Month: January 2016

Robust Fault-Tolerant Control for a Class of Second-Order Nonlinear Systems Using an Adaptive Third-Order Sliding Mode Control

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Abstract
Due to the robustness against the uncertainties, conventional sliding mode control (SMC) has been extensively developed for fault-tolerant control (FTC) system. However, the FTCs based on conventional SMC provide several disadvantages such as large transient state error, less robustness, and large chattering , that limit its application for real application. In order to enhance the performance, a novel adaptive third-order SMC, which combines a novel third-order sliding mode surface, a continuous strategy and an adaptation law, is proposed. Compared with other innovation approaches, the proposed controller has an excellent capability to tackle several types of actuator faults with an enhancing on robustness, precision, chattering reduction, and time of convergence. The proposed method is then applied for an attitude control of a spacecraft and the results demonstrate the superior performance. Index Termsโ€”Fault diagnosis, fault-tolerant control (FTC), high-order sliding mode (HOSM) control, nonlinear systems, observer-controller system.
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Fault-Tolerant Inverter for High-Speed Low-Inductance BLDC Drives in Aerospace Applications

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Abstract
Due to the simplicity and high reliability, brushless dc (BLDC) motors are widely used in space application. High-reliability levels are the vital aspect for ensuring the long-term stable operation of the BLDC motor system, which is used in aerospace applications. The fault-tolerant control of the BLDC motor is of great importance for its continuous operating capacity even under the faulty situation. This paper proposes a fault-tolerant topology composed of an additional phase leg and a fault-protective circuit for the high-speed low-inductance BLDC motor. Based on the analysis of the overcurrent and overvoltage phenomenon after the switch faults, a novel fault isolation and system reconfiguration method is presented. The method can achieve safe isolation and reconfiguration to avoid the secondary fault caused by direct switch of the redundant switch and the faulty switch after the fault-diagnosis process. Both simulation and experimental results confirm the feasibility and effectiveness of the proposed method.
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Preliminary development of a soft robotic ultrasound steering system

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Abstract:
Intravascular ultrasound (IVUS) imaging provides
two-dimensional (2D) real-time luminal and transmural
cross-sectional images of intravascular vessels with detailed
pathological information. It has offered significant advantages
in terms of diagnosis and guidance and has been increasingly
introduced from coronary interventions into more generalized
endovascular surgery. However, IVUS itself does not provide
spatial pose information for its generated images, making it
difficult to construct a 3D intravascular visualization. To
address this limitation, IVUS imaging-driven 3D intravascular
reconstruction techniques have been developed. These
techniques enable accuratediagnosis andquantitative
measurements of intravascular diseases to facilitate optimal
treatment determination. Such reconstruction extends the IVUS
imaging modality from pure diagnostic assistance to
intraoperative navigation and guidance and supports both
therapeutic options and interventional operations. This paper
presents a comprehensive survey of technological advances and
recent progress on IVUS imaging-based 3D intravascular
reconstruction and its state-of-the-art applications. Limitations
of existing technologies and prospects of new technologies are
also discussed.
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Self-correction of Commutation Point for High-speed Sensorless BLDC Motor With Low Inductance and Nonideal Back EMF

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Abstract
This paper presents a novel self-correction method of commutation point for high-speed sensorless brushless dc motors with low inductance and nonideal back electromotive force (EMF) in order to achieve low steady-state loss of magnetically suspended control moment gyro. The commutation point before correction is obtained by detecting the phase of EMF zero-crossing point and then delaying 30 electrical degrees. Since the speed variation is small between adjacent commutation points, the difference of the nonenergized phase’s terminal voltage between the beginning and the end of commutation is mainly related to the commutation error. A novel control method based on model-free adaptive control is proposed, and the delay degree is corrected by the controller in real time. Both the simulation and experimental results show that the proposed correction method can achieve ideal commutation effect within the entire operating speed range
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Motion Planning based on Learning from Demonstration for Multiple-Segment Flexible Robots Actuated by Electroactive Polymers

