Robust Multiobjective and Multidisciplinary Design Optimization of Electrical Drive Systems

Design and optimization of electrical drive systems often involve simultaneous consideration of multiple objectives that usually contradict to each other and multiple disciplines that normally coupled to each other.This paper aims to present efficient system-level multiobjective optimization methods for the multidisciplinary design optimization of electrical drive systems.From the perspective of quality control,deterministic and robust approaches will be investigated for the development of the optimization models for the proposed methods.Meanwhile,two approximation methods,Kriging model and Taylor expansion are employed to decrease the computation/simulation cost.To illustrate the advantages of the proposed methods,a drive system with a permanent magnet synchronous motor driven by a field oriented control system is investigated.Deterministic and robust Pareto optimal solutions are presented and compared in terms of several steady-state and dynamic performances(like average torque and speed overshoot)of the drive system.The robust multiobjective optimization method can produce optimal Pareto solutions with high manufacturing quality for the drive system.

Load Torque Compensator for Model Predictive Direct Current Control in High Power PMSM Drive Systems

The widely used cascade speed and torque controllers have a limited control performance in most high power applications due to the low switching frequency of power electronic converters and the convenience to avoid speed overshoots and oscillations for lifetime considerations. Model Predictive Direct Current Control （MPDCC） leads to an increase of torque control performance taking into account the discrete nature of inverters but temporary offsets and poor responses to load torque variations are still issues in speed control. A load torque estimator is proposed in this paper in order to further improve dynamic behavior. It compensates the load torque influence on the speed control setting a feed forward torque reference value. The benefits are twice; the speed controller reaches the speed reference value without offsets which would need to be compensated by an integrator and a better response to load torque variations is obtained since they are detected and compensated leading to small speed variations. Moreover, the influence of pararneter errors and disturbances has been analyzed and limited so that they play a minor role in operation.

Potential Driving Systems Associated with Extreme Rainfall across East Africa during October to December (OND) Season 2019

The East African (EA) region highly experiences intra-seasonal and inter-annual variation in rainfall amounts. This study investigates the driving factors for anomalous rainfall events observed during the season of October-November-December (OND) 2019 over the region. The study utilized daily rainfall data from Climate Hazards Group InfraRed Precipitation with Station Data Version 2 (CHIRPSv2) and the driving systems data. Statistical spatiotemporal analysis, correlation, and composite techniques were performed to investigate the teleconnection between OND 2019 seasonal rainfall and global synoptic climate systems. The findings showed that the OND 2019 experienced seasonal rainfall that was twice or greater than its seasonal climatology and varied with location. Further, the OND 2019 rainfall showed a positive correlation with the Indian Ocean Dipole (IOD) (0.81), Nino 3 (0.51), Nino 3.4 (0.47), Nino 4 (0.40), Pacific Decadal Oscillation (PDO) (0.22), and North Tropical Atlantic (NTA) (0.02), while El Nino-Southern Oscillation (ENSO) showed a negative correlation (−0.30). The region was dominated by southeasterly warming and humid winds that originated from the Indian Ocean, while the geopotential height, vertical velocity, and vorticity anomalies were closely related to the anomalous rainfall characteristics. The study deduced that the IOD was the major synoptic system that influenced maximum rainfall during the peak season of OND 2019. This study therefore provided insights on the diagnosis study of OND 2019 anomalous rainfall and its attribution over the EA. The findings of the study will contribute to improvements in forecasting seasonal rainfall by regional climate centers and national meteorological centers within the region.

