Electromagnetic launch system has characteristics of high acceleration, intense vibration shock, and strong electromagnetic interference, which may damage the position sensor and interfere with signal processing, thus affecting both safety and stability of the system. To solve the aforementioned problems and reduce the system cost, one position sensorless control method via a reduced-order observer based on the linear quadratic regulator and Kalman filter (LQR-KF) is proposed for the dual three-phase permanent-magnet linear synchronous motor (DTP-PMLSM) adapted to electromagnetic launch in this article. The fast solution of Kalman gain in the LQR-KF observer is completed by the LQR, which can eliminate the higher order matrix operations in sensorless control based on the traditional extended Kalman filter, and greatly simplifies the computational effort. Compared to the traditional method, the observer order in the new algorithm can be obviously reduced by variable substitution of the nonlinear system, and the observed accuracy can be greatly improved by eliminating the need for Taylor series approximation of the nonlinear system. Comprehensive field-circuit coupling simulation and experiments based on a 480-kW DTP-PMLSM have fully demonstrated the simplicity and accuracy of the proposed method in this article.

This paper proposes a novel ironless double-sided Halbach permanent magnet synchronous linear motor with C-shaped winding to satisfy the demanding operational confines of high-speed and high-thrust transient launch conditions, and a precise analytical model is presented for this type of motor that simultaneously considers the longitudinal end effects of the primary dual three-phase windings and secondary permanent magnets. The complete expressions for the air-gap flux density and electromagnetic thrust are derived in a modified Fourier series decomposition. The accuracy of the analytical model is verified through finite element simulation. Subsequently, an experimental prototype is developed and tested, confirming the accuracy of both the finite element simulation and the analytical model. Finally, validation is performed under launch conditions, demonstrating that the electromagnetic performance of the motor meets the requirements for non-periodic transient launch conditions.

In order to solve the electromagnetic force optimization problem of a high-power-density ironless Halbach-type permanent magnet synchronous linear motor, this paper adopts an electromagnetic force optimization method based on magnetic field analysis, electromagnetic force modeling, and genetic algorithm optimization: Firstly, the magnetic field of the Halbach permanent magnet array is solved by the combination of the equivalent magnetization strength method and the pseudo-periodic method, which takes into account the influence of the edge effect of the secondary magnetic field, and the magnetic field of the primary winding is solved by Fourier series expansion method. Secondly, the Maxwell tensor method is used to establish the functional relationship between the electromagnetic thrust and the main structural parameters of the unilateral motor. Finally, based on the parameter sensitivity analysis of the optimized variables and the response surface calculation, the optimal combination of the optimized variables to meet the optimization objective is found by a genetic algorithm. This method of the accurate modeling and optimization of an electromagnetic force can accurately calculate the motor air gap magnetic field and electromagnetic thrust, and the optimization speed is fast, which can greatly save time. The optimization results show that, under the premise of constant input power, the unilateral average thrust of the motor is increased by 18.75%, the peak value of thrust fluctuation is decreased by 30.27%, and the results match well with the finite element results, which verifies the correctness of the optimization results of the electromagnetic force and the reasonableness of the optimization method.

The aim of this study is to determine the electromagnetic field spatial distribution and longitudinal end effect characteristics of short primary linear induction motor (SPLIM) in the field of electromagnetic launch. First, in accordance to the electromagnetic model assumption of SPLIM and one‐dimensional electromagnetic field theory, the analytical expression of the whole‐region magnetic flux density of air‐gap is derived using electromagnetic field boundary conditions in line with the actual physical meaning. Second, the finite element method used in comparing the analytical calculation values of key physical quantities, such as air‐gap flux density and secondary eddy current in a multidimensional manner under various static and dynamic working conditions and verifying the correctness of the analytical calculation of the electromagnetic field. On this basis, the air‐gap flux density of SPLIM is decomposed according to the different generation mechanisms, and the spatial distribution of each part of the flux density is studied. Spatial Fourier analysis of flux density is conducted, the end effect coefficient is defined, and the change rule of the end effect coefficient with the design parameters of the motor is studied. Finally, a megawatt‐class high thrust prototype test platform was built, and the measured electromagnetic force was compared with the electromagnetic force calculated by air gap magnetic field and secondary eddy current. The steady‐state error of the two was less than 2%, which verified the accuracy of the analysis and calculation.

To ensure accurate closed-loop control of the transient motor system, the eddy current position sensors are generally adopted for obtaining motor operation signals. However, the transient operating characteristics of the motor will degrade accuracy and reliability of position sensors, and even worse result in abnormal operation. To address these issues, one improved adaptive speed observer with high accuracy is proposed in this work, thus ensuring the safe and stable operation of the dual three-phase permanent magnet linear synchronous motor (DTP-PMLSM) under the aperiodic transient operation. In this improved observer, an observation model is constructed that considers the effect of stochastic noise, and the expectation of the observation error is derived. In addition, the minimization of the expectation is achieved via a linear quadratic regulator (LQR), which can get higher observation accuracy with a simplified method. Moreover, a speed adaptive law is designed, which can dynamically adjust to changes in the system. Comprehensive simulations and experiments based on a 4kW prototype have fully demonstrated the effectiveness and the superiority of the improved observer.

Combinatorial optimization problems (COPs) offer essential mathematical frameworks and algorithmic foundations for modeling complex real-world decision-making tasks. Recent advances in deep reinforcement learning (DRL) have shown promising results for solving COPs, offering the potential to reduce dependence on domain-specific expertise and improve generalization across problem instances. These developments have accelerated research in the field and spurred the emergence of numerous innovative methods. Nevertheless, significant theoretical and practical challenges remain. A systematic synthesis of these challenges and their corresponding solutions is critical to guiding the future development of DRL-based approaches. To address this need, we propose a unified challenge-driven framework consisting of four core components: an environment, a state–action–reward mechanism, a solver, and an evaluation module. Using this framework, we conduct a systematic review of approximately 300 recent studies, mapping the evolution of challenges and the progress made in addressing them. We provide a multidimensional analysis of solver designs, training paradigms, and state-of-the-art (SOTA) performance, while documenting publicly available code repositories. Finally, we identify key open problems within the proposed framework to stimulate novel research directions.