Showing papers by "Hee-Jun Kang published in 2013"

A robust fault diagnosis and accommodation scheme for robot manipulators

Abstract: This paper investigates an algorithm for robust fault diagnosis (FD) in uncertain robotic systems by using a neural sliding mode (NSM) based observer strategy. A step by step design procedure will be discussed to determine the accuracy of fault estimation. First, an uncertainty observer is designed to estimate the uncertainties based on a first neural network (NN1). Then, based on the estimated uncertainties, a fault diagnosis scheme will be designed by using a NSM observer which consists of both a second neural network (NN2) and a second order sliding mode (SOSM), connected serially. This type of observer scheme can reduce the chattering of sliding mode (SM) and guarantee finite time convergence of the neural network (NN). The obtained fault estimations are used for fault isolation as well as fault accommodation to self-correct the failure systems. The computer simulation results for a PUMA560 robot are shown to verify the effectiveness of the proposed strategy.

A New Full Pose Measurement Method for Robot Calibration

Abstract: Identification of robot kinematic errors during the calibration process often requires accurate full pose measurements (position and orientation) of robot end-effectors in Cartesian space. This paper proposes a new method of full pose measurement of robot end-effectors for calibration. This method is based on an analysis of the features of a set of target points (placed on a rotating end-effector) on a circular trajectory. The accurate measurement is validated by computational simulation results from the Puma robot. Moreover, experimental calibration and validation results for the Hyundai HA-06 robot prove the effectiveness, correctness, and reliability of the proposed method. This method can be applied to robots that have entirely revolute joints or to robots for which only the last joint is revolute.

Chattering-Free Neuro-Sliding Mode Control of 2-DOF Planar Parallel Manipulators

Abstract: This paper proposes a novel chattering free neuro-sliding mode controller for the trajectory tracking control of two degrees of freedom (DOF) parallel manipulators which have a complicated dynamic model, including modelling uncertainties, frictional uncertainties and external disturbances. A feedforward neural network (NN) is combined with an error estimator to completely compensate the large nonlinear uncertainties and external disturbances of the parallel manipulators. The online weight tuning algorithms of the NN and the structure of the error estimator are derived with the strict theoretical stability proof of the Lyapunov theorem. The upper bound of uncertainties and the upper bound of the approximation errors are not required to be known in advance in order to guarantee the stability of the closed-loop system. The example simulation results show the effectiveness of the proposed control strategy for the tracking control of a 2-DOF parallel manipulator. It results in its being chattering-free, very small tracking errors and its robustness against uncertainties and external disturbances.

A novel neural second-order sliding mode observer for robust fault diagnosis in robot manipulators

Abstract: This paper investigates an algorithm for fault diagnosis in robot manipulators using a novel neural second-order sliding mode observer. Differently from the conventional neural network observer and first-order sliding mode observer for the robust fault estimation schemes, the second-order sliding mode observer is first designed and compared with them. Although the second-order sliding mode observer converges faster and with less error than each of the neural network and the first-order sliding mode observer does, it requires prior knowledge of the upper bound of the fault function. Because of this disadvantage, a neural second-order sliding mode observer is designed, which combines the second-order sliding mode observer with the neural network observer. The resulting observer not only preserves the features of the second-order sliding mode observer but also can improve it by removing the need for prior knowledge of the fault function upper bound. Computer simulation results for a PUMA560 industrial robot are also shown to verify the effectiveness of the proposed strategy.

Second order sliding mode-based output feedback tracking control for uncertain robot manipulators

Abstract: In this paper, a robust output feedback tracking control scheme for motion control of uncertain robot manipulators without joint velocity measurement based on a second-order sliding mode (SOSM) observer is presented. Two second-order sliding mode observers with finite time convergence are developed for velocity estimation and uncertainty identification, respectively. The first SOSM observer is used to estimate the state vector in finite time without filtration. However, for uncertainty identification, the values are constructed from the high switching frequencies, necessitating the application of a filter. To estimate the uncertainties without filtration, a second SOSM-based nonlinear observer is designed. By integrating two SOSM observers, the resulting observer can theoretically obtain exact estimations of both velocity and uncertainty. An output feedback tracking control scheme is then designed based on the observed values of the state variables and the direct compensation of matched modelling uncertainty using their identified values. Finally, results of a simulation for a PUMA560 robot are shown to verify the effectiveness of the proposed strategy.

Output feedback tracking control of uncertain robot manipulators via higher-order sliding-mode observer and fuzzy compensator

Abstract: This paper develops a new method to control uncertain robot manipulators by using only position measurements. The controller is designed based on a combination of a computed torque controller (CTC) with a higher-order sliding-mode observer and a fuzzy compensator. First, three higher-order sliding-mode (SM) observers (second-order SM, third-order SM and third-order SM linear (TOSML) observers) are designed and compared to verify whether the TOSML observer is the best for observing velocity and identifying uncertainty. A combined CTC-TOSML controller was then designed. Although this controller scheme can overcome the drawbacks of conventional CTCs, its tracking performance can still be improved. To enhance capability of the tracking performance, a CTC-TOSML controller plus fuzzy compensator called a CTC-TOSML-Fuzzy controller is proposed. The proposed controller increases the potential of the CTC for real robot applications. Finally, computer simulation results on a PUMA560 robot are discussed to verify the effectiveness of the proposed strategy.

