High-Order Sliding Mode Block Control of Single-Phase Induction Motor

A new sliding mode (SM) observer-based controller for single-phase induction motor is designed. The proposed control scheme is formulated using block control feedback linearization technique and high-order SM algorithms with measurements of the rotor speed and stator currents. The stability of the complete closed-loop system, including the rotor flux second-order SM observer, is analyzed in the presence of model uncertainty, namely, rotor resistance variation and bounded timevarying load torque.CINVESTA

Design and Control of Electrical Motor Drives

Dear Colleagues, I am very happy to have this Special Issue of the journal Energies on the topic of Design and Control of Electrical Motor Drives published. Electrical motor drives are widely used in the industry, automation, transportation, and home appliances. Indeed, rolling mills, machine tools, high-speed trains, subway systems, elevators, electric vehicles, air conditioners, all depend on electrical motor drives.However, the production of effective and practical motors and drives requires flexibility in the regulation of current, torque, flux, acceleration, position, and speed. Without proper modeling, drive, and control, these motor drive systems cannot function effectively.To address these issues, we need to focus on the design, modeling, drive, and control of different types of motors, such as induction motors, permanent magnet synchronous motors, brushless DC motors, DC motors, synchronous reluctance motors, switched reluctance motors, flux-switching motors, linear motors, and step motors.Therefore, relevant research topics in this field of study include modeling electrical motor drives, both in transient and in steady-state, and designing control methods based on novel control strategies (e.g., PI controllers, fuzzy logic controllers, neural network controllers, predictive controllers, adaptive controllers, nonlinear controllers, etc.), with particular attention to transient responses, load disturbances, fault tolerance, and multi-motor drive techniques. This Special Issue include original contributions regarding recent developments and ideas in motor design, motor drive, and motor control. The topics include motor design, field-oriented control, torque control, reliability improvement, advanced controllers for motor drive systems, DSP-based sensorless motor drive systems, high-performance motor drive systems, high-efficiency motor drive systems, and practical applications of motor drive systems. I want to sincerely thank authors, reviewers, and staff members for their time and efforts. Prof. Dr. Tian-Hua Liu Guest Edito

An Adaptive Controller Design for Flexible-joint Electrically-driven Robots With Consideration of Time-Varying Uncertainties

Almost all present control strategies for electrically-driven robots are under the rigid robot assumption. Few results can be found for the control of electrically driven robots with joint flexibility. This is because the presence of the joint flexibility greatly increases the complexity of the system dynamics. What is worse is when some system dynamics are not available and a good performance controller is required. In this paper, an adaptive design is proposed to this challenging problem. A backstepping-like procedure incorporating the model reference adaptive control is employed to circumvent the difficulty introduced by its cascade structure and various uncertainties. A Lyapunov-like analysis is used to justify the closed-loop stability and boundedness of internal signals. Moreover, the upper bounds of tracking errors in the transient state are also derived. Computer simulation results are presented to demonstrate the usefulness of the proposed scheme. Keywords: Adaptive control; Flexible-joint electrically-driven robot; FAT 2. Introduction Control of rigid robots has been well understood in recent years, but most of the schemes ignore the dynamics coming from electric motors and harmonic drivers that are widely implemented in the industrial robots. However, actuator dynamics constitute an important part of the complete robot dynamics, especially in the cases of high-velocity movement and highly varying loads[1],[2]. The main reason for using a reduced model is to simplify complexity of controller design. For each joint, consideration of the flexibility from the M. C. Chien was with the Department of Mechanical Engineering, National Taiwan University of Science and Technology. He is now with the Mechanical and Systems Research Laboratories, Industrial Technology Research Institute, No. 195, Sec. 4, Chung-Hsing Rd., Chutung, Hsinchu, 310, Taiwan, R.O.C. (e-mail: [email protected]). 2 A. C. Huang is with the Department of Mechanical Engineering, National Taiwan University of Science and Technology. No. 43, Keelung Rd., Sec. 4, Taipei, Taiwan, ROC. (Tel:+886-2-27376490, Fax: +886-2-37376460, E-mail: [email protected]). (A. C. Huang provides phone number because he is the corresponding author.

Energy management in biodiesel production

Biodiesel economics revolves around both private and public features. Success for an incentive for producers and consumers needs to be sufficient to take part in the marketing arena with the currently accessible alternatives in the private dimension. “Whereas in the civic aspect, success is described in terms of the community objectives that prompt government’s intercession (i. e. subsidies) and whether biodiesel will attain the public goals in a remunerative manner compared to other alternatives if the public assessment is highly preferred, but the market incentives are deficient, there will not be a possibility to produce the biodiesel. On the other hand, if market values generate strong dividends among producers and purchaser, but the social gains are very small, or the costs are extremely high compared to other alternatives, then the activity may not represent a better use of scarce public resources. Biodiesel has become more enticing to the world recently due to its environmental benefits and the fact that it is made from renewable resources. The production cost of biodiesel is the main barrier to commercialization of the product. In the absence of tax relief, there is an urgent need to explore alternative feedstocks for the production of biodiesel. Furthermore, Properties impact of biodiesel and its feedstock, Engine type and its operating conditions Additives are effected by the biodiesel type and production characteristics. Where Engine power is always affected by properties of biodiesel, especially in heating value, viscosity, and lubricity

