Learning for Advanced Motion Control

Iterative Learning Control (ILC) can achieve perfect tracking performance for mechatronic systems. The aim of this paper is to present an ILC design tutorial for industrial mechatronic systems. First, a preliminary analysis reveals the potential performance improvement of ILC prior to its actual implementation. Second, a frequency domain approach is presented, where fast learning is achieved through noncausal model inversion, and safe and robust learning is achieved by employing a contraction mapping theorem in conjunction with nonparametric frequency response functions. The approach is demonstrated on a desktop printer. Finally, a detailed analysis of industrial motion systems leads to several shortcomings that obstruct the widespread implementation of ILC algorithms. An overview of recently developed algorithms, including extensions using machine learning algorithms, is outlined that are aimed to facilitate broad industrial deployment.Comment: 8 pages, 15 figures, IEEE 16th International Workshop on Advanced Motion Control, 202

Embedded Controller Design for Mechatronics System

Many mechatronic systems have challenging control difficulties due to the high nonlinear structures and time‐varying dynamic behaviors. In addition to these, there are external disturbances, which cannot be predicted and change according to uncontrolled working environments. Conventional controllers are insufficient to solve the aforementioned problems and to compensate the environmental disturbances. Therefore, adaptive controllers have been proposed as a solution to these inefficiencies of conventional controllers. Adaptive control is applied for solving the control problem of the mechatronic sun tracker that ensures the movement of the mechanism to harvest maximum energy coming from solar to the PV module surface during the sunshine duration. For this type of control problems, conventional controllers are very limited and they have a lot of deficiencies. The adaptive mechanism governed by an adaptation law is the heart of any adaptive controller. We establish the adaptation law for the plant control system using Lyapunov stability theory. This adaptation law is precise for a generic second‐order system; hence, it is obviously applicable for adaptive control of other second‐order systems in different realms, such as industrial production system, military, and robotics

A programmable logic controller based laboratory analysis of conventional and intelligent control schemes for non-liner systems

Published ArticleIntelligent Neural Network (NN) based control schemes have surmounted many of the limitations found in the conventional control approaches such as Proportional Integral Derivative (PID) control. Nevertheless, these modern control techniques have only recently been introduced for use on industrial computational platforms such as the Programmable Logic Controller (PLC). Intelligent control on PLCs thus remains an area that is open to further research and development. In this paper, a strongly non-linear mechatronic type system, namely the Ball-on-Wheel balancing system, is developed using a PLC as its control platform. The research details the implementation of an intelligent controller on a standard, medium specification PLC. The results from the intelligent controller are then compared to those produced by a variety of conventional controllers as physical parameters are varied. Finally, the system is presented as a stimulating educational tool that addresses the knowledge gap that exists in industry pertaining to the implementation of these intelligent control algorithms on PLCs

Mechatronics of systems with undetermined configurations

This work is submitted for the award of a PhD by published works. It deals with some of the efforts of the author over the last ten years in the field of Mechatronics. Mechatronics is a new area invented by the Japanese in the late 1970's, it consists of a synthesis of computers and electronics to improve mechanical systems. To control any mechanical event three fundamental features must be brought together: the sensors used to observe the process, the control software, including the control algorithm used and thirdly the actuator that provides the stimulus to achieve the end result. Simulation, which plays such an important part in the Mechatronics process, is used in both in continuous and discrete forms. The author has spent some considerable time developing skills in all these areas. The author was certainly the first at Middlesex to appreciate the new developments in Mechatronics and their significance for manufacturing. The author was one of the first mechanical engineers to recognise the significance of the new transputer chip. This was applied to the LQG optimal control of a cinefilm copying process. A 300% improvement in operating speed was achieved, together with tension control. To make more efficient use of robots they have to be made both faster and cheaper. The author found extremely low natural frequencies of vibration, ranging from 3 to 25 Hz. This limits the speed of response of existing robots. The vibration data was some of the earliest available in this field, certainly in the UK. Several schemes have been devised to control the flexible robot and maintain the required precision. Actuator technology is one area where mechatronic systems have been the subject of intense development. At Middlesex we have improved on the Aexator pneumatic muscle actuator, enabling it to be used with a precision of about 2 mm. New control challenges have been undertaken now in the field of machine tool chatter and the prevention of slip. A variety of novel and traditional control algorithms have been investigated in order to find out the best approach to solve this problem

Linear motor motion control using a learning feedforward controller

The design and realization of an online learning motion controller for a linear motor is presented, and its usefulness is evaluated. The controller consists of two components: (1) a model-based feedback component, and (2) a learning feedforward component. The feedback component is designed on the basis of a simple second-order linear model, which is known to have structural errors. In the design, an emphasis is placed on robustness. The learning feedforward component is a neural-network-based controller, comprised of a one-hidden-layer structure with second-order B-spline basis functions. Simulations and experimental evaluations show that, with little effort, a high-performance motion system can be obtained with this approach

Switched predictive control design for optimal wet-clutch engagement

Modeling of hydraulic clutch transmissions is far from straightforward due to their nonlinear hybrid dynamics, i.e. switching between three dynamic phases. In this paper we identify a local linear model only for the constrained first phase, based on which a predictive controller is used to track a suitable engagement signal. The robustness of this controller in the latter two phases is guaranteed by making the constraints inactive and pre-tuning the control parameters based on its closed loop formulation and applying robust stability theorem. This controller is then implemented in real-time on a wet-clutch test setup and is shown to achieve optimal engagement

System Approach to Vehicle Suspension System Control in CAE Environment

In recent years, motor vehicles industry has shown a tendency of replacing electromechanical components by mechatronic systems with intelligent and autonomous properties. The integration of hardware components and implementation of advance control function characterize this replacement. In this paper we have applied the system approach and system engineering methods in the initial phase of vehicle active suspension development. An emphasis has been placed upon the interrelations between computer-aided simulation and other elements of the development process. The benefits of application of active suspension simulation are numerous: reduction of time to market, the new and improved functions of mechatronic components/devices, as well as the increased system reliability. In suspension model development, we used CAD/CAE tools, as well as the multipurpose simulation programs. For simulation, we used the one-quarter vehicle model. The modelling was carried out through the state-space equation, after which we designed the controller for our system. During this, we considered only the digital systems of automatic regulation