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Summary Recently, dynamic induction control is gaining the interest of the wind energy community as a promising strategy to increase the overall wind farm power production. Such a technique is based on a dynamic variation of the upstream rotor thrust, generated through a suitable blade pitch motion, to promote a faster wake recovery. Notwithstanding some promising results already published, the knowledge of the physical mechanism, connecting dynamic induction to the increased in‐wake velocity, was not yet exploited to enhance control effectiveness. This paper, through a computational fluid dynamics procedure based on large eddy simulations coupled with actuator line models, provides a description of the working principles of this control from a fluid dynamics standpoint. The analyses show that the faster recovery is strictly connected to the ability of the blade tip vortices to roll up and sucking energy from the outer flow. Exploiting such knowledge, a novel control strategy, which improves the vortex roll up mechanism, is proposed and analyzed. The new control proved more effective than standard techniques especially for very low turbine spacing.

Dynamic Induction Control (DIC) has been recently proposed as means for enhancing wake recovery and, in turn, for increasing the overall produced power. A faster wake recovery is triggered by a Periodic Collective Motion (PCM), following a single sine function (S-PCM), or by a combination of Gaussian functions (G-PCM). Both techniques are associated with power gains in simple two- or three-turbine farms, but entail an increase in machine loading. A technique named the Helix approach generates a dynamic induction through a thrust that varies in direction but not in magnitude, reducing the tower loading. This work aims to analyse the impact of bluff bodies, such as nacelle and tower on the performances of PCD techniques, and to quantify the DIC impact on the loads. A 5 MW reference wind turbine is used for the model, implemented in OpenFAST and SOWFA to perform large-eddy simulations (LES). The results obtained at a distance of 3D downstream, show less evidence of the bluff bodies using the PCM than the baseline, as an effect of the increased in-wake mixing. In a two-turbine wind farm with a separation of 3D between turbines, this effect leads to an increment in the overall power output of the farm, despite the presence of the tower and nacelle. The blockage itself does not seem to hamper the effectiveness of DIC. In both cases, DIC is responsible for an increment of about 7% in the overall power output.

From the dynamic point of view, an ion channel for a biology neuron can be estimated by applying an induction coil in a neural circuit, and the influence of the inner and outer magnetic field on the ions' propagation is also estimated. The magnetic field generated by the magnetic flux-controlled memristor (MFCM) is equivalent to that stored in the induction coil. Therefore, the magnetic field in the neural circuit can be estimated using an MFCM. In this work, a neural circuit of the memristive characteristic ion channel is designed by applying an MFCM to replace the inductor branch of a simple neural circuit, and this memristive ion channel can estimate the influence of the magnetic field on the firing patterns of a neuron. The equivalent oscillator equations for the neural circuit and the corresponding energy function are calculated by the Kirchhoff's theorem and the Helmholtz theory, respectively. Furthermore, to explore the adaptive regulation of the electrical activity of the neuron by energy flow, a parameter adaptive growth criterion controlled by energy ratio is designed. The numerical results indicate that the MFCM can effectively simulate the ion channels of biological neurons, the firing modes of a neuron model with an ion memristive channel are adjusted by the magnetic field distribution, and the stochastic resonance of a neuron can be induced under a suitable noise magnetic field. This study would provide guidance for the simulation of neural circuit ion channels.

Direct power control (DPC) is among the most popular control schemes used in renewable energy because of its many advantages such as simplicity, ease of execution, and speed of response compared to other controls. However, this method is characterized by defects and problems that limit its use, such as a large number of ripples at the levels of torque and active power, and a decrease in the quality of the power as a result of using the hysteresis controller to regulate the capacities. In this paper, a new idea of DPC using artificial neural networks (ANNs) is proposed to overcome these problems and defects, in which the proposed DPC of the doubly fed induction generators (DFIGs) is experimentally verified. ANN algorithms were used to compensate the hysteresis controller and switching table, whereby the results obtained from the proposed intelligent DPC technique are compared with both the classical DPC strategy and backstepping control. A comparison is made between the three proposed controls in terms of ripple ratio, durability, response time, current quality, and reference tracking, using several different tests. The experimental and simulation results extracted from dSPACE DS1104 Controller card Real-Time Interface (RTI) and Matlab/Simulink environment, respectively, have proven the robustness and the effectiveness of the designed intelligence DPC of the DFIG compared to traditional and backstepping controls in terms of the harmonic distortion of the stator current, dynamic response, precision, reference tracking ability, power ripples, robustness, overshoot, and stability.

An induction motor is generally used in industrial applications because it is reliable, robust, and low cost. Reliability is one of the essential parameters based on which the motor is selected, and the induction motor primarily comes into force. The well-founded induction motor gives good results under various operating states. To achieve this, the values of the motor are kept in mind. Dynamic simulation plays a significant part in evaluating the model’s design process to eliminate design errors in typical construction types and when testing the motor drive system. The induction motor is modeled in a synchronously revolving rotor flux-oriented frame, which is used as a reference. For sensorless vector control and induction motor control methods, accurate knowledge of a few induction motor parameters is necessary. The presentation of the drive will degrade if the original data in the motor do not match the values utilized in the controller. Various mechanisms have been developed to calculate the online and offline parameters of the induction machine for its application in high-performance drives. The foremost grail of this review paper is to present dynamic modeling and other considerable approaches used for estimating the induction motor parameter. This paper also constitutes some simulation examples related to dynamic modeling and parameter estimation techniques, which may be useful for specialists in the field of electric drive control systems.

