cope with a less powerful DSP than for FCS.

Development of electric vehicle powertrain: Experimental implementation and performance assessment

This paper presents an experimental evaluation of performance of a complete electric vehicle (EV) system. Experimental implementation of powertrain is presented and then the performance of each plant is tested and assessed. This drivetrain includes two permanent magnet synchronous motors (PMSMs), two power converters with their gate drivers, the control board of TMS320F28335 DSP, and energy storage system (ESS). The ESS includes battery cells, battery manage system (BMS) and Ultracapacitor unit. This work considers the possible performance of the EV based on theoretical calculations using its specifications. The dynamic performance of the power plants of powertrain is evaluated at both out-vehicle and in-vehicle operation. The performance is demonstrated using a commercial dynamometer. The experimental tests proved the applicability of the EV in different road circumstances.

Model predictive torque control of PMSM for EV drives: A comparative study of finite control set and predictive dead-beat control schemes

In this paper, two schemes of a model predictive control (MPC), namely finite control set with modulation control (FCS-MPC) and dead-beat model predictive control (DB-MPC) as a continuous control set (CCS-MPC) are applied to permanent magnet synchronous motor (PMSM) for torque control. The FCS-MPC strategy chooses the optimal output voltage vector with its optimal modulation interval. In CCS-MPC strategy, a current/torque control structure that utilizes the advantage of the dead-beat controller and combines it with a reference current prediction is presented. Then, we compare the performance of the two approaches. The comparison is mainly handled from two sides: (1) ripples reduction of torque and current waves which is assessed at steady-state, (2) fast response of torque at transient times. For a neutral comparison, both techniques are implemented to the same PMSM at the same operational circumstances. The performances of both MPC structures are found out by simulation results. As a result, torque and current ripples are reduced by CCS; whereas the FCS presents somewhat faster torque response.