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

Adaptive RBF network control for robot manipulators

TThe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function Network as an uncertainty estimator. The proposed network includes a hidden layer with one node, two inputs and a single output. In comparison with other model-free estimators such as multilayer neural networks and fuzzy systems, the proposed estimator is simpler, less computational and more effective. The weights of the RBF network are tuned online using an adaptation law derived by stability analysis. Despite the majority of previous control approaches which are the torque-based control, the proposed control design is the voltage-based control. Simulations and comparisons with a robust neural network control approach show the efficiency of the proposed control approach applied on the articulated robot manipulator driven by permanent magnet DC motors

Dinamički odziv nove adaptivne modificirane povratne Legendrove neuronske mreže upravljanja sinkronim motorom s permanentnim magnetima za električni skuter

Because an electric scooter driven by permanent magnet synchronous motor (PMSM) servo-driven system has the unknown nonlinearity and the time-varying characteristics, its accurate dynamic model is difficult to establish for the design of the linear controller in whole system. In order to conquer this difficulty and raise robustness, a novel adaptive modified recurrent Legendre neural network (NN) control system, which has fast convergence and provide high accuracy, is proposed to control for PMSM servo-driven electric scooter under the external disturbances and parameter variations in this study. The novel adaptive modified recurrent Legendre NN control system consists of a modified recurrent Legendre NN control with adaptation law and a remunerated control with estimation law. In addition, the online parameter tuning methodology of the modified recurrent Legendre NN control and the estimation law of the remunerated control can be derived by using the Lyapunov stability theorem and the gradient descent method. Furthermore, the modified recurrent Legendre NN with variable learning rate is proposed to raise convergence speed. Finally, comparative studies are demonstrated by experimental results in order to show the effectiveness of the proposed control scheme.S obzirom da električni skuter pogonjen servo sustavom sa sinkroni motor s permanentnim magnetima ima nelinearnu dinamiku i vremenski promjenjive parametre, njegov dinamički model nije jednostavno odrediti u svrhu dizajniranja linearnog regulatora. Kako bi se riješio taj problem te povećala robusnost predložen je sustav upravljanja korištenjem adaptivne modificirane povratne Legendrove neuronske mreže za upravljanje skuterom pogonjenim servo sustavom sa sinkronim motorom uz prisustvo vanjskog poremećaja i promjenjivih parametara. Predloženo upravljanje ima brzu konvergenciju i visoku preciznost. Sustav upravljanja sastoji se od modificirane povratne Legendrove neuronske moreže s adaptivnim zakonom upravljanja i estimacijom. Dodatno, \u27on-line\u27 podešavanje parametara takvog sustava može se dobiti korištenjem Ljapunovljevog teorema o stabilnosti sustava i gradijente metode. Modificirana povratne Legendrove neuronska mreža s promjenjivim vremenom učenja predložena je za povećanje brzine konvergencije. Ispravnost predložene sheme upravljanja provjerena je eksperimentalno

New Approaches in Automation and Robotics

The book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book

Automation and Control Architecture for Hybrid Pipeline Robots

The aim of this research project, towards the automation of the Hybrid Pipeline Robot (HPR), is the development of a control architecture and strategy, based on reconfiguration of the control strategy for speed-controlled pipeline operations and self-recovering action, while performing energy and time management. The HPR is a turbine powered pipeline device where the flow energy is converted to mechanical energy for traction of the crawler vehicle. Thus, the device is flow dependent, compromising the autonomy, and the range of tasks it can perform. The control strategy proposes pipeline operations supervised by a speed control, while optimizing the energy, solved as a multi-objective optimization problem. The states of robot cruising and self recovering, are controlled by solving a neuro-dynamic programming algorithm for energy and time optimization, The robust operation of the robot includes a self-recovering state either after completion of the mission, or as a result of failures leading to the loss of the robot inside the pipeline, and to guaranteeing the HPR autonomy and operations even under adverse pipeline conditions Two of the proposed models, system identification and tracking system, based on Artificial Neural Networks, have been simulated with trial data. Despite the satisfactory results, it is necessary to measure a full set of robot’s parameters for simulating the complete control strategy. To solve the problem, an instrumentation system, consisting on a set of probes and a signal conditioning board, was designed and developed, customized for the HPR’s mechanical and environmental constraints. As a result, the contribution of this research project to the Hybrid Pipeline Robot is to add the capabilities of energy management, for improving the vehicle autonomy, increasing the distances the device can travel inside the pipelines; the speed control for broadening the range of operations; and the self-recovery capability for improving the reliability of the device in pipeline operations, lowering the risk of potential loss of the robot inside the pipeline, causing the degradation of pipeline performance. All that means the pipeline robot can target new market sectors that before were prohibitive