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Abstract
Multiple-segment flexible and soft robotic arms composed by ionic polymer – metal composite (IPMC) flexible actuators exhibit compliance but suffer from the difficulty of path planning due to their redundant degrees of freedom, although they are promising in complex tasks such as crossing body cavities to grasp objects. We propose a learning from demonstration method to plan the motion paths of IPMC-based manipulators, by statistics machine-learning algorithms. To encode demonstrated trajectories and estimate suitable paths for the manipulators to reproduce the task, models are built based on Gaussian mixture model and Gaussian mixture regression, respectively. The forward and inverse kinematic models of IPMC-based soft robotic arm are derived for the motion control. A flexible and soft robotic manipulator is implemented with six IPMC segments, and it verifies the learned paths by successfully completing a representative task of navigating through a narrow keyhole.
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A novel constrained wire-driven flexible mechanism and its kinematic analysis

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Abstract
Snake-like flexible manipulators are widely used in minimally invasive surgery (MIS), which require adequate dexterity in confined workspace. Typically, the design mechanisms of these manipulators include tendon-driven mechanism and concentric tube mechanism. Though, the workspace and dexterity of these designs are limited due to the lack of control in either the length of the bending section or the curvature of the bending section at the distal end. In this paper, we present a novel constrained wire-driven flexible mechanism (CWFM), in which both the length and the curvature of the bending section are controllable. The idea is to employ an active constraint to control the length of the bending section and use the wires to control the curvature of the bending section. Compared to the existing designs based on wire-driven flexible mechanism (WFM), CWFM has expanded workspace and enhanced dexterity while its size is not sacrificed. Additional benefits include much reduced sweeping area and controllable stiffness. Based on the computer simulation, on average, CWFM with the same size as WFM can improve the dexterity by 4.69 times and reduce the sweeping area to 20.5%.
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Data-driven methods towards learning the highly nonlinear inverse kinematics of tendon-driven surgical manipulators: XWJ_IJMRAS_IK_KNNR_GMR_ELM

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Abstract
Background: Accurate motion control of flexible surgical manipulators is crucial in tissue manipulation tasks. The tendon-driven serpentine manipulator (TSM) is one of the most widely adopted flexible mechanisms in minimally invasive surgery because of its enhanced maneuverability in torturous environments. TSM, however, exhibits high nonlinearities and conventional analytical kinematics model is insufficient to achieve high accuracy. Methods: To account for the system nonlinearities, we applied a data driven approach to encode the system inverse kinematics. Three regression methods: extreme learning machine (ELM), Gaussian mixture regression (GMR) and K-nearest neighbors regression (KNNR) were implemented to learn a nonlinear mapping from the robot 3D position states to the control inputs. Results: The performance of the three algorithms was evaluated both in simulation and physical trajectory tracking experiments. KNNR performed the best in the tracking experiments, with the lowest RMSE of 2.1275 mm. Conclusions: The proposed inverse kinematics learning methods provide an alternative and efficient way to accurately model the tendon driven flexible manipulator.
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Prototyping and characterisation of a variable stiffness actuation mechanism based on low melting point polymer

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Abstract:

With the advent of automation and robotic systems, flexible robotic manipulators are becoming increasingly popular in various applications where safe interaction with surrounding
environments is needed. This project aims to investigate stiffness varying technology for a class of flexible manipulators with the aim of online changing manipulator stiffness. We propose and develop a stiffness varying mechanism based on low melting point Polycaprolactone (PCL), characterize it and test out together with extensive experiments. The proposed mechanism is further integrated into a tendon-driven flexible manipulator and it successfully change the overall stiffness of the manipulator. This paper mainly involves design improvement, modeling, characterization and hands-on experiments.