The paper presents an optimal control strategy selection for an induction motor during voltage start-up, focusing on limiting the maximum current and starting torque surges at a given start-up time. Optimizing the operation of a soft-start system can be achieved using a simple system that assumes a linear voltage increase from a specific initial value. The advantage of this approach is the elimination of transducer systems for measuring voltages and currents. That approach requires simulating the drive system to select the optimal variant for a given system based on maximum and minimum torque values, maximum current, and optimal starting time. Transient calculations, for example, for the induction motor, were performed using both the analytical and circuit-field methods for different U/f time characteristics under varying motor load conditions, and the simulation results were compared with measurements.

Memristor has been extensively employed to emulate neuron/synapse-inspired behaviors and to characterize the electromagnetic induction generated by ionic flowing. A link between memristive features and neural electrical activities is significantly necessary to be investigated. Thus, we propose a new neuron model with a locally active threshold flux-controlled (LTF) memristor, which depicts the electromagnetic induction. The LTF memristive neuron model can exhibit a regular evolution and transition of various firing patterns dependent upon the negative different conductance of the memristor, through performing the corresponding numerical simulations. It is demonstrated that due to the locally active threshold effect, the obtained model has complex firing behaviors. The memristive neural network is connected via chemical synapses. The memristive neural network under the modulation of excitatory and inhibitory chemical synapses shows different synchronous patterns. The captured results reveal that the locally active threshold effect is crucial for the generation of complex firing modes and the emergence of synchronization behaviors.

With the development of high-speed microprocessors, it is now possible to implement mathematically complex vector control algorithms without compromising on the performance of motor drive. Among vector control techniques space vector proportional-integral (PI), direct-torque control (DTC), field-oriented control (FOC), model-predictive control (MPC) are being widely used in industries. But their limitations have urged researchers to develop more advance techniques. In this paper, a new technique learning and adaptive model - based predictive control (termed as LAMPC) is proposed for the vector control of three phase induction motor. In the proposed method, the dynamic model of induction motor is updated adaptively based on prediction (receding horizon principle) for the inner control loop (current control) while the brain emotional learning-based intelligent controller (BELIC) is used for the outer control loop (speed control). The proposed methodology offers desired dynamic response, precise tracking, good disturbance handling capability along with satisfactory steady-state performance. To show the effectiveness of the proposed approach, benchmark simulation results for various inputs are presented using MATLAB/Simulink. Finally, the detailed qualitative and quantitative comparison of the proposed LAMPC is made with the most relevant vector techniques to show its significance.

This work presents the results of a research study focused on the development and evaluation of an algorithmic optimal control framework for energy-efficient operation of screw compressors in smart power systems. The proposed approach is based on the Pontryagin maximum principle (PMP), which enables the synthesis of a mathematically grounded regulator that minimizes the total energy consumption of a nonlinear electromechanical system composed of a screw compressor and a variable-frequency induction motor. Unlike conventional PID controllers, the developed algorithm explicitly incorporates system constraints, nonlinear dynamics, and performance trade-offs into the control law, allowing for improved adaptability and energy-aware operation. Simulation results obtained using MATLAB/Simulink confirm that the PMP-based regulator outperforms classical PID solutions in both transient and steady-state regimes. Experimental tests conducted in accordance with standard energy consumption evaluation methods showed that the proposed PMP-based controller provides a reduction in specific energy consumption of up to 18% under dynamic load conditions compared to a well-tuned basic PID controller, while maintaining high control accuracy, faster settling, and complete suppression of overshoot under external disturbances. The control system demonstrates robustness to parametric uncertainty and load variability, maintaining a statistical pressure error below 0.2%. The regulator’s structure is compatible with real-time execution on industrial programmable logic controllers (PLCs), supporting integration into intelligent automation systems and smart grid infrastructures. The discrete-time PLC implementation of the regulator requires only 103 arithmetic operations per cycle and less than 102 kB of RAM for state, buffers, and logging, making it suitable for mid-range industrial controllers under 2–10 ms task cycles. Fault-tolerance is ensured via range and rate-of-change checks, residual-based plausibility tests, and safe fallbacks (baseline PID or torque-limited speed hold) in case of sensor faults. Furthermore, the proposed approach lays the groundwork for hybrid extensions combining model-based control with AI-driven optimization and learning mechanisms, including reinforcement learning, surrogate modeling, and digital twins. These enhancements open pathways toward predictive, self-adaptive compressor control with embedded energy optimization. The research outcomes contribute to the broader field of algorithmic control in power electronics, offering a scalable and analytically justified alternative to heuristic and empirical tuning approaches commonly used in industry. The results highlight the potential of advanced control algorithms to enhance the efficiency, stability, and intelligence of energy-intensive components within the context of Industry 4.0 and sustainable energy systems.

Precise and smooth actuation is a central requirement in surgical robotics, where small tracking errors or oscillations can directly affect task quality and safety. This paper studies the control of an induction-motor-driven surgical joint using a sliding-mode strategy enhanced by fractional-order operators and implemented within a DTC–SVM structure. The motivation is to improve motion smoothness and disturbance rejection without sacrificing the fast dynamic response offered by direct torque control. A dynamic model of the actuator is developed by combining the electrical equations of the induction motor with the mechanical dynamics of a robotic joint, including inertia, viscous friction, gravity-induced torque, and Coulomb friction. Fractional-order sliding surfaces are introduced for both position and flux regulation, and the closed-loop stability is examined through Lyapunov-based arguments. Simulation results show accurate trajectory tracking with limited overshoot and smooth transient responses. The motor speed remains well regulated, while stator flux and currents stay within admissible bounds. The electromagnetic torque adapts to load variations with reduced ripple, and the rotor pulsation remains bounded. Within the limits of numerical evaluation, these results indicate that the proposed fractional-order sliding-mode DTC–SVM scheme is suitable for precision-oriented surgical robotic actuation.