The study presented in the paper investigates rigorously the methods for enhancing the performance of interconnected electric drives within the finishing group of a hot rolling mill. In particular, it examines the impact of supply voltage fluctuations on drive precision and the interactions mediated by the rolled metal strip. A detailed analysis of the existing power supply system identifies the primary causes of dynamic deviations in drive operation.A combined angular velocity control system is proposed to regulate the excitation of DC electric motors. The adaptive control strategy modulates magnetic flux during grid voltage drops, thereby reducing speed fluctuations and minimising tension inconsistencies in the inter-stand gaps. Unlike conventional systems that disregard supply voltage variations, the adaptive approach significantly improves the stability of the rolling process.A mathematical model of the system, incorporating second- and third-order elastic couplings arising from both mechanical and electromagnetic interactions among the drives, is developed. Numerical simulations conducted in MATLAB/Simulink validate the efficiency of the proposed method. Optimal values for the relative reduction in the magnetic flux are determined to minimise discrepancies in drive currents and strip elongation. The results confirm that implementing adaptive control enhances system stability, improves rolling quality, and reduces the load on the power supply, thereby supporting its adoption in rolling mills operating under unstable grid voltage conditions.

In recent years, due to the tightening of competition in the global market of steel producers, the requirements for the quality of hot-rolled steel have increased. The finishing group of the rolling mill is characterized by a complex structure of mechanical and electrical parts. The operation of...

The integration of solar generation into national energy balances is associated with a wide range of technical, economic, and organizational challenges, the solution of which requires the adoption of innovative strategies for energy system management. The inherent variability of electricity production, driven by fluctuating climatic conditions, complicates system balancing processes and necessitates the reservation of capacities from conventional energy sources to ensure reliability. Under modern market conditions, the pricing of generated electricity is commonly based on day-ahead forecasts of day energy yield, which significantly affects the economic performance of solar power plants. Consequently, achieving high accuracy in day-ahead electricity production forecasting is a critical and highly relevant task. To address this challenge, a physico-statistical model has been developed, in which the analytical approximation of daily electricity generation is represented as a function of a random variable—cloud cover—modeled by a β-distribution. Analytical expressions were derived for calculating the mathematical expectation and variance of daily electricity generation as functions of the β-distribution parameters of cloudiness. The analytical approximation of daily generation deviates from the exact value, obtained through hourly integration, by an average of 3.9%. The relative forecasting error of electricity production, when using the mathematical expectation of cloudiness compared to the analytical approximation of daily generation, reaches 15.2%. The proposed forecasting method, based on a β-parametric cloudiness model, enhances the accuracy of day-ahead production forecasts, improves the economic efficiency of solar power plants, and contributes to strengthening the stability and reliability of power systems with a substantial share of solar generation.

This paper investigates, for the first time, the synthesis of a controller that incorporates a fractional-order integral component to achieve a closed-loop astaticism order greater than one. To enhance both static and dynamic accuracy, the controller integrates direct-signal-propagation neural networks within each control channel. The controlled plant is the BLDCM speed loop, which is modeled using a fractional-order differential equation. The study compares the performance of four controller types: a classical PID regulator tuned close to the optimal modulus criterion (IntPID); a fractional PI–PI^μD controller (FrPID) that achieves an astaticism order of at least 1.8; and two hybrid neuro-controllers, NN–IntPID and NN–FrPID. While the FrPID controller reduces the root-mean-square error by nearly a factor of five compared with IntPID, the best results are delivered by NN–FrPID. Specifically, it decreases overshoot eight-fold during a reference step (from 2.98% to 0.35%), lowers the root-mean-square error during linear reference tracking by a factor of eleven, and reduces the relative speed error by more than thirty-five times. When combined with a fast learning algorithm executed at each control-cycle iteration, the controller enables the closed loop to adapt not only to variations in gain coefficients, but also to changes in the fractional-aperiodic order of the plant. These results demonstrate that neural fractional-integral controllers offer strong potential for improving accuracy and robustness in BLDC motor drives and are applicable to a wide range of electromechanical systems.

One of the most essential characteristics of a complex technical system is that it performs ‘an important useful function only with the assistance of a human operator and standard infrastructures…’ [1]. At the earliest stages of the development of a system, deciding whether the human operator should be regarded as part of the complex system or an external entity is necessary. In most cases, the human operator should be treated as an external entity.Most complex technical systems cannot function without the active involvement of a human operator, who retains responsibility for decision-making, control, and management functions. From a functional standpoint, operators can be considered an integral part of the system. However, the system designer rarely possesses sufficient authority over the operator to incorporate them fully into the system’s design. From the perspective of the systems engineer, the human operator instead represents an element of the system’s environment.Under this paradigm, the systems engineer must devote particular attention to the design and development of the operator interface, which is a critically important aspect of any complex technical system [2]. Accordingly, the development of universal technical solutions that enhance the quality of the human–machine interface in complex technical systems represents both a significant and timely challenge, the resolution of which can be applied across a wide range of practical applications.The primary objective of the present work is to improve the performance of the human–machine interface by compensating for the inertial and nonlinear characteristics of the human operator as a control element within a complex technical system.