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.

Model predictive control (MPC), manly based on a direct use of an explicit and identifiable model, has been widely used in controller design in different applications both by academia and industry. The reason for such popularity is due to its strong ability for providing high performance electric drive systems, as highly recognized as the most reliable control approach compared with field-oriented control (FOC) and direct torque control (DTC). In general, the MPC has numerous features and advantages, such as direct switching states to the converter without any modulation, online optimization with multivariable control, low current total harmonic distortion, low switching loss, etc. The aim of this paper is to provide a comprehensive review for major development and achievements of the recent progress on the MPC for electrical machines and drives. This review begins with the innovative topologies and operating principles of fundamental MPC, and ends to summary on different advanced MPC algorithms. Typical MPC techniques have been fully adopted to enhance the drive performance of the electrical drives, mainly including finite-set model predictive control (FS-MPC) based on tuning weighting factors, without weighting factors, maximum torque per ampere, low number of switching vectors, and multi voltage vectors in one sample period. Finally, great attention has been paid to the discussion of the new trends and future research topics.

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.

The major contribution of this paper is to propose a Fuzzy Logic Global Power Management Strategy for Hybrid Electric Vehicles (HEVs) that are driven by the PM-EVT (PM machine—Electric Variable Transmission) powertrain, such that the PM-EVT will have superior advantages over other types of powertrains, including the current Toyota Prius powertrain for series-parallel HEVs. This has been investigated throughout three aspects. The first is the optimum power splitting between the Internal Combustion Engine (ICE) and the PM-EVT. The second is maximizing the vehicle’s energy capture during the braking process. Finally, sustaining the State of Charge (SOC) of the battery is adopted by a robust ON/OFF controller of the ICE. These goals have been accomplished by developing three fuzzy logic (FL) controllers. The FL controllers are designed based on the state of charge of the battery, vehicle’s velocity, traction torque, and the vehicle’s requested power. The integration of the studied system is accomplished via the Energetic Macroscopic Representation (EMR) simulation model strategy based on the software Matlab/Simulink. The PM-EVT based HEV system with the proposed power management strategy is validated by comparing to the Toyota Prius HEV. The vehicle’s performances have been analyzed throughout a combined long-trip driving cycle that represents the normal and the worst operating conditions. The simulation results show that global control system is effective to control the engine’s operating points within the highest efficiency region, exploiting of EVT machines for capturing maximum braking energy, as well as to sustain the SOC of the battery while satisfy the drive ability. The proposed control strategy for the studied HEVs sounds interesting and feasible as supported by a large amount of simulation results.

This paper introduces the application of the variants of the Grey Wolf Optimisation algorithm for the sake of assessing unknown parameters of Proton Exchange Membrane Fuel Cells models. Three versions of Grey Wolf Optimisation algorithm are applied: Conventional Grey Wolf Optimisation, Improved Grey Wolf Optimisation based on dimension learning‐based hunting, and Selective Opposition‐based Grey Wolf Optimisation. Moreover, Optimisation algorithms of Ant Lion Optimiser, Atom search optimisation, Dragonfly algorithm, and Multi‐Verse Optimiser have been applied to estimate the parameters of Proton Exchange Membrane Fuel Cells models for the validation purpose. These algorithms are applied to three commercial Proton Exchange Membrane Fuel Cells stacks: BCS 500W‐PEM, 500W‐SR‐12PEM and 250W‐stack. The analyses are executed considering several operational circumstances. Sum of square errors value of the results based on parameters estimation and those experimentally tested are calculated. The objective function is chosen as sum of square errors value. The results are compared with those obtained using well‐known methods in the literature to validate the effectiveness of the proposed methods. It is noticeable that the simulated I‐V curves momentously match the datasheet curves for all the studied cases. In addition, considering the accuracy of the solution and the convergence speed, the Proton Exchange Membrane Fuel Cells model based on the Improved Grey Wolf Optimisation algorithm excels all other algorithms. Based on the simulation results, the Improved Grey Wolf Optimisation algorithm can improve the optimisation efficiency to 99.97 for the 250 W stack while the efficiency with Grey Wolf Optimisation and Selective Opposition‐based Grey Wolf Optimisation are 98.83 and 98.84, respectively, for the 250 W stack case study.