Adaptive Control of an Active Magnetic Bearing with External Disturbance

Adaptive back stepping control (ABC) is originally applied to a linearized model of an active magnetic bearing (AMB) system. Our control goal is to regulate the deviation of the magnetic bearing from its equilibrium position in the presence of an external disturbance and system uncertainties. Two types of ABC methods are developed on the AMB system. One is based on full state feedback, for which displacement, velocity, and current states are assumed available. The other one is adaptive observer based back stepping controller (AOBC) where only displacement output is measurable. An observer is designed for AOBC to estimate velocity and current states of AMB. Lyapunov approach proves the stabilities of both regular ABC and AOBC. Simulation results demonstrate the effectiveness and robustness of two controllers

Contemporary Robotics

This book book is a collection of 18 chapters written by internationally recognized experts and well-known professionals of the field. Chapters contribute to diverse facets of contemporary robotics and autonomous systems. The volume is organized in four thematic parts according to the main subjects, regarding the recent advances in the contemporary robotics. The first thematic topics of the book are devoted to the theoretical issues. This includes development of algorithms for automatic trajectory generation using redudancy resolution scheme, intelligent algorithms for robotic grasping, modelling approach for reactive mode handling of flexible manufacturing and design of an advanced controller for robot manipulators. The second part of the book deals with different aspects of robot calibration and sensing. This includes a geometric and treshold calibration of a multiple robotic line-vision system, robot-based inline 2D/3D quality monitoring using picture-giving and laser triangulation, and a study on prospective polymer composite materials for flexible tactile sensors. The third part addresses issues of mobile robots and multi-agent systems, including SLAM of mobile robots based on fusion of odometry and visual data, configuration of a localization system by a team of mobile robots, development of generic real-time motion controller for differential mobile robots, control of fuel cells of mobile robots, modelling of omni-directional wheeled-based robots, building of hunter- hybrid tracking environment, as well as design of a cooperative control in distributed population-based multi-agent approach. The fourth part presents recent approaches and results in humanoid and bioinspirative robotics. It deals with design of adaptive control of anthropomorphic biped gait, building of dynamic-based simulation for humanoid robot walking, building controller for perceptual motor control dynamics of humans and biomimetic approach to control mechatronic structure using smart materials

Passivity-Based adaptive bilateral teleoperation control for uncertain manipulators without jerk measurements

In this work, we consider the bilateral teleoperation problem of cooperative robotic systems in a Single-Master Multi-Slave (SM/MS) configuration, which is able to perform load transportation tasks in the presence of parametric uncertainty in the robot kinematic and dynamic models. The teleoperation architecture is based on the two-layer approach placed in a hierarchical structure, whose top and bottom layers are responsible for ensuring the transparency and stability properties respectively. The load transportation problem is tackled by using the formation control approach wherein the desired translational velocity and interaction force are provided to the master robot by the user, while the object is manipulated with a bounded constant force by the slave robots. Firstly, we develop an adaptive kinematic-based control scheme based on a composite adaptation law to solve the cooperative control problem for robots with uncertain kinematics. Secondly, the dynamic adaptive control for cooperative robots is implemented by means of a cascade control strategy, which does not require the measurement of the time derivative of force (which requires jerk measurements). The combination of the Lyapunov stability theory and the passivity formalism are used to establish the stability and convergence property of the closed-loop control system. Simulations and experimental results illustrate the performance and feasibility of the proposed control scheme.No presente trabalho, considera-se o problema de teleoperação bilateral de um sistema robótico cooperativo do tipo single-master e multiple-slaves (SM/MS) capaz de realizar tarefas de transporte de carga na presença de incertezas paramétricas no modelo cinemático e dinâmico dos robôs. A arquitetura de teleoperação está baseada na abordagem de duas camadas em estrutura hierárquica, onde as camadas superior e inferior são responsáveis por assegurar as propriedades de transparência e estabilidade respectivamente. O problema de transporte de carga é formulado usando a abordagem de controle de formação onde a velocidade de translação desejada e a força de interação são fornecidas ao robô mestre pelo operador, enquanto o objeto é manipulado pelos robôs escravos com uma força constante limitada. Primeiramente, desenvolve-se um esquema de controle adaptativo cinemático baseado em uma lei de adaptação composta para solucionar o problema de controle cooperativo de robôs com cinemática incerta. Em seguida, o controle adaptativo dinâmico de robôs cooperativos é implementado por meio de uma estratégia de controle em cascata, que não requer a medição da derivada da força (o qual requer a derivada da aceleração ou jerk). A teoria de estabilidade de Lyapunov e o formalismo de passividade são usados para estabelecer as propriedades de estabilidade e a convergência do sistema de controle em malha-fechada. Resultados de simulações numéricas ilustram o desempenho e viabilidade da estratégia de controle proposta