Browsing by Author "Hsiao, Tesheng"

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A Dual-Model Fault Detection Approach with Application to Actuators of Robot Manipulators
(2011)
Hsiao, Tesheng
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Weng, Mao-Chiao
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電機工程學系
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Department of Electrical and Computer Engineering
Multiple-model (MM)-based methods have been successfully applied to many fault detection schemes; however systematic design of the associated model set remains an open question. The difficulty comes from the fact that using a large model set reduces the risk of undetected faults, but also increases the computation load drastically. In this paper we propose a dual-model fault detection (DMFD) algorithm aiming at solving the model set design problem, and apply it to detect actuator faults of robot manipulators. The DMFD algorithm is able to detect various types of unexpected actuator faults, including abrupt faults, incipient faults, and simultaneous faults, in a computationally efficient way. To evaluate the performance of the DMFD algorithm, upper bounds of the false alarm and missed detection probabilities are explicitly presented in terms of the tunable variables. Furthermore, experiments are conducted to demonstrate its ability in immediate detection of faults.
A hierarchical multiple-model approach for detection and isolation of robotic actuator faults
(2012-02-01)
Hsiao, Tesheng
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Weng, Mao-Chiao
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電機工程學系
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Department of Electrical and Computer Engineering
Modern robotic systems perform elaborate tasks in complicated environments and have close interactions with humans. Therefore fault detection and isolation (FDI) schemes must be carefully designed and implemented on robotic systems in order to guarantee safe and reliable operations. In this paper, we propose a hierarchical multiple-model FDI (HMM-FDI) scheme to detect and isolate actuator faults of robot manipulators. The proposed algorithm performs FDI in stages and refines the associated model set at each stage. Consequently only a small number of models are required to detect and isolate various types of unexpected actuator faults, including abrupt faults, incipient faults, and simultaneous faults. In addition, the computational load is alleviated due to the reduced-sized model set. The relation between the fault detection stage of the HMM-FDI scheme and the likelihood ratio test is explicitly revealed and theoretical upper bounds of the false alarm and missed detection probabilities are evaluated. Then we conduct experiments to demonstrate the ability of the HMM-FDI scheme in successful and immediate detection and isolation of actuator faults. (C) 2011 Elsevier B.V. All rights reserved.
A New Geometric Subproblem to Extend Solvability of Inverse Kinematics Based on Screw Theory for 6R Robot Manipulators
(1970-01-01)
Leoro, Josuet
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Hsiao, Tesheng
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Betancourt, Carlos
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電機工程學系
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Department of Electrical and Computer Engineering
Geometric inverse kinematics procedures that divide the whole problem into several subproblems with known solutions, and make use of screw motion operators have been developed in the past for 6R robot manipulators. These geometric procedures are widely used because the solutions of the subproblems are geometrically meaningful and numerically stable. Nonetheless, the existing subproblems limit the types of 6R robot structural configurations for which the inverse kinematics can be solved. This work presents the solution of a novel geometric subproblem that solves the joint angles of a general anthropomorphic arm. Using this new subproblem, an inverse kinematics procedure is derived which is applicable to a wider range of 6R robot manipulators. The inverse kinematics of a closed curve were carried out, in both simulations and experiments, to validate computational cost and realizability of the proposed approach. Multiple 6R robot manipulators with different structural configurations were used to validate the generality of the method. The results are compared with those of other methods in the screw theory framework. The obtained results show that our approach is the most general and the most efficient.
Adaptive Feedforward Active Noise Cancellation in Ducts Using the Model Matching of Wave Propagation Dynamics
(2012-09-01)
Hu, Jwusheng
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Hsiao, Tesheng
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電機工程學系
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Department of Electrical and Computer Engineering
Active noise cancellation in ducts using multiple feedforward sensors have been investigated by many researchers. When adaptive algorithms were applied, usually a lumped parameter model was assumed and the model includes the dynamics of the duct, sensors and actuators (e.g., microphone and loudspeaker). The distributed nature of the duct usually leads to a large parameter space and is difficult to implement direct adaptive control. This brief presents a control architecture which separates the duct's dynamics from those of sensors and actuators. An adaptive control algorithm is proposed to cope with the unknown dynamics of sensors and actuators. Experiments were conducted to show the effectiveness of the control law.
