Motion control and synchronisation of multi-axis drive systems

Motion control and synchronisation of multi-axis drive system

Accuracy Enhancement for High Precision Gantry Stage

Ph.DDOCTOR OF PHILOSOPH

Advanced control designs for output tracking of hydrostatic transmissions

The work addresses simple but efficient model descriptions in a combination with advanced control and estimation approaches to achieve an accurate tracking of the desired trajectories. The proposed control designs are capable of fully exploiting the wide operation range of HSTs within the system configuration limits. A new trajectory planning scheme for the output tracking that uses both the primary and secondary control inputs was developed. Simple models or even purely data-driven models are envisaged and deployed to develop several advanced control approaches for HST systems

Induction Motors

AC motors play a major role in modern industrial applications. Squirrel-cage induction motors (SCIMs) are probably the most frequently used when compared to other AC motors because of their low cost, ruggedness, and low maintenance. The material presented in this book is organized into four sections, covering the applications and structural properties of induction motors (IMs), fault detection and diagnostics, control strategies, and the more recently developed topology based on the multiphase (more than three phases) induction motors. This material should be of specific interest to engineers and researchers who are engaged in the modeling, design, and implementation of control algorithms applied to induction motors and, more generally, to readers broadly interested in nonlinear control, health condition monitoring, and fault diagnosis

Intelligent instrumentation, control and monitoring of precision motion systems

Ph.DDOCTOR OF PHILOSOPH

Advance Servo Control for Hard Disk Drive in Mobile Application

Ph.DDOCTOR OF PHILOSOPH

Mechatronics Design Process with Energy Optimization for Industrial Machines

The need for designing industrial machines with higher energy efficiency, reliability, flexibility, and accuracy has increased to satisfy market demand for higher productivity at reduced costs in a sustainable manner. As machines become more complex, model-based design is essential to overcome the challenges in mechatronic system design. However, a well-designed mechanical system with a well-designed and tuned control system are not sufficient for machines to operate at high-performance conditions; this also heavily depends on trajectory planning and the appropriate selection of the motors controlling the axes of the machine. In this work, a model-based design approach to properly select motors for single-axes or multi-axes coordinated systems was proposed. Additionally, a trajectory planning approach was also proposed to improve performance of industrial machines. The proposed motor selection process and trajectory planning approach were demonstrated via modeling, simulation, and experimental validation for three systems: two-inertia system, planar robot, and self-balancing transporter. Over 25% of the electric energy delivered in the U.S. in 2013 was used in the industrial sector according to the U.S. Energy Information Administration, with an estimated efficiency of 80% according to the Lawrence Livermore National Laboratory. This entails major responsibility by the industry to utilize energy efficiently and promote sustainable energy usage. To help improve the energy efficiency in the industrial sector, a novel method to optimize the energy of single-axis and multi-axis coordinated systems of industrial machines was developed. Based on trajectory boundaries and the kinetic model of the mechanism and motors, this proposed energy optimization method performs iterations to recalculate the shape of the motion profile for each motor of the system being optimized until it converges to a motion profile with optimal energy cost and within these boundaries. This method was validated by comparing the energy consumption of those three systems while commanded by the optimized motion profile and then by motion profiles typically used in industrial applications. The energy saved was between 5% and 10%. The implementation cost of this method in industrial systems resides in machine-code changes; no physical changes are needed

Using a Combination of PID Control and Kalman Filter to Design of IoT-based Telepresence Self-balancing Robots during COVID-19 Pandemic

COVID-19 is a very dangerous respiratory disease that can spread quickly through the air. Doctors, nurses, and medical personnel need protective clothing and are very careful in treating COVID-19 patients to avoid getting infected with the COVID-19 virus. Hence, a medical telepresence robot, which resembles a humanoid robot, is necessary to treat COVID-19 patients. The proposed self-balancing COVID-19 medical telepresence robot is a medical robot that handles COVID-19 patients, which resembles a stand-alone humanoid soccer robot with two wheels that can maneuver freely in hospital hallways. The proposed robot design has some control problems; it requires steady body positioning and is subjected to disturbance. A control method that functions to find the stability value such that the system response can reach the set-point is required to control the robot's stability and repel disturbances; this is known as disturbance rejection control. This study aimed to control the robot using a combination of Proportional-Integral-Derivative (PID) control and a Kalman filter. Mathematical equations were required to obtain a model of the robot's characteristics. The state-space model was derived from the self-balancing robot's mathematical equation. Since a PID control technique was used to keep the robot balanced, this state-space model was converted into a transfer function model. The second Ziegler-Nichols's rule oscillation method was used to tune the PID parameters. The values of the amplifier constants obtained were Kp=31.002, Ki=5.167, and Kd=125.992128. The robot was designed to be able to maintain its balance for more than one hour by using constant tuning, even when an external disturbance is applied to it. Doi: 10.28991/esj-2021-SP1-016 Full Text: PD

Controller Synthesis of Multi-Axial Robotic System Used for Wearable Devices

Wearable devices are commonly used in different fields to help improving performance of movements for different groups of users. The long-term goal of this study is to develop a low-cost assistive robotic device that allows patients to perform rehabilitation activities independently and reproduces natural movement to help stroke patients and elderly adults in their daily activities while moving their arms. In the past few decades, various types of wearable robotic devices have been developed to assist different physical movements. Among different types of actuators, the twisted-string actuation system is one of those that has advantages of light-weight, low cost, and great portability. In this study, a dual twisted-string actuator is used to drive the joints of the prototype assistive robotic device. To compensate the asynchronous movement caused by nonlinear factors, a hybrid controller that combines fuzzy logic rules and linear PID control algorithm was adopted to compensate for both tracking and synchronization of the two actuators.;In order to validate the performance of proposed controllers, the robotic device was driven by an xPC Target machine with additional embedded controllers for different data acquisition tasks. The controllers were fine tuned to eliminate the inaccuracy of tracking and synchronization caused by disturbance and asynchronous movements of both actuators. As a result, the synthesized controller can provide a high precision when tracking simple actual human movements