Mien Van

Finite Time Fault Tolerant Control for Robot Manipulators Using Time Delay Estimation and Continuous Nonsingular Fast Terminal Sliding Mode Control

In this paper, a novel finite time fault tolerant control (FTC) is proposed for uncertain robot manipulators with actuator faults. First, a finite time passive FTC (PFTC) based on a robust nonsingular fast terminal sliding mode control (NFTSMC) is investigated. Be analyzed for addressing the disadvantages of the PFTC, an AFTC are then investigated by combining NFTSMC with a simple fault diagnosis scheme. In this scheme, an online fault estimation algorithm based on time delay estimation (TDE) is proposed to approximate actuator faults. The estimated fault information is used to detect, isolate, and accommodate the effect of the faults in the system. Then, a robust AFTC law is established by combining the obtained fault information and a robust NFTSMC. Finally, a high-order sliding mode (HOSM) control based on super-twisting algorithm is employed to eliminate the chattering. In comparison to the PFTC and other state-of-the-art approaches, the proposed AFTC scheme possess several advantages such as high precision, strong robustness, no singularity, less chattering, and fast finite-time convergence due to the combined NFTSMC and HOSM control, and requires no prior knowledge of the fault due to TDE-based fault estimation. Finally, simulation results are obtained to verify the effectiveness of the proposed strategy.In this paper, a novel finite time fault tolerant control (FTC) is proposed for uncertain robot manipulators with actuator faults. First, a finite time passive FTC (PFTC) based on a robust nonsingular fast terminal sliding mode control (NFTSMC) is investigated. Be analyzed for addressing the disadvantages of the PFTC, an AFTC are then investigated by combining NFTSMC with a simple fault diagnosis scheme. In this scheme, an online fault estimation algorithm based on time delay estimation (TDE) is proposed to approximate actuator faults. The estimated fault information is used to detect, isolate, and accommodate the effect of the faults in the system. Then, a robust AFTC law is established by combining the obtained fault information and a robust NFTSMC. Finally, a high-order sliding mode (HOSM) control based on super-twisting algorithm is employed to eliminate the chattering. In comparison to the PFTC and other state-of-the-art approaches, the proposed AFTC scheme possess several advantages such as high precision, strong robustness, no singularity, less chattering, and fast finite-time convergence due to the combined NFTSMC and HOSM control, and requires no prior knowledge of the fault due to TDE-based fault estimation. Finally, simulation results are obtained to verify the effectiveness of the proposed strategy.

Wavelet Kernel Local Fisher Discriminant Analysis With Particle Swarm Optimization Algorithm for Bearing Defect Classification

Feature extraction and dimensionality reduction (DR) are necessary and helpful preprocessing steps for bearing defect classification. Linear local Fisher discriminant analysis (LFDA) has recently been developed as a popular method for feature extraction and DR. However, the linear method tends to give undesired results if the samples between classes are nonlinearly separated in the input space. To enhance the performance of LFDA in bearing defect classification, a new feature extraction and DR algorithm based on wavelet kernel LFDA (WKLFDA) is presented in this paper. Herein, a new wavelet kernel function is proposed to construct the kernel function of LFDA. To seek the optimal parameters for WKLFDA, particle swarm optimization (PSO) is used; as a result, a new PSO-WKLFDA algorithm is proposed. The experimental results for the synthetic data and measured vibration bearing data show that the proposed WKLFDA and PSO-WKLFDA outperform other state-of-the-art algorithms.

Bearing Defect Classification Based on Individual Wavelet Local Fisher Discriminant Analysis with Particle Swarm Optimization

In order to enhance the performance of bearing defect classification, feature extraction and dimensionality reduction have become important. In order to extract the effective features, wavelet kernel local fisher discriminant analysis (WKLFDA) is first proposed; herein, a new wavelet kernel function is proposed to construct the kernel function of LFDA. In order to automatically select the parameters of WKLFDA, a particle swarm optimization (PSO) algorithm is employed, yielding a new PSO-WKLFDA. When compared with the other state-of-the-art methods, the proposed PSO-WKLFDA yields better performance. However, the use of a single global transformation of PSO-WKLFDA for the multiclass task does not provide excellent classification accuracy due to the fact that the projected data still significantly overlap with each other in the projected subspace. In order to enhance the performance of bearing defect classification, a novel method is then proposed by transforming the multiclass task into all possible binary classification tasks using a one-against-one (OAO) strategy. Then, individual PSO-WKLFDA (I-PSO-WKLFDA) is used for extracting effective features of each binary class. The extracted effective features of each binary class are input to a support vector machine (SVM) classifier. Finally, a decision fusion mechanism is employed to merge the classification results from each SVM classifier to identify the bearing condition. Simulation results using synthetic data and experimental results using different bearing fault types show that the proposed method is well suited and effective for bearing defect classification.

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

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.

Second Order Sliding Mode-Based Output Feedback Tracking Control for Uncertain Robot Manipulators

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.