Admittance Control of Powered Exoskeletons Based on Joint Torque Estimation
(2020-01-01)
Liang, Chiawei
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Hsiao, Tesheng
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電機工程學系
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Department of Electrical and Computer Engineering
In recent years, exoskeletons have been widely accepted as rehabilitative and walking assistive devices for either healthy persons or patients with mobility impairment. Depending on mobility of the wearers, the control strategies for exoskeletons may differ significantly. This paper aims at developing an admittance control framework for exoskeletons to assist healthy persons in safer, faster, or more energy-efficient walking. A crucial step to accomplish assistive walking for healthy persons is to detect their intention. Direct sensing of the wearers biological signals, such as electromyography (EMG) and electroencephalography (EEG), requires additional sensors, which increases the cost and makes it inconvenient for the wearer to put on and take off the exoskeleton. Instead, we propose to detect the wearer & x2019;s intention by estimating the total torques applied from the wearer to the human-exoskeleton system based on the motor current and joint angles. Then the velocity commands to the joint motors are generated according to the estimated torque and a predefined mechanical admittance function of each joint. Consequently, the exoskeleton complies with the wearer & x2019;s intention. Rigorous theoretical analysis is performed on robust stability of the closed-loop system. Then experiments are carried out to verify the accuracy and robustness of the proposed torque estimation algorithm. In addition, experimental data show that the wearer & x2019;s gait can be shaped in a desired way by choosing an appropriate admittance function in the proposed admittance control loop.
Application of Optimum Tire Force Distribution to Vehicle Motion Control
(2012)
Wang, Chun-Chieh
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Cheng, Sheng-Yuan
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Hsiao, Tesheng
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Chou, Wei-Yao
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交大名義發表
This paper presents a motion control scheme for a front-wheel-steering/rear-wheel-driving (FWS/RWD) vehicle using the optimum tire force distribution method. The proposed control system is divided into three layers: the upper controller, the optimum tire force distribution algorithm, and the lower controller. When the driver commands the vehicle, the upper controller computes the total desired longitudinal tire force, lateral tire force, and the desired yaw moment which allow the vehicle to follow the given reference trajectory. Then the desired forces and moments are optimally distributed to the four wheels, and the lower controller compensates for the nonlinear and uncertain characteristics of the tire dynamics to generate the desired forces. Finally, simulations are carried out to verify the effectiveness of the proposed control scheme.
Clustering of Laser Measurements via the Dirichlet Process Mixture Model for Object Tracking
(2012)
Lee, Yung-Chou
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Hsiao, Tesheng
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Chang, Chih-Tang
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交大名義發表
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National Chiao Tung University
In this paper, the Dirichlet process mixture model is used to describe the distribution of the whole laser measurements in a given scan. Then the number of clusters is inferred from the measurements by the Gibbs sampler. We focus on the automotive application which usually has a more complex environment. Due to the variant shapes and sizes of the real traffic objects, the multi-class DP-based clustering model, which is incorporated with a mixture prior distribution, is proposed to cluster the measurements more properly. The clustering results of the proposed method are compared with those of several existing clustering methods both in an expressway case and in an urban road case. The corresponding tracking performances are also analyzed and the improvements of the proposed method are presented.
Detection and Identification of Actuator Faults in Robotic Systems Based on Multiple-Model Nonlinear State Estimation
(2009)
Hsiao, Tesheng
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Haung, Huei-jyun
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電控工程研究所
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Institute of Electrical and Control Engineering
Modern robotic systems perform elaborate tasks in a complicated environment and have close interactions with humans. Therefore fault detection and isolation (FDI) systems must be carefully designed and implemented on robots in order to guarantee safe and reliable operations. In addition, many high performance robotic controllers require full state feedback; hence it is essential to implement state estimators whenever not all state variables are measurable. Moreover, the state estimator must work properly despite the presence of faults so that the robot is fault tolerable. In this paper, we propose an algorithm for state estimation, fault detection, and fault identification of a robotic system. All faults in consideration are associated with a set of exclusive fault modes. Then a multiple-model nonlinear state estimator is applied to estimate not only the state but also the fault mode of the robot at each time step. Furthermore all fault modes are organized in a hierarchical structure to alleviate the computation load. Simulations show that state estimation is accurate even in the event of actuator faults, and that the occurrence of faults is detected immediately. The computational advantage of the proposed hierarchical structure is also demonstrated by the simulations.