A novel fuzzy second-order sliding mode observer-controller for a T-S fuzzy system with an application for robot control

Abstract: This paper investigates an algorithm for the tracking performance of a Takagi-Sugeno (T-S) fuzzy system using the second-order sliding mode observer/controller technique. First, the original second-order nonlinear system is represented by a T-S fuzzy model, in which most of the parameters can be computed offline. A novel fuzzy second-order sliding mode observer (FSOSMO), which combines the T-S fuzzy model and the second-order sliding mode observer (SOSMO), is then designed to estimate the velocity. Also, a new fuzzy second-order sliding mode control (FSOSMC), which combines the T-S fuzzy model and the second-order sliding mode control (SOSMC), is proposed to stabilize and guarantee the exact motion tracking for the T-S fuzzy system. By integrating the T-S fuzzy model with SOSMO/C, the resulting observer/controller scheme preserves the advantages of both techniques, such as the low online computational burden of the T-S fuzzy model, and low chattering, fast response, and finite time convergence of the SOSMO/C. Moreover, the stability and convergence of the proposed closed loop observer-based controller strategy is theoretically proven by the Lyapunov method. Finally, the simulation results of a two-link robot manipulator are presented to demonstrate the effectiveness of the proposed approach.

Distance estimation using inertial sensor and vision

Abstract: A simple distance estimation algorithm using inertial sensors and a mono camera is proposed. Two images of a target are obtained by moving a mono camera. The movement of the camera is estimated using inertial sensors and used as the baseline for the distance estimation. Through experiments, the accuracy of the proposed method is evaluated, where the distance error is 3.9% on average in a few meter ranges.

Robot Geometric Parameter Identification with Extended Kalman Filtering Algorithm

Abstract: This paper proposes a calibration method for enhancing position accuracy of robotic manipulators. In order to increase the robot accuracy, the method first develops a robot kinematic model and then identifies the robot geometric parameters by using an extended Kalman filtering (EFK) algorithm. The Kalman filter has advantages in identifying geometric parameters from the noisy measurements. Therefore, the obtained kinematic parameters are more precise. A simulation study of this calibration is performed for a PUMA 560 robot to prove the effectiveness of the method in increasing robot position accuracy.

A method for optimal kinematic design of five-bar planar parallel manipulators

Abstract: In this paper, an optimal kinematic design method for symmetrical five-bar planar parallel manipulators is presented. The proposed design method is implemented in two steps. First, an optimal configuration is achieved, resulting in a set of closed-form parametric relationships. Second, a searching algorithm is proposed for finding the link lengths of the manipulator to maximize usable workspace. In this usable workspace there is no singularity configuration, and also a good dexterity is satisfied. A design example is included to illustrate the effectiveness of the proposed method.

Fault tolerant control for robot manipulators using neural network and second-order sliding mode observer

Abstract: This paper investigates an algorithm for fault tolerant control of uncertain robot manipulator with only joint position measurement using neural network and second-order sliding mode observer. First, a neural network (NN) observer is designed to estimate the modeling uncertainties. Based on the obtained uncertainty estimation, a second-order sliding mode observer is then designed for two purposes: 1) Providing the velocity estimation, 2) providing the fault information that is used for fault detection, isolation and identification. Finally, a fault tolerant control scheme is proposed for compensating the effect of uncertainties and faults based on the fault estimation information. Computer simulation results on a PUMA560 industrial robot are shown to verify the effectiveness of the proposed strategy.

An Adaptive Controller Using Wavelet Network for Five-Bar Manipulators with Deadzone Inputs

Abstract: In this paper, an adaptive model-based control scheme is proposed for tracking control of five-bar manipulators with deadzone inputs. The proposed controller is based on the combination of nominal dynamic model of the five-bar manipulator, a wavelet network and a deadzone precompensator. The wavelet network and the precompensator are used for compensating the unknown deadzone inputs, modeling errors and uncertainties of the five-bar manipulator. The adaptation laws are derived for tuning parameters of the precompensator and wavelet network. The efficiency of the proposed control scheme is verified by comparative simulations.

Enhancing the Robustness of Fault Isolation Estimator for Fault Diagnosis in Robotic Systems

Abstract: Fault diagnosis and fault tolerant control are increasingly importance in robotic systems. A number of researchers have proposed the generalized observer scheme for fault isolation when a fault happened. One of the key issues in this scheme is based on the sensitive of the residual with the corresponding adaptive threshold. In this paper, we present a new method to derive the adaptive threshold in order to enhance the robustness of fault isolation estimator and reduce the fault isolation time. Mathematical proof and computer simulation are performed to show the effectiveness of the proposed method.