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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.

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.

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.

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.

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 .

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 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.

Airline schedules are generally tight and fragile to disruptions. Disruptions can have severe effects on existing aircraft routings, crew pairings, and passenger itineraries that lead to high delay and recovery costs. A recovery approach should integrate the recovery decisions for all entities (aircraft, crew, passengers) in the system as recovery decisions about an entity directly affect the others’ schedules. Because of the size of airline flight networks and the requirement for quick recovery decisions, the integrated airline recovery problem is highly complex. In the past decade, an increasing effort has been made to integrate passenger and crew related recovery decisions with aircraft recovery decisions both in practice and in the literature. In this paper, we develop a new flight network based representation for the integrated airline recovery problem. Our approach is based on the flow of each aircraft, crew member, and passenger through the flight network of the airline. The proposed network structure allows common recovery decisions such as departure delays, aircraft/crew rerouting, passenger reaccommodation, ticket cancellations, and flight cancellations. Furthermore, we can implement aircraft cruise speed (flight time) decisions on the flight network. For the integrated airline recovery problem defined over this network, we propose a conic quadratic mixed integer programming formulation that can be solved in reasonable CPU times for practical size instances. Moreover, we place a special emphasis on passenger recovery. In addition to aggregation and approximation methods, our model allows explicit modeling of passengers and evaluating a more realistic measure of passenger delay costs. Finally, we propose methods based on the proposed network representation to control the problem size and to deal with large airline networks.

Hydro-viscous clutch has already become an inevitable choice for special vehicle transmission in the present and future. As a nonlinear system with a large hysteresis loop, its speed control performance is affected by input rotational speed, lubricating oil temperature, lubrication pressure, and other factors. The traditional control method cannot adjust the temperature and rotational speed, which will lead to problems of narrow speed range, poor rotational speed stability, and large dynamic load impact. In order to solve the above problems, this paper studies the control method of an integrated multi-parameter hydro-viscous speed control system (HSCS) in a controlled environment. Through the mechanism analysis of the law of HSCS, the influence law of speed and temperature during the system operation is found. The temperature closed loop based on model predictive control (MPC) is introduced to control the rotational speed, and then the traditional PID control results are compensated according to the speed closed loop. Next, a novel double closed loop control method of temperature and rotational speed for HSCS is formed. Finally, the simulating verification is carried out. Compared with the traditional control method, the design method in this paper can adjust the control parameters according to the temperature of the lubricating oil and the input rotational speed and effectively expand the domain of HSCS and the speed control stability. The effective transmission ratio is extended to 0.2~0.8, and the hydro-viscous torque and speed fluctuation under the engine rotational speed fluctuation are reduced by more than 30%. The novel control method of HSCS designed in this paper can effectively improve the influence of input rotational speed and lubricating oil temperature on the speed control performance of HSCS and can be widely used in nonlinear HSCS such as hydro-viscous clutch.

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.