Detection and identification of sensor failures in vehicle lateral control systems
(2007)
Hsiao, Tesheng
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電控工程研究所
;
Institute of Electrical and Control Engineering
The vehicle lateral control system performs lane-keeping and lane-changing tasks on automated highways. The controller acquires the vehicle's lateral positions through two sets of magnetometers. Failures on either set of magnetometers may cause instability of the system and jeopardize safety of the passengers. Hence it is of crucial importance to design and implement a fault detection and identification (FDI) mechanism which is responsible for handling the magnetometers' malfunctions. In this paper, we propose an FDI scheme consisting of two switching observers and a probabilistic inference algorithm. The existence of failures is detected by evaluating the posterior probabilities of fault occurrences, while the sizes and types of the faults are estimated by the observers. Simulation results show that sensor failures can be detected and identified in a timely manner. Moreover the lateral control system maintains stability, and an acceptable level of performance is achieved in the presence of sensor failures.
Direct Longitudinal Tire Force Control under Simultaneous Acceleration/Deceleration and Turning
(2013)
Hsiao, Tesheng
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電機工程學系
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Department of Electrical and Computer Engineering
Slip-ratio-based controllers are incompetent for precise longitudinal tire force tracking due to non-parametric model uncertainties. The tracking performance further deteriorates whenever the tire is simultaneously accelerating /decelerating and turning. In this paper, we propose an observer-based direct longitudinal tire force control scheme which guarantees achievement of the desired longitudinal tire force robustly with respect to tire model uncertainties, variations in road conditions, and simultaneous lateral tire motions. Convergence of the force estimation/tracking errors is analyzed rigorously by the Lyapunov method. Simulations demonstrate the satisfactory performance on both tire force estimation and tracking.
Frequency Constrained Adaptive PID Laws for Motion Control Systems
(2017-01-01)
Hsiao, Tesheng
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Cheng, Chung-Chiang
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電機工程學系
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Department of Electrical and Computer Engineering
The proportional-integral-derivative (PID) controller is widely used in motion control systems due to its simplicity and effectiveness. To achieve satisfactory performance, the PID parameters must be properly tuned. Although numerous PID tuning methods were investigated in the past, most of them were based on either time-domain or frequency-domain responses, while integration of features in both domains for PID tuning was less addressed. However, many industrial practitioners still found it difficult to compromise multiple conflicting control objectives, such as fast responses, small overshoot and tracking errors, and good robustness, with PID controllers. Moreover, it is desirable to adjust PID parameters online such that plant variations and unexpected disturbances can be compensated for more efficiently. In view of these requirements, this paper proposes an adaptive PID control law that updates its parameters online by minimizing the time-domain tracking errors subject to frequency-domain constraints that are imposed for loop shaping. By combining optimization criteria in both time and frequency domains for online parameter adjustment, the proposed PID controller can achieve good tracking performance with adequate robustness margin. Then the proposed PID law is applied to control an XZ-table driven by AC servo motors. Experimental results show that the tracking performance of the proposed controller is superior to that of a constant-gain PID controller whose parameters were tuned by the commercial Matlab/Simulink PID tuner.
