Control Of Rigid Robots With Large Uncertainties Using The Function Approximation Technique

This dissertation focuses on the control of rigid robots that cannot easily be modeled due to complexity and large uncertainties. The function approximation technique (FAT), which represents uncertainties as finite linear combinations of orthonormal basis functions, provides an alternate form of robot control - in situations where the dynamic equation cannot easily be modeled - with no dependency on the use of model information or training data. This dissertation has four aims - using the FAT - to improve controller efficiency and robustness in scenarios where reliable mathematical models cannot easily be derived or are otherwise unavailable. The first aim is to analyze the uncertain combination of a test robot and prosthesis in a scenario where the test robot and prosthesis are adequately controlled by different controllers - this is tied to efficiency. We develop a hybrid FAT controller, theoretically prove stability, and verify its performance using computer simulations. We show that systematically combining controllers can improve controller analysis and yield desired performance. In the second aim addressed in this dissertation, we investigate the simplification of the adaptive FAT controller complexity for ease of implementation - this is tied to efficiency. We achieve this by applying the passivity property and prove controller stability. We conduct computer simulations on a rigid robot under good and poor initial conditions to demonstrate the effectiveness of the controller. For an n degrees of freedom (DOFs) robot, we see a reduction of controller tuning parameters by 2n. The third aim addressed in this dissertation is the extension of the adaptive FAT controller to the robust control framework - this is tied to robustness. We invent a novel robust controller based on the FAT that uses continuous switching laws and eliminates the dependency on update laws. The controller, when compared against three state-of-the-art controllers via computer simulations and experimental tests on a rigid robot, shows good performance and robustness to fast time-varying uncertainties and random parameter perturbations. This introduces the first purely robust FAT-based controller. The fourth and final aim addressed in this dissertation is the development of a more compact form of the robust FAT controller developed in aim~3 - this is tied to efficiency and robustness. We investigate the simplification of the control structure and its applicability to a broader class of systems that can be modeled via the state-space approach. Computer simulations and experimental tests on a rigid robot demonstrate good controller performance and robustness to fast time-varying uncertainties and random parameter perturbations when compared to the robust FAT controller developed in aim 3. For an n-DOF robot, we see a reduction in the number of switching laws from 3 to 1

Experimental evaluation of robot controllers

In the last decade an abundance of control laws for nonlinear robotic systems was proposed. A careful evaluation of several classes of these controllers has been made, to overcome some drawbacks of previous evaluations. Experiments were done on a simple robotic system, with prismatic joints only and with low cost controller hardware. A flexible and effective real-time software environment, using object oriented programming techniques, was developed, easing the implementation and the evaluation of many control laws. The experience gained leads to the recommendation to develop as good a model as possible combined with adaptive control for tuning of the model parameter

AI based Robot Safe Learning and Control

Introduction This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities

Modelling and control of a robotic manipulator subject to base disturbances

This thesis presents the modelling and control of a high gear ratio robotic manipulator mounted on a heavier moving base which is subject to base disturbances. The manipulator motion is assumed not to affect the base motion. The problem of a robotic manipulator on a non-inertial base can be applied to operation on sea vessels or all-terrain vehicles, where the base motion is unknown and cannot be used as a feed-forward signal to the model. A dynamic model is derived for the PA10-6CE manipulator with the assumption of a fixed base and the model terms are analysed numerically when comparing the simulation and experimental results. Based on the obtained results a set of model based controllers is compared to a basic proportional and derivative type controller to evaluate the trajectory tracking gains and trade-offs. The dynamic model is extended to the case of a manipulator on a moving base and numerical comparisons of simulation and experimental results are used to verify the model validity and the significance of the various model terms. From the results of this study a set of model based controllers is obtained. A novel adaptive scheme is then proposed for compensation of an unknown and varying gravity acceleration vector acting on the manipulator base. Controllers based on using an additional sensor output are compared with static and adaptive gravity controllers and the latter proved to be superior in terms of trajectory tracking performance

Adaptive Tracking Control with Uncertainty-aware and State-dependent Feedback Action Blending for Robot Manipulators

Adaptive control can significantly improve tracking performance of robot manipulators subject to modeling errors in dynamics. In this letter, we propose a new framework combining the composite adaptive controller using a natural adaptation law and an extension of the adaptive variance algorithm (AVA) for controller blending. The proposed approach not only automatically adjusts the feedback action to reduce the risk of violating actuator constraints but also anticipates substantial modeling errors by means of an uncertainty measure, thus preventing severe performance deterioration. A formal stability analysis of the closed-loop system is conducted. The control scheme is experimentally validated and directly compared with baseline methods on a torque-controlled KUKA LWR IV+