43 research outputs found
Practical Modeling and Comprehensive System Identification of a BLDC Motor
The aim of this paper is to outline all the steps in a rigorous and simple procedure for system identification of BLDC motor. A practical mathematical model for identification is derived. Frequency domain identification techniques and time domain estimation method are combined to obtain the unknown parameters. The methods in time domain are founded on the least squares approximation method and a disturbance observer. Only the availability of experimental data for rotor speed and armature current are required for identification. The proposed identification method is systematically investigated, and the final identified model is validated by experimental results performed on a typical BLDC motor in UAV
Flight Control Development and Test for an Unconventional VTOL UAV
This chapter deals with the control system development and flight test for an unconventional flight vehicle, namely, a tandem ducted-fan experimental flying platform. The first-principle modeling approach combined with the frequency system identification has been adopted to obtain a high-fidelity dynamics model. It is inherently less stable and difficult to control. To accomplish the required practical flight tasks, the flying vehicle needs to work well even in windy conditions. Moreover, for flight control engineers, simple prescribed multi-loop controller structures are preferred. To handle the multiple problems, a structured velocity controller consisting of two feedback loops is developed, where inner loop provides stability augmentation and decoupling, and the outer loop guarantees desired velocity tracking performance. The simultaneous design of the two-loop controllers under multiple performance requirements in the usual H∞ metrics can be cast as a nonsmooth optimization program. To compensate for changes in plant dynamics across the flight envelope, a smooth and compact polynomial scheduling formula is implemented as a function of the forward flight speed. Both simulations and flight test results have been presented in this work to showcase the potential for the proposed robust nonlinear control system to optimize the performance of UAV, specifically unconventional vehicles
Mode Shift Control for a Hybrid Heavy-Duty Vehicle with Power-Split Transmission
Given that power-split transmission (PST) is considered to be a major powertrain technology for hybrid heavy-duty vehicles (HDVs), the development and application of PST in the HDVs make mode shift control an essential aspect of powertrain system design. This paper presents a shift schedule design and torque control strategy for a hybrid HDV with PST during mode shift, intended to reduce the output torque variation and improve the shift quality (SQ). Firstly, detailed dynamic models of the hybrid HDV are developed to analyze the mode shift characteristics. Then, a gear shift schedule calculation method including a dynamic shift schedule and an economic shift schedule is provided. Based on the dynamic models and the designed shift schedule, a mode shift performance simulator is built using MATLAB/Simulink, and simulations are carried out. Through analysis of the dynamic equations, it is seen that the inertia torques of the motor–generator lead to the occurrence of transition torque. To avoid the unwanted transition torque, we use a mode shift control strategy that coordinates the motor–generator torque to compensate for the transition torque. The simulation and experimental results demonstrate that the output torque variation during mode shift is effectively reduced by the proposed control strategy, thereby improving the SQ
A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing
The ability of robots to write Chinese strokes, which is recognized as a sophisticated task, involves complicated kinematic control algorithms. The conventional approaches for robotic writing of Chinese strokes often suffer from limited font generation methods, which limits the ability of robots to perform high-quality writing. This paper instead proposes a developmental evolutionary learning framework that enables a robot to learn to write fundamental Chinese strokes. The framework first considers the learning process of robotic writing as an evolutionary easy-to-difficult procedure. Then, a developmental learning mechanism called “Lift-constraint, act and saturate” that stems from developmental robotics is used to determine how the robot learns tasks ranging from simple to difficult by building on the learning results from the easy tasks. The developmental constraints, which include altitude adjustments, number of mutation points, and stroke trajectory points, determine the learning complexity of robot writing. The developmental algorithm divides the evolutionary procedure into three developmental learning stages. In each stage, the stroke trajectory