Identification of time-varying autoregressive systems using maximum a posteriori estimation
(2008-08-01)
Hsiao, Tesheng
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電控工程研究所
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Institute of Electrical and Control Engineering
Time-varying systems and nonstationary signals arise naturally in many engineering applications, such as speech, biomedical, and seismic signal processing. Thus, identification of the time-varying parameters is of crucial importance in the analysis and synthesis of these systems. The present time-varying system identification techniques require either demanding computation power to draw a large amount of samples (Monte Carlo-based methods) or a wise selection of basis functions (basis expansion methods). In this paper, the identification of time-varying autoregressive systems is investigated. It is formulated as a Bayesian inference problem with constraints on the conditional and prior probabilities of the time-varying parameters. These constraints can be set without further knowledge about the physical system. In addition, only a few hyper parameters need tuning for better performance. Based on these probabilistic constraints, an iterative algorithm is proposed to evaluate the maximum a posteriori estimates of the parameters. The proposed method is computationally efficient since random sampling is no longer required. Simulation results show that it is able to estimate the time-varying parameters reasonably well and a balance between the bias and variance of the estimation is achieved by adjusting the hyperparameters. Moreover, simulation results indicate that the proposed method outperforms the particle filter in terms of estimation errors and computational efficiency.
Integrated Estimation of Vehicle States, Tire Forces, and Tire-Road Friction Coefficients
(2014)
Hsiao, Tesheng
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Lan, Jing-Yuan
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Yang, Hanping
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電機工程學系
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電控工程研究所
Information about the vehicle states (e.g. the sideslip angle and the longitudinal velocity), tire forces, and tire-road friction coefficients is crucial for advanced vehicular active safety systems. In particular, the latest high-performance control system based on optimally distributing and controlling all tire forces requires feedback of the tire forces and the tire-road friction coefficient of each individual tire. Therefore, estimating all or parts of the vehicle/tire/road states from available sensor measurements in production vehicles has long been an active research topic in both academia and industry. Despite recent advances in automotive estimation technologies, it remains an open question on identifying the tire-road friction coefficient of each individual tire. This paper proposes an integrated estimation system that provides accurate estimates of the vehicle/tire/road states from available sensor measurements. More importantly, the tire-road friction coefficient of each tire can be separately estimated. The performance of the proposed estimation system is verified by simulations based on a complex nonlinear vehicle/tire model.
Iterative Estimation of the Tire-Road Friction Coefficient and Tire Stiffness of Each Driving Wheel
(2016)
Hsiao, Tesheng
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Yang, Je-Wei
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電控工程研究所
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Institute of Electrical and Control Engineering
Real-time estimation of tire-road friction coefficients is essential for vehicular active safety systems to achieve better performance such that safe driving is guaranteed. However, real-time estimation of tire-road friction coefficients is challenging due to highly nonlinear and uncertain tire dynamics, and diverse operating conditions of tires. In this paper, we propose an iterative estimation scheme for each driving wheel based on the modified brush tire model which is suitable for the combined longitudinal and lateral motion and consists of only two parameters, i.e. the longitudinal stiffness and the tire-road friction coefficient. Two separate Lyapunov-based estimators are designed to estimate one parameter at a time, assuming the other is known. Then these two estimators are connected together to perform iterative estimation for both parameters. Simulations for various driving scenarios are carried out, including forward acceleration, simultaneous acceleration and turning, and split-mu test. The results show that satisfactory performance is achieved for each driving wheel despite the existence of non-parametric tire model uncertainties.
Joint Torque Estimation of a Powered Exoskeleton Under Compliance Control Loop
(2018-01-01)
Liang, Chiawei
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Hsiao, Tesheng
;
Hsiao, Chin-Chi
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電機工程學系
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電控工程研究所
Exoskeletons have been widely used as walking assistive devices for military and rehabilitation purposes. Many exoskeleton control systems detect users' intention by directly sensing their biological signals such as EMG, EEG, force, etc. Therefore, additional sensors are required, resulting in high cost and inconvenience of putting on and taking off the exoskeleton. In this paper, the user's intention is detected by estimating the torque exerted by the user based on exoskeleton dynamics as well as the current and angle of each joint motor; hence, requirements of sensors are diminished. Moreover, we conduct compliance control by modifying joint velocity commands according to the estimated torque and predefined mechanical admittance of each joint. Experiments are carried out to demonstrate that satisfactory estimation of the user's torque can be achieved by the proposed method.
