Download or read book Intelligent adaptive Control Strategies for Robot Manipulators written by Hoon Kang and published by . This book was released on 1989 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Adaptive Control of Mechanical Manipulators written by John J. Craig and published by Addison Wesley Publishing Company. This book was released on 1988 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Adaptive Control of Robot Manipulators written by An-Chyau Huang and published by World Scientific. This book was released on 2010 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces an unified function approximation approach to the control of uncertain robot manipulators containing general uncertainties. It works for free space tracking control as well as compliant motion control. It is applicable to the rigid robot and the flexible joint robot. Even with actuator dynamics, the unified approach is still feasible. All these features make the book stand out from other existing publications.

Download or read book Adaptive Control for Robotic Manipulators written by Dan Zhang and published by CRC Press. This book was released on 2017-02-03 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: The robotic mechanism and its controller make a complete system. As the robotic mechanism is reconfigured, the control system has to be adapted accordingly. The need for the reconfiguration usually arises from the changing functional requirements. This book will focus on the adaptive control of robotic manipulators to address the changed conditions. The aim of the book is to summarise and introduce the state-of-the-art technologies in the field of adaptive control of robotic manipulators in order to improve the methodologies on the adaptive control of robotic manipulators. Advances made in the past decades are described in the book, including adaptive control theories and design, and application of adaptive control to robotic manipulators.

Download or read book Adaptive Neural Network Control of Robotic Manipulators written by Shuzhi S. Ge and published by World Scientific Series In Robotics And Intelligent Systems. This book was released on 1998 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, there has been considerable research interest in neural network control of robots, and satisfactory results have been obtained in solving some of the special issues associated with the problems of robot control in an "on-and-off" fashion. This book is dedicated to issues on adaptive control of robots based on neural networks. The text has been carefully tailored to (i) give a comprehensive study of robot dynamics, (ii) present structured network models for robots, and (iii) provide systematic approaches for neural network based adaptive controller design for rigid robots, flexible joint robots, and robots in constraint motion. Rigorous proof of the stability properties of adaptive neural network controllers is provided. Simulation examples are also presented to verify the effectiveness of the controllers, and practical implementation issues associated with the controllers are also discussed.

Download or read book Intelligent Control written by Kaushik Das Sharma and published by Springer. This book was released on 2018-08-28 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses systematic designs of stable adaptive fuzzy logic controllers employing hybridizations of Lyapunov strategy-based approaches/H∞ theory-based approaches and contemporary stochastic optimization techniques. The text demonstrates how candidate stochastic optimization techniques like Particle swarm optimization (PSO), harmony search (HS) algorithms, covariance matrix adaptation (CMA) etc. can be utilized in conjunction with the Lyapunov theory/H∞ theory to develop such hybrid control strategies. The goal of developing a series of such hybridization processes is to combine the strengths of both Lyapunov theory/H∞ theory-based local search methods and stochastic optimization-based global search methods, so as to attain superior control algorithms that can simultaneously achieve desired asymptotic performance and provide improved transient responses. The book also demonstrates how these intelligent adaptive control algorithms can be effectively utilized in real-life applications such as in temperature control for air heater systems with transportation delay, vision-based navigation of mobile robots, intelligent control of robot manipulators etc.

Download or read book Neural and Adaptive Control Strategies for a Rigid Link Manipulator written by Dorin Popescu and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this chapter, some classical, adaptive and neural control strategies for a simple planar robotic manipulator with two revolute joints were designed and implemented. First, the conventional computed-torque method was discussed. This control method solves the precision tracking problem, by using an exactly linearization of the nonlinearities of the manipulator model. The main disadvantage is the assumption of an exactly known dynamic model. If the model is imprecise known, it is necessary to design adaptive and/or neural control strategies. Direct and indirect adaptive controllers have been studied and implemented in order to preserve the tracking performances when parameter uncertainties occur. From the simulation point of view, it can be noticed that the evolution of tracking errors remains good, even if the estimated parameters are used in the control law. Also, three neural based control strategies were developed: a feedforward neural controller, a feedback neural control scheme, and a feedback error based neural controller. The simulations showed that the proposed neural controllers obtain results comparable to those achieved using adaptive control strategies. If a classical (PD or computed-torque) controller already controls a manipulator the advantage of proposed neural structures is that extension to a neural controller for performances improvement is easy.

