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A Proportional-Integral (PI) controller is a system designed to diminish the error between the desired reference value and the measured variable of the process to be regulated. The PI controller works according to two input parameters that must be calculated by a tuning process. In this work, the process is performed using intelligent swarm algorithms to seek for optimal parameters of PI controller. Three reactive, nature-inspired algorithms (bat, whale optimization and cuckoo search), were used to tune the PI controller. The control scheme based on linear regulators is used to improve the performance of the permanent magnet synchronous motor (PMSM) and to regulate the rotor speed to a desired reference value. In order to validate the effectiveness of the controller, simulation is performed under constant load condition, varying load condition and varying set speed conditions of the PMSM. [ABSTRACT FROM PUBLISHER]

Summary: Classical control design methods have been successfully applied to several practical engineering systems for regulation tasks around a desired equilibrium operation state. Nevertheless, real‐time information of some system parameters and mechanical torque could be required to guarantee an efficient and robust control performance for variable operation conditions. In this paper, an online and algebraic scheme is proposed for simultaneous estimation of parameters and variable mechanical load torque for nonlinear shunt DC motors. An adaptive PI velocity tracking control scheme is also described to verify the effectiveness and efficiency of the parametric estimation approach. Control dynamic gains are computed online by applying a B‐spline neural network algorithm. Computational simulation results confirm the fast estimation of parameters and variable mechanical torque. [ABSTRACT FROM AUTHOR]

A B-spline neural networks-based adaptive control technique for angular speed reference trajectory tracking tasks with highly efficient performance for direct current shunt motors is proposed. A methodology for adaptive control and its proper training procedure are introduced. This algorithm sets the control signal without using a detailed mathematical model nor exact values of the parameters of the nonlinear dynamic system. The proposed robust adaptive tracking control scheme only requires measurements of the velocity output signal. Thus, real-time measurements or estimations of acceleration, current and disturbance signals are avoided. Experimental results confirm the efficient and robust performance of the proposed control approach for highly demanding motor operation conditions exposed to variable-speed reference trajectories and completely unknown load torque. Hence, laboratory experimental tests on a direct current shunt motor prove the viability of the proposed adaptive output feedback trajectory tracking control approach. [ABSTRACT FROM AUTHOR]

This proposal is aimed to overcome the problem that arises when diverse regulation devices and controlling strategies are involved in electric power systems regulation design. When new devices are included in electric power system after the topology and regulation goals were defined, a new design stage is generally needed to obtain the desired outputs. Moreover, if the initial design is based on a linearized model around an equilibrium point, the new conditions might degrade the whole performance of the system. Our proposal demonstrates that the power system performance can be guaranteed with one design stage when an adequate adaptive scheme is updating some critic controllers' gains. For large-scale power systems, this feature is illustrated with the use of time domain simulations, showing the dynamic behavior of the significant variables. The transient response is enhanced in terms of maximum overshoot and settling time. This is demonstrated using the deviation between the behavior of some important variables with StatCom, but without or with PSS. A B-Spline neural networks algorithm is used to define the best controllers' gains to efficiently attenuate low frequency oscillations when a short circuit event is presented. This strategy avoids the parameters and power system model dependency; only a dataset of typical variable measurements is required to achieve the expected behavior. The inclusion of PSS and StatCom with positive interaction, enhances the dynamic performance of the system while illustrating the ability of the strategy in adding different controllers in only one design stage. [ABSTRACT FROM AUTHOR]

This article focuses on the optimal gain selection for Proportional Integral (PI) controllers comprising a speed control scheme for the Permanent Magnet Synchronous Motor (PMSM). The gains calculation is performed by means of different algorithms inspired by nature, which allows improvement of the system performance in speed regulation tasks. For the tuning of the control parameters, five optimization algorithms are chosen: Bat Algorithm (BA), Biogeography-Based Optimization (BBO), Cuckoo Search Algorithm (CSA), Flower Pollination Algorithm (FPA) and Sine-Cosine Algorithm (SCA). Finally, for purposes of efficiency assessment, two reference speed profiles are introduced, where an acceptable PMSM performance is attained by using the proposed PI controllers tuned by nature inspired algorithms. [ABSTRACT FROM AUTHOR]