Search

Active magnetic bearing (AMB) is a suspension system to levitate a rotating shaft freely without any physical contact which allows extremely fast rotation speeds. One big control challenge of the AMB systems, which appears during high rotation speeds, is the non-uniform distribution of the rotor weight about its rotating axis. This is usually referred to as the rotor imbalance problem which produces sinusoidal disturbance forces. These disturbances lead to undesirable vibrations and large deviations of the rotor shaft from its desired trajectories. We adopt in this work model predictive control (MPC) to reduce the effect of these sinusoidal disturbances and to achieve a stable levitation of the rotor shaft while tracking a reference trajectory. Owing to the MPC capability of handling constraints in an optimal manner, physical input constraints can be committed. Moreover, state constraints can be imposed to ensure safety of operation. For tractable implementation, we embed the nonlinear dynamics of the system in a linear parameter-varying (LPV) representation. To guarantee stability of the closed-loop system, a terminal cost and a terminal constraint set are included in the MPC optimization problem. For tractable computations of these terminal ingredients, a reduced LPV model is considered. The performance of the proposed LPV-MPC scheme is validated via simulation on the nonlinear model of an experimental setup of an AMB system and it is compared with two other classical controllers commonly used for these AMB systems in practice. [ABSTRACT FROM AUTHOR]

This paper provides design and task management of the first stage of a project entitled “Fall-Army Worm (FAW) insect killer”. The project has three main stages: design and energy management stage, flying control of drone stage and detecting and killing FAW stage. The goal of this project is to detect and kill; without chemical methods; a harmful FAW insect which is rapidly spreading in Africa and Asia. This paper focuses on design of the first stage of the system, getting maximum power and controlling the energy of the system. A Photo Voltaic system with energy storage devices is proposed to be the source of power. A new algorithm to control the time scheduling of the killing and detection mission is proposed and its effects on the system’s energy and mission period are studied. A comparison between different methods for tracking the maximum power from PV panels is performed to choose the best (less time and high accuracy) method. The results of simulation indicate the effectiveness of the proposed maximum power tracking and task management system.

Robust [H.sub.[infinity]] optimal control theory has proven to be one of the best techniques in linear control system design. The achievable robust stability and performance are high, but the resulting controllers are very complex and difficult to implement. As a result, few practical implementations of [H.sub.[infinity]] control can be found in the literature. This paper presents a robust [H.sub.[infinity]] controller for a two-degree-of-freedom magnetic micro-levitation positioner and its real time experimental implementation. The experimental device used in this study is designed for use in semiconductor manufacturing and consists of two U-shaped electromagnets and a manipulator. First, we describe the system dynamics in state space form. Second, the system which is unstable in nature is stabilized using the [H.sub.[infinity]] synthesis. The [H.sub.[infinity]] control design problem is described and formulated in the standard form with emphasis on the selection of weighting transfer functions that reflect robustness and performance goals. The interactive computing environment MATLAB is used to calculate the controller. Third, the controller is implemented digitally using a digital signal processor with 16 bit A/D and 12 bit D/A converters. Finally, some simulation and experimental results are presented. The results obtained show that robust stability against model uncertainties is achieved and the performance goals are satisfied.

Conical magnetic bearings with radial and thrust (axial) control are discussed. First a model of the conical bearing is made in state variable form. Airgap flux, airgap displacement, and velocity are used as state variables. A new control method is proposed, based on recent developments in control theory. The method is called Q-parameterization theory and is used to design a stabilizing controller for the system which, without control, is unstable in nature. The controller parameters are chosen using console (a software tandem for interactive optimization-based design developed at the University of Maryland in 1987) so that all design requirements are satisfied. Digital simulation was used to verify forced responses in all three directions (vertical, horizontal, and axial), and the interactions between them are discussed. Also, stiffness and whirling characteristics are obtained. The results obtained are satisfactory and suggest the robustness of the proposed technique.

