For the dual three-phase interior permanent magnet synchronous machines (DT-IPMSMs), open-phase fault can cause current unbalance and degrade the drive performance. This article proposes a fault tolerant maximum torque per ampere (FT-MTPA) control for DT-IPMSMs, which can maximize the ratio of the average torque to the stator current and minimize the fault-induced torque ripple. In the proposed approach, optimal FT-MTPA solution is derived and theoretically proven, and current rms is considered as one design constraint to ensure the equivalent loss to the healthy condition. The proposed FT-MTPA control ensures the smooth switching between fault tolerant control and healthy control without inducing noticeable torque ripple. Compared with existing methods, the proposed approach is computation-efficient and fast in achieving the FT-MTPA control, which is critical to practical applications with fast changing loads. Moreover, this article derives the optimal solution from the faulty MTPA model with fault induced terms considered to ensure high torque. Experiments and comparisons with existing methods are conducted to evaluate the proposed approach on a laboratory DT-IPMSM.

This work presents a novel design framework of adaptive iterative learning control (ILC) approach for a class of uncertain nonlinear systems. By using the closed-loop reference model that can be viewed as an observer, the proposed adaptive ILC approach can be adapted to deal with the output tracking problem of nonlinear systems with unavailable system states. In the systems considered, two classes of uncertainties are taken into account, including parametric input disturbances and input distribution uncertainties. To facilitate the controller design and convergence analysis, the composite energy function (CEF) methodology is employed. The design framework in this brief is novel and widely applicable, which extends the CEF-based ILC approach to output tracking control of nonlinear systems without requiring full knowledge of state information and complicated observer design process. To show the effectiveness of the proposed design framework and control algorithms, two numerical examples are illustrated.

The complex network has attracted increasing attention and shown effectiveness in modeling multifarious systems. Focusing on selecting members with good spreading ability, the influence maximization problem is of great significance in network-based information diffusion tasks. Plenty of attention has been paid to simulating the diffusion process and choosing influential seeds. However, errors and attacks typically threaten the normal function of networked systems, and few studies have considered the influence maximization problem under structural failures. Therefore, a quantitative measure with a changeable parameter is first developed in this paper to tackle the unpredictable destruction percentage on networks. Further, limitations on the existing methods are shown experimentally. To address these limitations, the evolutionary multitasking paradigm is employed, and several problem-specific operators are developed. On top of these developments, a multi-factorial evolutionary algorithm is devised to find seeds with robust influence ability, termed MFEARIM, where the genetic information for both myopia and holistic areas is considered to improve the search ability. Additionally, an asynchronous strategy is designed to efficiently tackle tasks with distinct costs, and the convergence of the search process can thus be accelerated. Experiments on several synthetic and real-world networks validate the competitive performance of MFEARIM over the existing methods.

For dual three-phase permanent magnet synchronous machines (DT-PMSMs) under open-phase fault, the fault tolerant currents are often unbalanced to generate smooth torque, which results in the peak current and copper loss of certain phases and per phase loss being significantly larger than healthy conditions. This can limit the maximum attainable torque and lead to overheating issue of the phases with larger currents. However, this is not fully addressed especially for salient PMSM. This article studies open-phase fault tolerant control (FTC) of salient DT-PMSM by considering the peak phase current limit to reduce the maximum per phase loss and improve the ratio of torque to total loss (TTL), resulting in improved maximum attainable torque. Analyses are first conducted to show the motivation. The proposed FTC aims to maximize the ratio of TTL and minimize peak phase current and fault-induced ripples without introducing phase current harmonics. Moreover, the current distribution affecting reluctance torque is optimized to improve the maximum torque considering saliency. Analytical solutions are derived to achieve better computation-efficiency. Compared with existing methods, the proposed approach can achieve better TTL ratio, maximum per phase loss, peak/rms phase current and maximum attainable torque, which is validated with experiments and comparisons.

For permanent magnet synchronous machines (PMSMs), magnetic saturation is a key challenge to parameter estimation and saturation modeling using polynomials is popular with dq- axis inductances estimated independently. However, dq-axis inductances are mathematical terms derived from stator frame and are indeed correlated, which is not fully explored for improving estimation performance. Hence, this paper proposes a correlated inductance model to represent the relationship among dq- axis inductances and model their nonlinear variations with respect to stator currents due to magnetic saturation. The idea is to model the saturation in the stator frame and derive the correlated inductance model in the rotating frame. The estimation model is established based on the correlated inductance model, in which dq- axis inductances are represented using the same components and the correlation can be fully explored to improve the estimation accuracy. Moreover, the proposed estimation approach does not involve the division by the dq- axis currents, which can ensure the estimation accuracy especially under conditions with small current magnitude. While existing methods often involve the division and result in limited accuracy under such conditions. The proposed approach is evaluated on two test machines and compared with existing methods to demonstrate the performance improvement.

Robotics research is motivated by the desire to develop tools or machines to accomplish tasks in place of humans or undertake tough activities which cannot be done by human labour. In order to design advanced robots, one approach is the learn from mechanical structures and locomotion mechanisms of animals in the natural world. This survey focuses on legged dynamic locomotion of robots which are mammal-like and mammal sized. The paper shows that there are many different paradigms and platforms used for walking, running and hopping robot research. In order to present a general view of the dynamic locomotion of legged robots, the paper starts with introducing a brief history of this research area. Then balancing issues for robots which are walking or running are discussed. To help address the limitations of previous research, the last part will identify the concept of `agile running'. Agile running involves developing control techniques and algorithms to allow a running robot to recover from disturbances caused by rough, slippery, shifting or uncertain terrain and rapidly execute desired changes in the direction and stride length.