In recent years, generative transformers have become increasingly prevalent in the field of artificial intelligence, especially within the scope of natural language processing. This paper provides a comprehensive overview of these models, beginning with the foundational theories introduced by Alan Turing and extending to contemporary generative transformer architectures. The manuscript serves as a review, historical account, and tutorial, aiming to offer a thorough understanding of the models’ importance, underlying principles, and wide-ranging applications. The tutorial section includes a practical guide for constructing a basic generative transformer model. Additionally, the paper addresses the challenges, ethical implications, and future directions in the study of generative models.

In a heating, ventilation, and air conditioning system, an air-handling system is a key module. Its components (e.g., air handling unit, air-mixing box, and fans), linked through airflows, condition air to a desired temperature and/or humidity based on comfort or controlled environment requirements. Identifying failure modes and estimating their severities allow maintenance crews to know which faults have occurred, how critical they are, and be guided in the repair process to improve the system availability. The problem of fault detection and diagnosis in air-handling systems is complex because of fault propagation across components, and high false alarm rates caused by uncertainties in system and measurement dynamics. In this paper, to capture fault propagation impacts in an efficient manner, dynamic hidden Markov models are developed to identify failure modes, since they contain state transition matrices depending on other components and do not generate joint states. To filter out false alarms, “coupled statistical process control” techniques are developed by using state transitions matrices representing coupling among components. Experimental results show that the method can effectively diagnose faults with high-diagnosis accuracy. Note to Practitioners-Faults in heating, ventilation, and air conditioning air handling units (AHU) may cause high energy consumption and discomfort to occupants. Fault diagnosis in AHU is challenging since: 1) effects of faults propagate across components connected by airflows and 2) measurement noises may cause high false alarm rates. In this paper, a novel fault diagnosis method is established to identify failure modes and fault severities. This method explicitly considers the fault coupling among components. To reduce false alarm rates, new statistical process control techniques are developed to filter out false alarms. Experimental results show that our method can effectively diagnose faults with high diagnosis accuracy.

In this paper, a synthetic sensorless starting scheme, which is based on pulse injection technology, is proposed for switched reluctance motor (SRM) drives to enhance the starting reliability. This scheme consists of a set of sensorless position estimation algorithms for different stages of SRM starting. In order to start the SRM from standstill stage smoothly, the initial driving phase is selected by pairwise comparing of the three-phase inductance. Then, to ensure reliable starting acceleration, a double inductance dynamic thresholds algorithm is developed for estimating the direct phase-driving signals. Meanwhile, to estimate the real-time rotor position, a position index pulse estimation algorithm, which is with phase lacking fault-tolerant capability, is developed based on an idle phase inductance threshold. By seamless switching between the above mentioned two algorithms, higher reliability of the sensorless starting control can be achieved. Furthermore, the performance of three pulse injection methods are compared, and the best one is then selected to reduce the influences of the high frequency pulse signals in the idle phases. The theoretical analysis and experimental results are provided to verify the feasibility of the proposed control scheme.

Model predictive control (MPC) is an advanced control technique with salient features, such as simplicity applied in multivariable systems, fast-transient response, inclusion of nonlinearities, and straightforward constraints in the control law, which is attracted in applying for high-performance control of motor drives. The electromagnetic characteristics of the switched reluctance motor (SRM) are of highly nonlinearity, which may result in lower control accuracy, slow stabilization time of control variables, unsatisfactory dynamic response, and torque ripples elimination performance. This article presents an overview of the current finite control set and continuous control set based MPC strategies in SRM drives. The model predictive current control, torque control, and flux control are analyzed from perspectives of modeling schemes, switching vectors optimization approaches, cost function selections, and the control performance. And finally, the current challenges and future development trends in applying the MPC technologies in SRM drives are also discussed.

The phase-locked loop (PLL) algorithm uses the external input reference signal to control the frequency and phase of the internal oscillation signal of the loop, thus the control signal and the internal signal can be synchronized to achieve the purpose of phase locking. This algorithm has been widely used in the sensorless position estimation technology of permanent magnet synchronous motor (PMSM) drives. The PLL can eliminate the steady-state error effectively and achieve better phase tracking accuracy. And furthermore, its low-pass filtering characteristics can effectively filter the high frequency harmonics and chattering signals of the estimated back emotional force (BEMF) and flux linkages. Thus, by utilizing the PLL algorithm, the position estimation accuracy in PMSM can be highly improved. In this paper, the existing PLL algorithms for sensorless PMSM control are reviewed systematically. Furthermore, the key challenges and opportunities for future research are identified.