Methodical Approach to Integrate Human Movement Diversity in Real-Time into a Virtual Test Field for Highly Automated Vehicle Systems

Recently, virtual realities and simulations play important roles in the development of automated driving functionalities. By an appropriate abstraction, they help to design, investigate and communicate real traffic scenario complexity. Especially, for edge cases investigations of interactions between vulnerable road users (VRU) and highly automated driving functions, valid virtual models are essential for the quality of results. The aim of this study is to measure, process and integrate real human movement behaviour into a virtual test environment for highly automated vehicle functionalities. The overall system consists of a georeferenced virtual city model and a vehicle dynamics model, including probabilistic sensor descriptions. By motion capture hardware, real humanoid behaviour is applied to a virtual human avatar in the test environment. Through retargeting methods, which enable the independency of avatar and person under test (PuT) dimensions, the virtual avatar diversity is increased. To verify the biomechanical behaviour of the virtual avatars, a qualitative study is performed, which funds on a representative movement sequence. The results confirm the functionality of the used methodology and enable PuT independence control of the virtual avatars in real-time.

Recent advances in traction drive technology for rail transit

The traction drive system is the“heart”of rail transit vehicles.The development of sustainable,secure,economic,reliable,efficient,and comfortable contemporary rail transportation has led to increasingly stringent requirements for traction drive systems.The interest in such systems is constantly growing,supported by advancements such as permanent magnet(PM)motors,advanced electronic devices such as those using silicon carbide(SiC),new-generation insulating materials such as organic silicon,and advanced magnetic materials such as rare-earth magnets and amorphous materials.Progress has also been made in control methods,manufacturing technology,artificial intelligence(AI),and other advanced technologies.In this paper,we briefly review the state-of-the-art critical global trends in rail transit traction drive technology in recent years.Potential areas for research and the main obstacles hindering the development of the next-generation rail transit traction drive systems are also discussed.Finally,we describe some advanced traction drive technologies used in actual engineering applications.

Application of networked discrete event system theory on intelligent transportation systems

The responses of vehicles to the changes in traffic situations inevitably have delays in observing an event and implementing a control command,which often causes fatal accidents.So far,the methods for handling delays are empirical and cannot be mathematically proven.To eliminate the accidents caused by such delays,in this paper,we develop mathematically provable methods to handle these delays.Specifically,we use networked discrete event systems to model the process of driving vehicles and present a supervisory controller for handling delay situations.The method developed in this paper could serve as a new start for modeling and controlling the responsive behaviors of self-driving vehicles in the future.

An analysis on possibility of intelligent speed adaptation in terms of drivers' consciousness

Intelligent speed adaptation （ISA） is considered as an effective measure to reduce number of traffic accidents in the field of intelligent transportation systems （ITS）. On the other hand, its effects for traffic safety are still doubted by many people. To make the possibility analysis, an experiment is conducted by using driving simulator. Regarding ISA ap- proaches, there are three modes： mandatory, voluntary and advisory. Among them, the advisory type seems to be the easiest one to introduce. Therefore, we focus on the advisory mode in this study by considering ISA just at the beginning stage in Japan. The experiment consists of four steps： without ISA, ISA using pictures, ISA using voices and again without ISA. The outputs obtained from the driving simulator are analyzed combined with the consciousness of the participants. The experiment shows that the ISA can improve recognition of speed limitation especially for people who have random rambling or looking aside tendency. Furthermore, the ISA especially when using voices can contribute in changing the consciousness of people who are aggressive in driving. Their driving speeds can reduce so that positive effects on traffic safety can be concluded.

Adaptive robust control for four-motor driving servo system with uncertain nonlinearities

A novel adaptive robust control （ARC） is presented for the four-motor driving servo systems with the uncertain nonlinearities and actuation failures, such that the load tracking control is achieved with the proximate optimal-time. By applying the proposed scheme, several control objectives are achieved. First, the nonlinear synchronization algorithm is presented to maintain the velocity synchronization of each motor, which provides fast convergence without chatting. Moreover, the time-varying bias torque is applied to eliminate the effect of backlash and reduce the waste of energy. Then, the ARC is designed to achieve the proximate optimal-time output tracking with the transient performance in L2 norm, where the friction and actuation failures are addressed by the adaptive scheme based on the norm estimation of unknown parameter vector. Finally, the extensive simulated and experimental results validate the effectiveness of the proposed method.