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This study presents the tilt integral derivative (TID) controller technique for controlling the speed of BLDC motors in order to improve the real-time control of brushless direct current motors in electric vehicles. The TID controller is applied to the considered model to enhance its performance, e.g., torque and speed. This control system manages the torque output, speed, and position of the motor to ensure precise and efficient operation in EV applications. Brushless direct current motors are becoming more and more popular due to their excellent torque, power factor, efficiency, and controllability. The differences between PID, TID, and PI controllers are compared. The outcomes demonstrated that the TID control enhanced the torque and current stability in addition to the BLDC system’s capacity to regulate speed. TID controllers provide better input power for BLDC (brushless DC) drives than PI and PID controllers do. Better transient responsiveness and robustness to disturbances are features of TID controller design, which can lead to more effective use of input power. TID controllers are an advantageous choice for BLDC drive applications because of their increased performance, which can result in increased system responsiveness and overall efficiency. In an experimental lab, a BLDC motor drive prototype is implemented in this study. To fully enhance the power electronic subsystem and the brushless DC motor’s real-time performance, a test bench was also built.

This study presents a novel control strategy for the regulation of engine speed in nonlinear four-cylinder spark ignition (SI) engines by integrating a proportional–integral–derivative controller with a filter (PID-F) tuned using the flood algorithm (FLA). The approach leverages the dual-phase exploration and exploitation mechanism of FLA to determine optimal controller parameters efficiently, addressing the nonlinear and time-varying behavior of SI engines. A modified objective function is formulated to penalize overshoot and cumulative tracking error simultaneously, ensuring rapid transient response and precise steady-state accuracy. The proposed control framework is modeled and validated in MATLAB/Simulink and benchmarked against existing tuning techniques, including the Simulink PID tuner and selected metaheuristic optimizers such as the whale optimization algorithm, sinh-cosh optimizer, and cuckoo search. Comparative analyses demonstrate the superiority of the FLA-optimized PID-F controller in achieving stable, robust, and noise-resilient speed regulation under varying load and disturbance conditions. The findings establish the FLA as an efficient and scalable optimization tool for real-time controller tuning in automotive applications, contributing to a practical and computationally efficient solution for advanced engine control systems.

The emergence of building condenser water systems with all-variable speed pumps and tower fans allows for increased efficiency and flexibility of chiller plants in partial load operation but also increases the control complexity of condenser water systems. This study aims to develop an integrated modeling technique for evaluating and optimizing the energy performance of such a condenser water system. The proposed system model is based on the semi-physical semi-empirical chiller, pump, and cooling tower models, with capabilities of fully considering the hydraulic and thermal interactions in the condenser water loop, being solved analytically and much faster than iterative solvers and supporting the explicit optimization of the pump and tower fan frequency. A mathematical approach, based on the system model and constrained optimization technique, is subsequently established to evaluate the energy performance of a typical dual setpoint-based variable speed strategy and find its energy-saving potential and most efficient operation by jointly optimizing pumps and tower fans. An all-variable speed chiller plant from Wuhan, China, is used for a case study to validate the system model’s accuracy and explore its applicability. The results showed that the system model can accurately simulate the condenser water system’s performance under various operating conditions. By optimizing the frequencies of pumps and tower fans, the total system energy consumption can be reduced by 12%–13% compared to the fixed dual setpoint-based strategy with range and approach setpoints of 4 °C and 2 °C. In contrast, the energy-saving potential of optimizing the cooling tower sequencing is insignificant. A simple joint speed control method for optimizing the pumps and tower fans emerged, i.e., the optimal pump and fan frequency are linearly correlated (if both are non-extremes) and depend on the chiller part load ratio only, irrespective of the ambient wet-bulb temperature and chilled water supply temperature. It was also found that the oversizing issue has further limited the energy-saving space of the studied system and results in the range and approach setpoints being inaccessible. The study’s findings can serve as references to the operation optimization of all-variable speed condenser water systems in the future.

This paper introduces a novel approach to speed-sensorless predictive torque control (PTC) in an autonomous wind energy conversion system, specifically utilizing an asymmetric double star induction generator (ADSIG). To achieve accurate estimation of non-linear quantities, the Gaussian Process Regression algorithm (GPR) is employed as a powerful machine learning tool for designing speed and flux estimators. To enhance the capabilities of the GPR, two improvements were implemented, (a) hyperparametric optimization through the Bayesian optimization (BO) algorithm and (b) curation of the input vector using the gray box concept, leveraging our existing knowledge of the ADSIG. Simulation results have demonstrated that the proposed GPR-PTC would remain robust and unaffected by the absence of a speed sensor, maintaining performance even under varying magnetizing inductance. This enables a reliable and cost-effective control solution.

The paper deals with the creation and implementation of a methodology for optimizing the parameters of cascade control of the machine tool axis drives. The first part presents the identification of a dynamic model of the axis based on experimental data from measuring the axis dynamics. The second part describes the controller model, selection of optimization objective functions, and optimization of constraint conditions. The optimization of controllers is tuned by simulation using identified state-space model. Subsequently, the optimization procedure is implemented on the identified model, and the found control parameters are used on a real machine tool linear axis with different loads. The implementation of the proposed complex procedure on a real horizontal machine tool proved the advantage of simultaneous tuning of all parameters using optimization methods. The strategy solves the problem of mutual interaction of all control law parameters disabling effective usability of gradual sequential tuning. The methodology was developed on a speed control loop, the tuning of which is usually the most difficult due to the close interaction with the dynamic properties of the machine mechanics. The whole procedure is also applicable to the position and current control loop.