Synchronous control of double-containers for overhead crane

The development and wide application of double spreaders overhead cranes have effectively improved the loading and unloading efficiency of the container terminals. However, due to the nonlinear time-varying characteristics and parameter perturbation of the lifting device of the double spreaders, the difficulty of synchronous and coordinated control of the double spreader overhead crane is increased. In order to solve the problem of synchronous control of double spreaders overhead cranes, this work establishes the mathematical model of the double spreaders overhead crane and proposes two main methods. The controller based on the fuzzy sliding mode method is established. Fuzzy logic control can effective estimate the parameters of the system, reduce the chattering of sliding mode control, and improve the performance of its control. Mean deviation coupling synchronization control combined with sliding mode control can effectively control the speed error between the two spreaders, so that they can keep working synchronously. The other controller is established which use fast non-singular terminal sliding mode control to ensure that the system can converge in a finite time. The combination of terminal sliding mode control and super twisting algorithm can enhance the stability of the system.O desenvolvimento e a vasta aplicação de pontes rolantes de duplo espalhamento tem melhorado a eficiência de carga e descarga dos terminais de contentores. No entanto devido ao facto das variações não lineares do tempo e a perturbação dos parâmetros do dispositivo de elevação de duplo espalhamento, é dificultado o controlo sincronizado e coordenado. Com o objetivo de resolver o problema do controlo síncrono das pontes rolantes de duplo espalhamento, este projeto usa o modelo matemático do guindaste de dupla propagação e propõe dois métodos de resolução. O controlo baseado no método do modo deslizante difuso. O controlo lógico difuso pode estimar eficazmente os parâmetros do sistema, reduzir a vibração do controlo do modo deslizante e melhorar o seu desempenho. O control de sincronização do acoplamento do desvio médio, combinado com o control do modo deslizante que pode controlar eficazmente o erro de velocidade entre os dois espalhadores, para que o seu trabalho possa continuar de forma síncrona. O outro controlador usa um controlo rápido e não singular do modo de deslizamento do terminal para garantir que o sistema possa convergir num tempo limitado. A combinação do control no modo deslizante do terminal e do algoritmo de super rotação pode melhorar a estabilidade do sistema

Robust fractional order PI control for cardiac output stabilisation

Drug regulatory paradigms are dependent on the hemodynamic system as it serves to distribute and clear the drug in/from the body. While focusing on the objective of the drug paradigm at hand, it is important to maintain stable hemodynamic variables. In this work, a biomedical application requiring robust control properties has been used to illustrate the potential of an autotuning method, referred to as the fractional order robust autotuner. The method is an extension of a previously presented autotuning principle and produces controllers which are robust to system gain variations. The feature of automatic tuning of controller parameters can be of great use for data-driven adaptation during intra-patient variability conditions. Fractional order PI/PD controllers are generalizations of the well-known PI/PD controllers that exhibit an extra parameter usually used to enhance the robustness of the closed loop system. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

Adaptive PI Hermite neural control for MIMO uncertain nonlinear systems

[[abstract]]This paper presents an adaptive PI Hermite neural control (APIHNC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The proposed APIHNC system is composed of a neural controller and a robust compensator. The neural controller uses a three-layer Hermite neural network (HNN) to online mimic an ideal controller and the robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Moreover, a proportional–integral learning algorithm is derived to speed up the convergence of the tracking error. Finally, the proposed APIHNC system is applied to an inverted double pendulums and a two-link robotic manipulator. Simulation results verify that the proposed APIHNC system can achieve high-precision tracking performance. It should be emphasized that the proposed APIHNC system is clearly and easily used for real-time applications.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Unintrusive Monitoring of Induction Motors Bearings via Deep Learning on Stator Currents

Induction motors are fundamental components of several modern automation system, and they are one of the central pivot of the developing e-mobility era. The most vulnerable parts of an induction motor are the bearings, the stator winding and the rotor bars. Consequently, monitoring and maintaining them during operations is vital. In this work, authors propose an Induction Motors bearings monitoring tool which leverages on stator currents signals processed with a Deep Learning architecture. Differently from the state-of-the-art approaches which exploit vibration signals, collected by easily damageable and intrusive vibration probes, the stator currents signals are already commonly available, or easily and unintrusively collectable. Moreover, instead of using now-classical data-driven models, authors exploit a Deep Learning architecture able to extract from the stator current signal a compact and expressive representation of the bearings state, ultimately providing a bearing fault detection system. In order to estimate the effectiveness of the proposal, authors collected a series of data from an inverter-fed motor mounting different artificially damaged bearings. Results show that the proposed approach provides a promising and effective yet simple bearing fault detection system