This study presents a successful application of a model predictive control (MPC) design approach based on linear parameter-varying (LPV) models subject to input/output constraints to control a utility-scale wind turbine. The control objectives are to allow the wind turbine to extract from the wind the rated power taking into account the wind speed variation, to reduce mechanical loads and power fluctuations and to guarantee the stability of the system for the whole range of operation. A modified min--max MPC-LPV scheme is proposed to compute online the optimal control input at each sampling instant by solving an optimisation problem subject to linear matrix inequality constraints. To reduce the conservatism of the original MPC scheme due to the overbounding associated with affine parameter-dependence, the full block S-procedure with a linear fractional transformation formulation is used. The performance and the efficiency of the proposed MPC-LPV algorithm is validated via simulation and compared with the original scheme and other conventional controllers. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Sciences is the property of Faculty of Engineering - Assiut University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering Sciences is the property of Faculty of Engineering - Assiut University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper provides a design, a charging control, and energy management of a movable Photo Voltaic (PV) charging station with an Automatic Battery Replacement (ABR) system to enable drones for ongoing missions. The paper represents the first stage of a three-staged project titled Fall Armyworm (FAW) insect killer. The other two stages involve the flight control of drones and detecting and killing FAW insects. Without chemical methods, the project aims to eliminate harmful FAW insects that are rapidly spreading in Africa and Asia. The power source is a hybrid PV system with energy storage devices (batteries and supercapacitors). The maximum power from PV panels is tracked using three different online methods (PSO, IC, and P&O), and the best method with the highest accuracy is selected. The experimental and simulation results approved that PSO is the recommended method used in this project among the studied methods because of its high target reach (about 97%) and low steady-state oscillation (maximum 2.15%). An intelligent energy management system is investigated and designed to efficiently utilize solar power with a constant-current constant-voltage charger for LiPo batteries. A new Battery Selection System (BSS) is designed and verified to efficiently utilize the harvested energy and increase the mission time. The BSS targets to manage the selection of the appropriate battery to charge and control its charging rate. The system performance is tested using MATLAB software. Then, an experimental setup for the system is built to validate simulation results. The results of simulations and experiments proved the reliability of BSS in different operating cases with an efficiency higher than 97%. [ABSTRACT FROM AUTHOR]

The large number of interdigitated electrodes (IDEs) in a macro fiber composite (MFC) piezoelectric actuator dictates using a very fine finite element (FE) mesh that requires extremely large computational costs, especially with a large number of actuators. The situation becomes infeasible if repeated finite element simulations are required, as in control tasks. In this paper, an efficient technique is proposed for modeling MFC using a finite element method. The proposed technique replaces the MFC actuator with an equivalent simple monolithic piezoceramic actuator using two electrodes only, which dramatically reduces the computational costs. The proposed technique was proven theoretically since it generates the same electric field, strain, and displacement as the physical MFC. Then, it was validated with the detailed FE model using the actual number of IDEs, as well as with experimental tests using triaxial rosette strain gauges. The computational costs for the simplified model compared with the detailed model were dramatically reduced by about 74% for memory usage, 99% for result file size, and 98.6% for computational time. Furthermore, the experimental results successfully verified the proposed technique with good consistency. To show the effectiveness of the proposed technique, it was used to simulate a morphing wing covered almost entirely by MFCs with low computational cost. [ABSTRACT FROM AUTHOR]

In rural and remote areas, solar photovoltaic energy (PV) water pumping systems (SPWPSs) are being favored over diesel-powered water pumping due to environmental and economic considerations. PV is a clean source of electric energy offering low operational and maintenance cost. However, the direct-coupled SPWPS requires inventive solutions to improve the system's efficiency under solar power variations while producing the required amount of pumped water concurrently. This paper introduces a new quadratic V/f (Q V/f) control method to drive an induction motor powered directly from a solar PV source using a two-stage power converter without storage batteries. Conventional controllers usually employ linear V/f control, where the reference motor speed is derived from the PV input power and the dc-link voltage error using a simple proportional–integral (PI) controller. The proposed Q V/f-based system is compared with the conventional linear V/f control using a simulation case study under different operating conditions. The proposed controller expectedly enhances the system output power and efficiency, particularly under low levels of solar irradiance. Some alternative controllers rather than the simple PI controller are also investigated in an attempt to improve the system dynamics as well as the water flow output. An experimental prototype system is used to validate the proposed Q V/f under diverse operating conditions. [ABSTRACT FROM AUTHOR]