Kinematics-Based Studies on a 7-DOF Redundant Manipulator
(2014)
Liu, Min-Chih
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Tsai, Ho-Hsuan
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Hsiao, Tesheng
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電控工程研究所
;
Institute of Electrical and Control Engineering
Traditional industrial robots require at least 6-degree-of-freedom (DOF) to completely specify the position and orientation of the end-effector in the 3D space. However, extra DOF enables the robot to accomplish more challenging tasks such as obstacle avoidance, torque optimization, fault tolerance, etc. at the price of increasingly complicated kinematics. This paper is aimed at investigating some kinematics-related problems associated with a 7-DOF robot, including forward kinematics, inverse kinematics, Jacobian matrix, and collision detection, to set up the base for future researches.
Object Tracking via the Probability-Based Segmentation Using Laser Range Images
(2010)
Lee, Yung-Chou
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Hsiao, Tesheng
;
交大名義發表
;
National Chiao Tung University
In this paper, a probability-based segmentation approach is presented for object tracking. The proposed approach uses the Dirichlet process mixture model to describe the probabilistic distribution of observations in a single scan of a laserscanner. Then the number of segments is inferred from the observations by the Gibbs sampling method. Moreover each segment is classified into one of the three predefined classes such that most of non-vehicle-like objects on the roadsides can be filtered out. Then, the tracking algorithm, called Joint Integrated Probabilistic Data Association Filter (JIPDAF), is applied to track the classified objects and manage existing tracks. Simulations based on real traffic data demonstrate that the non-vehicle-like objects on the roadsides are suppressed. Since the number of objects in the tracking step is decreased, the computation load of the tracking step is decreased.
Online Motion Adjustment Using Compliance Control for a Multi-Axis Robot Manipulator
(2017-01-01)
Reyes-Uquillas, Daniel A.
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Hsiao, Tesheng
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電機工程學系
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Department of Electrical and Computer Engineering
The demand for more human-robot interactions is increasing rapidly. Safety of these interactions is a crucial concern and is closely related to the design of the control system. When the robot is affected by an external force, in order to have a safe interaction, it is necessary to regulate the speed and position changes that may occur due to this expected or unexpected force. This work focuses on robot compliance control that can online adjust the motion of the robot manipulator according to the external force applied to the end effector. The proposed control law calculates the Jacobian matrix and modifies the task-space velocities of the end-effector with respect to the prescribed force/velocity relationship, i.e. the admittance law; then the desired position is achieved by the computed-torque control in the joint space. Simulations of the proposed algorithm show that the trajectory of the end effector can be modified online based on the magnitude and direction of the external force. These results can be further applied to lead-through teaching applications where we want the robot to comply with the force to achieve the specified task.
Robust Estimation and Control of Tire Traction Forces
(2013-03-01)
Hsiao, Tesheng
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電機資訊學士班
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Undergraduate Honors Program of Electrical Engineering and Computer Science
Due to increasingly demanding requirements for driving safety, precise and robust control of tire traction forces is essential to implement high-performance vehicle-control systems. However, traditional slip-ratio-based methods cannot guarantee achievement of desired traction forces due to a lack of tire force feedback. This paper proposes an observer-based traction force control scheme that is robust with respect to variations of road conditions and tire model uncertainties. Moreover, achievement of desired traction forces is guaranteed. Simulation results verify the robust and satisfactory performance, even under suddenly changing road conditions, as well as parametric and nonparametric tire model uncertainties.
Robust Estimation of the Friction Forces Generated by Each Tire of a Vehicle
(2011)
Hsiao, Tesheng
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Liu, Nien-Chi
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Chen, Syuan-Yi
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電機工程學系
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Department of Electrical and Computer Engineering
Real-time information of tire friction forces is invaluable for vehicle control systems, such as ABS and the electronic stability program (ESP), to achieve better stability and maneuverability. To estimate tire forces on-line, this paper proposes a robust tire force estimation algorithm which is able to identify the longitudinal and lateral tire forces of each individual wheel. In addition, the estimation results are robust w.r.t variations in vehicle parameters. The dependency between the longitudinal and lateral tire forces is explicitly taken into account by incorporating friction ellipses into the estimation algorithm. Simulations based on a 14-degree-of-freedom nonlinear vehicle model are conducted and the results are satisfactory, even in the presence of sudden changes of the road conditions and variations in vehicle parameters.