Multi-motor systems are strong coupled multiple-input–multiple-output systems. The main objective in multi-motor drive control is to achieve synchronized operation of all motors in the system. In this paper, multi-motor systems are classified in accordance with their control demands. This paper also provides a systematic categorization of multi-motor synchronization techniques. The review of recent research literature indicates that fuzzy algorithms are widely used in multi-motor control. Finally, in this paper, a review of fuzzy logic controllers and their functionalities in multi-motor control is given.

The paper researches the impact of the input data resolution on the solution of optimal allocation and power management of controllable and non-controllable renewable energy sources distributed generation in the distribution power system. Computational intelligence techniques and co-simulation approach are used, aiming at more realistic system modeling and solving the complex optimization problem. The optimization problem considers the optimal allocation of all distributed generations and the optimal power control of controllable distributed generations. The co-simulation setup employs a tool for power system analysis and a metaheuristic optimizer to solve the optimization problem. Three different resolutions of input data (generation and load profiles) are used: hourly, daily, and monthly averages over one year. An artificial neural network is used to estimate the optimal output of controllable distributed generations and thus significantly decrease the dimensionality of the optimization problem. The proposed procedure is applied on a 13 node test feeder proposed by the Institute of Electrical and Electronics Engineers. The obtained results show a huge impact of the input data resolution on the optimal allocation of distributed generations. Applying the proposed approach, the energy losses are decreased by over 50–70% by the optimal allocation and control of distributed generations depending on the tested network.

An optimization procedure for type 1 Takagi–Sugeno Fuzzy Logic Controller (FLC) parameter tuning is shown in this paper. Ant colony optimization is used to obtain the optimal controller parameters, and only a small amount of post-optimization parameter adjustment is needed. The choice of controller parameters is explained, along with the methodology behind the criterion for objective function value calculation. The optimized controller is implemented as an outer-loop speed controller for Predictive Current Control (PCC) of an induction machine. The performance of the proposed control method is compared with that of several other model predictive control methods. The results show a 55% decrease in speed tracking error and 74% decrease in torque overshoot.

This paper presents a method for selecting the sampling time for induction machine parameter estimation from the machine line start measurements. the metaheuristic optimization method is used to find the optimal Prony exponential series approxiamtion of the line start transient current. From the optimal approximation, poles of the linearized induction machine model are computed and used to determine the optimal sampling time. the results show that sampling frequencies needed for parameter estimation are much lower than 1–15 kHz commonly used today. This reduces the necessary amount of collected data and the computing power needed for the estimation. the optimal sampling time is computed for the simulated and for the measured data. Referenced parameter estimation technique is tested for the measured transient showing benefits of using the optimal sampling time.

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