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BN5209-6209 Neurosensors and Signal Processing/Neurotechnology AY15/16

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BN5209/BN6209 Neurosensors and Signal Processing / Neurotechnology Semester 2, 2015/2016

SCHEDULE

Lecture Time:

  • Tuesday: 3 pm โ€“ 6 pm (EA-06-03)

Syllabus

Note: Information contained in this syllabus may be subject to change.

Week Topic
1
Jan12		 Intro to the Course (NT)
Intro to Neurotechnology  (NT)
2
Jan19		 Introduction of BioSignal Processing  (HR)
L1-CFT; L2-Stochastic Process/R.V./Moments/PSD
3
Jan26		 Neural recording methods: Neural circuits, amplifiers, telemetry, stimulation (NT)
4
Feb2		 Prepare Student Seminars – paper selection
Time-Frequency-Spatial Analysis  STFT (HR)
5
Feb9 (CNY)		 Holidays
6
Feb16		 Neural signals (clinical applications)- EEG, evoked potentials (HR)
Lab tutorial for Project I: Neural Signals and Analysis
Recess Feb22
7
Mar1		 Multiple Dimensional  Signal Processing (HR)
Lab Project II: Application in neural systems
Student Reading Seminars (HR)
8
Mar8		 Neuro Diagnostic and Therapeutic Devices by NT
9
Mar15		 Brain machine interfaces  (NT)
EEG/ECoG
10
Mar22		 Neuromorphic Engineering – Brain Inspired Robotics by SK
11
Mar29		 Neuroimaging and Image Processing (HR)
Neuroimaging fMRI (HR)
12
Apr5		 Advanced Neurosignal Processing / Neurosurgical systems (HR)
13
Apr12 (makeup)		 Project Reports (due before final) & presentations (HR, NT)

 

Course Projects

1. EEG for brain state monitoring
2. EEG/EMG Feature Identification Extension

AIMS & OBJECTIVES

This module teaches students the advanced neuroengineering principles ranging from basic neuroscience introduction to neurosensing technology as well as advanced signal processing techniques. Major topics include: introduction to neurosciences, neural recording methods, neural circuits, amplifiers, telemetry, stimulation, sensors for measuring the electric field and magnetic field of the brain in relation to brain activities, digitization of brain activities, neural signal processing, brain machine interfaces, neurosurgical systems and applications of neural interfaces. The module is designed for students at Master and PhD levels in Engineering, Science and Medicine.

PREREQUISITES

Basic probability
Basic circuits
Linear algebra (matrix/vector)
Matlab or other programming
Recommended Textbooks: Neural Engineering, Edited by Bin He
Seminar papers

TEACHING MODES

The majority of the course will be in lecture-tutorial format. Some advanced topics will be in the formats of seminar and research presentations.

ASSESSMENT

Take Home Tests (5 for 50%)
Labs/Projects Reports + Presentations (20%)
Seminars (10%)
Take Home Final Exam(20%)

Finding the Kinematic Base Frame of a Robot by Hand-Eye Calibration Using 3D Position Data

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Abstract

When a robot is required to perform specific tasks defined in the world frame, there is a need for finding the coordinate transformation between the kinematic base frame of the robot and the world frame. The kinematic base frame used by the robot controller to define and evaluate the kinematics may deviate from the mechanical base frame constructed based on structural features. Besides, by using kinematic modeling rules such as the product of exponentials (POE) formula, the base frame can be arbitrarily located, and does not have to be related to any feature of the mechanical structure. As a result, the kinematic base frame cannot be measured directly. This paper proposes to find the kinematic base frame by solving a hand-eye calibration problem using 3D position measurements only, which avoids the inconvenience and inaccuracy of measuring orientations and thus significantly facilitates practical operations. A closed-form solution and an iterative solution are explicitly formulated and proved effective by simulations. Comprehensive analyses of the impact of key parameters to the accuracy of the solution are also carried out, providing four guidelines to better conduct practical operations. Finally, experiments on a 7-DOF industrial robot are performed with an optical tracking system to demonstrate the superiority of the proposed method using position data only over the method using full pose data.

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