There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

This article provides a tutorial introduction to visual servo control of robotic manipulators. Since the topic spans many disciplines our goal is limited to providing a basic conceptual framework. We begin by reviewing the prerequisite topics from robotics and computer vision, including a brief review of coordinate transformations, velocity representation, and a description of the geometric aspects of the image formation process. We then present a taxonomy of visual servo control systems. The two major classes of systems, position-based and image-based systems, are then discussed in detail. Since any visual servo system must be capable of tracking image features in a sequence of images, we also include an overview of feature-based and correlation-based methods for tracking. We conclude the tutorial with a number of observations on the current directions of the research field of visual servo control.

/pdf/a-tutorial-on-visual-servo-control-5cy6rje63g.pdf

A tutorial on visual servo control

A review of shape memory alloy research, applications and opportunities

Fundamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called adaptive-network-based fuzzy inference system (ANFIS), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neuro-fuzzy approaches are also addressed. >

https://ir.nctu.edu.tw:443/bitstream/11536/2013/1/A1995QJ99800003.pdf

Neuro-fuzzy modeling and control

Analysis and Synthesis of Discrete-Time Repetitive Controllers

This paper presents the development of robust digital tracking control algorithms and their real-time implementation on an electrohydraulic servo-actuator for tool positioning in norcircular machining. Robust adaptive feedforward controller for tracking arbitrary signals and robust repetitive controller for tracking periodic signals against disturbances and unmodeled dynamics have been developed. Experimental results are presented to illustrate the control system synthesis procedures and tracking performance

Robust adaptive and repetitive digital tracking control and application to a hydraulic servo for noncircular machining

Repetitive control is formulated and analyzed in the discrete-time domain. Sufficiency conditions for the asymptotic convergence of a class of repetitive controllers are given. The "plug-in" repetitive controller is introduced and applied to track-following in a disk-file actuator system. Inter-sample ripples in the tracking error were present when the "plug-in" repetitive controller was installed. The performance is enhanced, however, when the zero-holding device is followed by a low-pass filter or replaced by a delayed first-order hold.

Discrete-Time Domain Analysis and Synthesis of Repetitive Controllers

Adaptive Zero Phase Error Tracking Algorithm for Digital Control

This paper describes the design and implementation of a discrete-time repetitive controller for the continuous steel casting process that is used for the rejection of periodic load disturbances with an unknown period. A discrete, recursive scheme is applied to identify the unknown disturbance period and to adaptively change the period setting of the repetitive control algorithm. An experiment was conducted on a full-scale industrial-grade water model. A comparison of the disturbance rejection properties of the repetitive controller with those of PI control is presented. Results that indicate the ease and effectiveness of manual tuning for the rejection of periodic disturbances are then given. Finally, an online adaptation of the repetitive controller for the rejection of a disturbance with an unknown period is demonstrated.

Tsu-Chin Tsao

Papers

Analysis and Synthesis of Discrete-Time Repetitive Controllers

Robust adaptive and repetitive digital tracking control and application to a hydraulic servo for noncircular machining

Discrete-Time Domain Analysis and Synthesis of Repetitive Controllers

Adaptive Zero Phase Error Tracking Algorithm for Digital Control

Rejection of unknown periodic load disturbances in continuous steel casting process using learning repetitive control approach