Download or read book Prescribed Time Decentralized and Delay Adaptive Control Strategies for Robot Manipulators written by Alexander Bertino and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this manuscript, we formulate and experimentally verify four state-of-the-art controlstrategies on Baxter, a 7-DOF redundant robot manipulator. The control strategies examined in this manuscript are the subject of active research in the field of non-linear control, and have the potential to significantly improve the performance of robot manipulators when they operate in unstructured environments. The first control strategy we investigate in this manuscript is model-free decentralized-adaptive control. The purpose of this control strategy is to achieve consistent performance across a wide range of joint configurations and end-effector inertias, while having a similar computational efficiency as PID approaches. The second control strategy we investigate in this manuscript is delay-adaptive control. The purpose of this control strategy is to simultaneously estimate and compensate for an unknown long actuator delay. The third control strategy we investigate in this manuscript is prescribed-time control. A key feature of this control strategy is that the settling time is explicitly assigned by the control designer to a value desired, or "prescribed" by the user, and that the settling time is independent of the initial conditions and of the reference signal. The fourth control strategy we investigate in this manuscript is the prescribed-time safety filter. This formation yields a filter that is capable of avoiding multiple obstacles in a minimally invasive manner with bounded joint torques, while simultaneously allowing a nominal controller to converge to positions located on the boundary of the safe set by the end of a fixed-duration task. Through the formulation and experimental verification of each control strategy we present in this manuscript, we demonstrate that our proposed methods perform well in both theory and in practice.

Download or read book Robot Manipulators written by Agustin Jimenez and published by BoD – Books on Demand. This book was released on 2010-03-01 with total page 680 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the most recent research advances in robot manipulators. It offers a complete survey to the kinematic and dynamic modelling, simulation, computer vision, software engineering, optimization and design of control algorithms applied for robotic systems. It is devoted for a large scale of applications, such as manufacturing, manipulation, medicine and automation. Several control methods are included such as optimal, adaptive, robust, force, fuzzy and neural network control strategies. The trajectory planning is discussed in details for point-to-point and path motions control. The results in obtained in this book are expected to be of great interest for researchers, engineers, scientists and students, in engineering studies and industrial sectors related to robot modelling, design, control, and application. The book also details theoretical, mathematical and practical requirements for mathematicians and control engineers. It surveys recent techniques in modelling, computer simulation and implementation of advanced and intelligent controllers.

Download or read book Intelligent Optimal Adaptive Control for Mechatronic Systems written by Marcin Szuster and published by Springer. This book was released on 2017-12-28 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book deals with intelligent control of mobile robots, presenting the state-of-the-art in the field, and introducing new control algorithms developed and tested by the authors. It also discusses the use of artificial intelligent methods like neural networks and neuraldynamic programming, including globalised dual-heuristic dynamic programming, for controlling wheeled robots and robotic manipulators,and compares them to classical control methods.

Download or read book Intelligent Control of Robotic Systems written by D. Katic and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: As robotic systems make their way into standard practice, they have opened the door to a wide spectrum of complex applications. Such applications usually demand that the robots be highly intelligent. Future robots are likely to have greater sensory capabilities, more intelligence, higher levels of manual dexter ity, and adequate mobility, compared to humans. In order to ensure high-quality control and performance in robotics, new intelligent control techniques must be developed, which are capable of coping with task complexity, multi-objective decision making, large volumes of perception data and substantial amounts of heuristic information. Hence, the pursuit of intelligent autonomous robotic systems has been a topic of much fascinating research in recent years. On the other hand, as emerging technologies, Soft Computing paradigms consisting of complementary elements of Fuzzy Logic, Neural Computing and Evolutionary Computation are viewed as the most promising methods towards intelligent robotic systems. Due to their strong learning and cognitive ability and good tolerance of uncertainty and imprecision, Soft Computing techniques have found wide application in the area of intelligent control of robotic systems.

Download or read book Learning for Adaptive and Reactive Robot Control written by Aude Billard and published by MIT Press. This book was released on 2022-02-08 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises. This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer safe and compliant control of forces in contact. The techniques offer theoretical advantages, including convergence to a goal, non-penetration of obstacles, and passivity. The coverage of learning begins with low-level control parameters and progresses to higher-level competencies composed of combinations of skills. Learning for Adaptive and Reactive Robot Control is designed for graduate-level courses in robotics, with chapters that proceed from fundamentals to more advanced content. Techniques covered include learning from demonstration, optimization, and reinforcement learning, and using dynamical systems in learning control laws, trajectory planning, and methods for compliant and force control . Features for teaching in each chapter: applications, which range from arm manipulators to whole-body control of humanoid robots; pencil-and-paper and programming exercises; lecture videos, slides, and MATLAB code examples available on the author’s website . an eTextbook platform website offering protected material[EPS2] for instructors including solutions.