Speed control of DC motors is essential for automated vehicles and four-wheel differential drive (4WD) cars, which are distinct by their high level of maneuverability. The PID controller is one of the most popular techniques for controlling speed, but tuning its parameters is challenging. This paper presents a novel hybrid algorithm, the Moth-Flame Particle Swarm Optimization (MFPSO), which combines moth-flame optimization (MFO) and particle swarm optimization (PSO) to address the slow convergence of MFO and the premature convergence of PSO. The MFPSO is deployed for real-time interactive tuning of the PID controller to control the speed of DC motors in a 4WD car. Additionally, a novel practical procedure is proposed to build a robust four-wheel differential drive and maintain the synchronization of the four DC motors. Simulation results and statistical analysis demonstrate the superior performance of the MFPSO compared with the PSO, MFO, and other hybrid variants (HMFPSO and HyMFPSO), with MFPSO ranking first in the Friedman test on CEC2020/2021 and engineering optimization benchmark problems. Practical results and the transient response analysis of the speed control revealed that MFPSO significantly outperformed the traditional Ziegler-Nichols (ZN) method, MFO, PSO, HMFPSO, and HyMFPSO algorithms. Specifically, the MFPSO algorithm reduced settling time by 34.83%, 21.20%, 20.75%, 22.97%, and 31.59%, and overshoot by 86.11%, 64.99%, 71.02%, 74.37%, and 60.58% compared to the ZN, MFO, PSO, HMFPSO, and HyMFPSO algorithms, respectively. The source code of the proposed algorithm is available at https://github.com/MohamedRedaMu/MFPSO-Algorithm .

Hydro-viscous clutch has become an inevitable choice for special vehicle transmissions. As a nonlinear dynamic system with large lagging link, its timing performance is affected by input rotational speed, lubricating oil temperature and pressure and other factors. However, from the control perspective, the speed regulation law, formation mechanism control characteristics, global model of hydro-viscous speed control system (HSCS) are unclear. To solve these problems, this paper presents a comprehensive analysis of the Hydro-viscous Speed Control System (HSCS), focusing on its steady-state and dynamic speed control characteristics. A data-driven model is established to describe the relationship between input rotational speed, output speed, control oil pressure, and lubricating oil temperature. The findings provide a foundation for optimizing HSCS structure and parameters, enhancing the performance and reliability of such systems in special vehicle transmissions and establishing a temperature-speed control system for special vehicle transmissions.

The winch’s performance under complex sea conditions is significantly influenced by its collecting and releasing processes. To enhance its performance and reliability, an optimization approach considering wave disturbances and control laws is proposed to balance time efficiency and tension stability. Within a multiobjective optimization framework, the method designs constant tension control and robust adaptive speed control and introduces sinusoidal acceleration trajectories to minimize tension surges and reduce system impacts caused by rapid starts/stops. The constant tension controller reduces wave disturbances, while the speed controller manages the working process. These controllers are designed with unknown reference signals determined during the optimization process. Additionally, the objective functions in the optimization phase aim to reduce working time and tension fluctuations, with constraints ensuring system safety and mission requirements. Furthermore, an experimental platform constructed on a ship validates the accuracy of the winch model. The optimized process not only shortens operational time, as collecting same length only consumption 127.44 s compared 143.14 s without optimization, but also reduces tension and acceleration. More importantly, transitions between states become more gradual. This indicates that the proposed method is both time‐efficient and effective in dampening tension fluctuations and mitigating the effects of abrupt changes during the working process.

This paper presents an Event-B model of a speed control system, a part of the case study provided in the ABZ2020 conference. The case study describes how the system regulates the current speed of a car according to a set of criteria like the driver’s desired speed, the position of a possible preceding vehicle, but also a given speed limit that the driver must not exceed. For that purpose, this controller reads different information from the available sensors (key state, desired speed) and takes adequate actions by acting on the actuators of the car’s speed according to the information read. To formally model this system, we adopt a stepwise refinement approach with the Event-B method. We consider most of the features of the case study. All proof obligations of the invariant properties have been discharged using the Rodin provers. Our model has been validated using ProB by applying the different provided scenarios. This validation has permitted us to point out and correct some mistakes, ambiguities and oversights contained in the first versions of the case study.

The effective command of the mechanical and electrical components of a wind turbine is essential to secure optimal efficiency and stability of the system. This article aims to present a novel Nonlinear Autoregressive Neural Network (NARNN) strategy for controlling the electrical aspect of a system employing a Doubly-Fed Induction Generator (DFIG). The control strategy is designed to regulate active and reactive power in order to optimise energy production. To generate a reference power signal, rotor speed control is implemented in the mechanical part of the system. The results provided by the presented NARNN control strategy are then compared with those obtained from the reference Proportional Integral (PI) controller.