points gradually increase, while the number of mutation points and adjustment altitudes gradually decrease, allowing the learning difficulties involved in these three stages to be categorized as easy, medium, and difficult. Our robot starts with an easy learning task and then gradually progresses to the medium and difficult tasks. Under various developmental constraint setups in each stage, the robot applies an evolutionary algorithm to handle the basic shapes of the Chinese strokes and eventually acquires the ability to write with good quality. The experimental results demonstrate that the proposed framework allows a calligraphic robot to gradually learn to write five fundamental Chinese strokes and also reveal a developmental pattern similar to that of humans. Compared to an evolutionary algorithm without the developmental mechanism, the proposed framework achieves good writing quality more rapidly
6G Network AI Architecture for Everyone-Centric Customized Services
Mobile communication standards were developed for enhancing transmission and
network performance by using more radio resources and improving spectrum and
energy efficiency. How to effectively address diverse user requirements and
guarantee everyone's Quality of Experience (QoE) remains an open problem. The
Sixth Generation (6G) mobile systems will solve this problem by utilizing
heterogenous network resources and pervasive intelligence to support
everyone-centric customized services anywhere and anytime. In this article, we
first coin the concept of Service Requirement Zone (SRZ) on the user side to
characterize and visualize the integrated service requirements and preferences
of specific tasks of individual users. On the system side, we further introduce
the concept of User Satisfaction Ratio (USR) to evaluate the system's overall
service ability of satisfying a variety of tasks with different SRZs. Then, we
propose a network Artificial Intelligence (AI) architecture with integrated
network resources and pervasive AI capabilities for supporting customized
services with guaranteed QoEs. Finally, extensive simulations show that the
proposed network AI architecture can consistently offer a higher USR
performance than the cloud AI and edge AI architectures with respect to
different task scheduling algorithms, random service requirements, and dynamic
network conditions
Spin-orbit-coupled triangular-lattice spin liquid in rare-earth chalcogenides
Spin-orbit coupling is an important ingredient in many spin liquid candidate
materials, especially among the rare-earth magnets and Kitaev materials. We
explore the rare-earth chalcogenides NaYbS where the Yb ions form a
perfect triangular lattice. Unlike its isostructural counterpart YbMgGaO
and the kagom\'{e} lattice herbertsmithite, this material does not have any
site disorders both in magnetic and non-magnetic sites. We carried out the
thermodynamic and inelastic neutron scattering measurements. The magnetic
dynamics could be observed with a broad gapless excitation band up to 1.0 meV
at 50 mK and 0 T, no static long-range magnetic ordering is detected down to 50
mK. We discuss the possibility of Dirac spin liquid for NaYbS. We identify
the experimental signatures of field-induced transitions from the disordered
spin liquid to an ordered antiferromagnet with an excitation gap at finite
magnetic fields and discuss this result with our Monte Carlo calculation of the
proposed spin model. Our findings could inspire further interests in the
spin-orbit-coupled spin liquids and the magnetic ordering transition from them
A New Topology and Control Strategy for a Hybrid Battery-Ultracapacitor Energy Storage System
This study investigates a new hybrid energy storage system (HESS), which consists of a battery bank and an ultra-capacitor (UC) bank, and a control strategy for this system. The proposed topology uses a bi-directional DC-DC converter with a lower power rating than those used in the traditional HESS topology. The proposed HESS has four operating modes, and the proposed control strategy chooses the appropriate operating mode and regulates the distribution of power between the battery bank and the UC bank. Additionally, the control system prevents surges during mode switching and ensures that both the battery bank and the bi-directional DC-DC converter operate within their power limits. The proposed HESS is used to improve the performance of an existing power-split hybrid electric vehicle (HEV). A method for calculating the parameters of the proposed HESS is presented. A simulation model of the proposed HESS and control strategy was developed, and a scaled-down experimental platform was constructed. The results of the simulations and the experiments provide strong evidence for the feasibility of the proposed topology and the control strategy. The performance of the HESS is not influenced by the power limits of the bi-directional DC-DC converter