Due to the absence of mechanical contact, active magnetic bearing can be electrically controlled in an accuracy of a micrometer. This makes it a good choice to be used for robot manipulation in the micrometer scale, especially in environments that need to be very clean, for example, surgery or clean rooms. Moreover, it can be used in the applications that need high precision micromotion such as semiconductor wafers manipulation. Despite all these benefits, there are few studies that have investigated the application of active magnetic bearing in the robotics field in spotless environments for micromotion applications. This article proposes a new novel six-degree of freedom two-link manipulator using two contactless joints with active magnetic bearing. The key design aspects of the proposed manipulator are presented. The proposed manipulator is designed using finite element method. Each joint roll angle is controlled using a PID-based feedback linearization controller, while a state feedback controller with integral term is used for controlling the active magnetic bearing five-degree of freedom. The stability analysis of the system, under the proposed controller, is carried out. The robustness of the controllers is tested against end effector payload variations. The results demonstrate that the proposed two-link manipulator is feasible and valid for the applications in spotless environments that need high precision accuracy micromotion control. These significant findings have indicated the feasibility of implementing this proposed manipulator in practice and open the door for developing other types of robots with complete contactless joints using active magnetic bearing. [ABSTRACT FROM AUTHOR]

This article comprehends the design of a Brain-Based Robot (BBR) using hybrid techniques that incorporate both Brain-Based Device (BBD) and computational algorithms. BBDs are biologically inspired machines which have its behavior guided by a simulated nervous system. This nervous system follows detailed neuroanatomy of different brain areas. BBDs tend to have a nervous system with a large number of neurons and synapses. Thus, a huge computational power is required to simulate the nervous system of a BBD. Nevertheless, some of the tasks carried out by the simulated nervous system can be accomplished using computational algorithms which can help reducing the required computational power greatly. In this article, a BBR is built which combines some subsystems from BBD with computer vision algorithms. Computer vision algorithms are applied using OpenCV to extract some features from images, while neuronal-areas are connected together based on a detailed neuroanatomical structure to mimic the human learning process. Nengo python package is used for simulating neuronal areas in the system and monitoring activities of neuronal units. Moreover, the successful integration of the BBD's subsystems with computer vision leads to the perceptual categorization based on invariant object-recognition of various visual cues. To make a fair comparison with BBD, the nervous system of a BBD is built on the same computer used to build the hybrid brain for the proposed BBR. The proposed hybrid brain is then applied to a Nao humanoid robot in V-REP simulation environment to test it. The results obtained through this article prove that the proposed hybrid brain possesses the same intelligence of the BBD and requires much less computational power that it can run on an on-board computer of a robot, which makes it plausible for engineering applications. [ABSTRACT FROM AUTHOR]

This paper is concerned with the singularity analysis of a novel 4-DOF endoscopic surgical parallel manipulator (2-PUU_2-PUS). This parallel manipulator has larger bending angles compared to previously existing surgical parallel manipulators. It is easy to imagine the singular configurations inside the workspace of a manipulator with a zero pitch and infinity pitch reciprocal screws. On the other hand, the (2-PUU_2-PUS) surgical manipulator has two reciprocal screws of h-pitch. Hence, the singular configurations of this manipulator could not be provided by the known reciprocal screw-based geometrical approach. Therefore, the geometrical/analytical approach for reciprocal screw-based singularity analysis of 2-PUU_2-PUS is proposed in this work. The results illustrate the feasibility of the proposed algorithm to find all singular configurations of the 2-PUU_2-PUS manipulator. The discovered singularity configurations greatly shrink the singularity-free workspace to be one-fourth of the original workspace. In order to be able to work through the entire workspace, we suggest changing the topology structure of the manipulator. [ABSTRACT FROM AUTHOR]