Download or read book Robotic Manipulator Control Using Neural Networks written by Al Ashi Mahmoud and published by LAP Lambert Academic Publishing. This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The learning capabilities of artificial neural networks (ANNs) to identify and emulate the behavior of complicated nonlinear systems have made them effective tools that can be utilized in intelligent adaptive control strategies. The use of ANNs in the design of trajectory tracking controllers for robotic manipulators is dated back to the 1980s. Due to the flexibility of their structure as well as the continuous development and enhancement of their self-training algorithms, the use of ANNs in the field of robotic manipulator trajectory tracking control is being considered an important research area. This textbook explains in great detail the process of designing an effective controller to enhance the trajectory tracking performance of a two degree of freedom (2-DOF) robotic arm using neural networks. Feed-forward ANNs were used in both model-based and non-model-based control strategies. Since it also includes a deep explanation of the modeling of the 2-DOF robotic arm system including its actuating DC-motors and their control using a PD controller, this textbook can also serve as an effective educational tool for both undergraduate and graduate electrical engineering students.

Download or read book Adaptive Neural Network Control of Robotic Manipulators written by Tong Heng Lee and published by World Scientific. This book was released on 1998 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction; Mathematical background; Dynamic modelling of robots; Structured network modelling of robots; Adaptive neural network control of robots; Neural network model reference adaptive control; Flexible joint robots; task space and force control; Bibliography; Computer simulation; Simulation software in C.

Download or read book An Adaptive Control Strategy for Robot Manipulator Control written by Guo-Qiang Zhou and published by . This book was released on 1987 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robot Learning Human Skills and Intelligent Control Design written by Chenguang Yang and published by CRC Press. This book was released on 2021-06-21 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last decades robots are expected to be of increasing intelligence to deal with a large range of tasks. Especially, robots are supposed to be able to learn manipulation skills from humans. To this end, a number of learning algorithms and techniques have been developed and successfully implemented for various robotic tasks. Among these methods, learning from demonstrations (LfD) enables robots to effectively and efficiently acquire skills by learning from human demonstrators, such that a robot can be quickly programmed to perform a new task. This book introduces recent results on the development of advanced LfD-based learning and control approaches to improve the robot dexterous manipulation. First, there's an introduction to the simulation tools and robot platforms used in the authors' research. In order to enable a robot learning of human-like adaptive skills, the book explains how to transfer a human user’s arm variable stiffness to the robot, based on the online estimation from the muscle electromyography (EMG). Next, the motion and impedance profiles can be both modelled by dynamical movement primitives such that both of them can be planned and generalized for new tasks. Furthermore, the book introduces how to learn the correlation between signals collected from demonstration, i.e., motion trajectory, stiffness profile estimated from EMG and interaction force, using statistical models such as hidden semi-Markov model and Gaussian Mixture Regression. Several widely used human-robot interaction interfaces (such as motion capture-based teleoperation) are presented, which allow a human user to interact with a robot and transfer movements to it in both simulation and real-word environments. Finally, improved performance of robot manipulation resulted from neural network enhanced control strategies is presented. A large number of examples of simulation and experiments of daily life tasks are included in this book to facilitate better understanding of the readers.

Download or read book Robot Force Control written by Bruno Siciliano and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the fundamental requirements for the success of a robot task is the capability to handle interaction between manipulator and environment. The quantity that describes the state of interaction more effectively is the contact force at the manipulator's end effector. High values of contact force are generally undesirable since they may stress both the manipulator and the manipulated object; hence the need to seek for effective force control strategies. The book provides a theoretical and experimental treatment of robot interaction control. In the framework of model-based operational space control, stiffness control and impedance control are presented as the basic strategies for indirect force control; a key feature is the coverage of six-degree-of-freedom interaction tasks and manipulator kinematic redundancy. Then, direct force control strategies are presented which are obtained from motion control schemes suitably modified by the closure of an outer force regulation feedback loop. Finally, advanced force and position control strategies are presented which include passivity-based, adaptive and output feedback control schemes. Remarkably, all control schemes are experimentally tested on a setup consisting of a seven-joint industrial robot with open control architecture and force/torque sensor. The topic of robot force control is not treated in depth in robotics textbooks, in spite of its crucial importance for practical manipulation tasks. In the few books addressing this topic, the material is often limited to single-degree-of-freedom tasks. On the other hand, several results are available in the robotics literature but no dedicated monograph exists. The book is thus aimed at filling this gap by providing a theoretical and experimental treatment of robot force control.