Backstepping quasi-continuous high-order sliding mode control for a Takagi–Sugeno fuzzy system with an application for a two-link robot control

A new control scheme is proposed for motion tracking of a Takagi–Sugeno fuzzy system using the backstepping quasi-continuous high-order sliding mode (HOSM) control technique. First, a Takagi–Sugeno fuzzy model is used to represent the original second-order nonlinear system; most of the parameters for this model can be computed offline. Next, a conventional backstepping sliding mode control (BSMC) is designed to stabilize and guarantee the exact motion tracking for the Takagi–Sugeno fuzzy system. However, use of the conventional sliding mode control generates significant chattering. Therefore, a quasi-continuous second-order sliding mode (QC2S) control is employed to reduce chattering and obtain higher tracking precision, resulting in a backstepping quasi-continuous second-order sliding mode (BQC2S) control law. Combining the Takagi–Sugeno fuzzy model with the BQC2S controller results in a controller scheme that preserves the advantages of both techniques, such as the low online computational burden of the Takagi–Sugeno fuzzy model, and the low chattering, robustness, and fast transient response of the BQC2S controller. Finally, the proposed controller is used to control a two-link robot manipulator and is compared with the existing approaches. Simulation results are presented to demonstrate the effectiveness of the proposed methodology.

Fault Diagnosis in Image-Based Visual Servoing With Eye-in-Hand Configurations Using Kalman Filter

In this paper, the fault diagnosis (FD) problem in image-based visual servoing with eye-in-hand configurations is investigated. The potential failures are detected and isolated based on approximating parameters related. First, the failure scenarios of the visual servoing systems are reviewed and classified into the actuator and sensor faults. Second, a residual generator is proposed to detect the failure occurrences, based on the Kalman filter. Third, a decision table is proposed to isolate the fault type. Finally, simulation and experimental results are given to validate the efficacy and the efficiency of the proposed FD strategies.

Robust fault-tolerant control for uncertain robot manipulators based on adaptive quasi-continuous high-order sliding mode and neural network

This paper investigates a robust fault-tolerant control scheme for uncertain robot manipulators. The proposed scheme is designed via active fault-tolerant control method by combining a fault estimation scheme with a novel robust adaptive quasi-continuous second-order sliding mode (AQC2S) controller, so as to accommodate not only system failures but also uncertainties. First, a neural network based fault estimation is designed to online approximate the unknown uncertainties and faults. The estimated uncertainty and fault information are then used to compensate in advance for the effects of uncertainties in fault-free operation and both uncertainties and faults in fault operation. To eliminate the neural network compensation error, QC2S with adaptation gain, named as adaptive QC2S (AQC2S), is proposed. By integrating the advantages of the neural network observer and the AQC2S controller, the integrated scheme has a good capability to accommodate both the uncertainties and faults with chattering-free, higher position tracking accuracy, and no requirement of prior knowledge of the fault information. The stability and convergence of the proposed fault-tolerant control system is proved theoretically. Simulation results for a PUMA560 robot demonstrate the effectiveness of the proposed algorithm.

Novel quasi-continuous super-twisting high-order sliding mode controllers for output feedback tracking control of robot manipulators

In this paper, a robust output feedback tracking control scheme for uncertain robot manipulators with only position measurements is investigated. First, a quasi-continuous second-order sliding mode (QC2S)-based exact differentiator and super-twisting second-order sliding mode (STW2S) controllers are designed to guarantee finite time convergence. Although the QC2S produces continuous control and less chattering than that of a conventional sliding mode controller and other high-order sliding mode controllers, a large amount of chattering exists when the sliding manifold is defined by the equation s = s · = 0 . To decrease the chattering, an uncertainty observer is used to compensate for the uncertainty effects, and this controller may possess a smaller switching gain. Compared to the QC2S controller, the STW2S has less chattering and tracking error when the system remains on the sliding manifold s = s · = 0 . Therefore, to further eliminate the chattering and obtain a faster transient response and higher tracking precision, we develop a quasi-continuous super-twisting second-order sliding mode controller, which integrates both the merits of QC2S and STW2S controllers. The stability and convergence of the proposed scheme are theoretically demonstrated. Finally, computer simulation results for a PUMA560 robot comparing with conventional QC2S and STW2S controllers are shown to verify the effectiveness of the proposed algorithm.

Third Order Sliding Mode Observer based Robust Fault Diagnosis for Robot Manipulators

Abstract: This paper investigates an algorithm for robust fault diagnosis in robot manipulators. The TOSM (Third Order Sliding Mode observer) provides both theoretically exact observation and unknown fault identification without filtration. The EOI (Equivalent Output Injections) of the TOSM observers can be used as residuals for the problem of fault diagnosis and to identify the unknown faults. The obtained fault information can be used for fault detection, isolation as well as fault accommodation to the self-correcting failure system. The computer simulation results for a PUMA 560 robot are shown to verify the effectiveness of the proposed strategy. Keywords: fault detection, fault diagnosis, sliding mode observer, nonlinear model I. INTRODUCTION Various approaches to fault diagnosis in nonlinear systems as well as robot manipulators have been proposed recently. The observer based on normal measurable variables have been approached [1,2]. By using neural network learning, robust fault detection scheme for nonlinear system [3], and for robot manipulators [4,5] have been developed. The basic idea of these methods is to design the robust fault diagnosis by using the model based method, and to use neural network (NN) to approximate the faults involved in the observer design. In [6], a neural-fuzzy model is used to obtain the model based of the unknown dynamic system. One of the best advantages of robust fault diagnoses is that they are not only able to detect the occurrence of a fault, but also can be provided the fault information which is useful for compensating the affect of the faults in the dynamic systems. Due to important feature of the sliding mode in the system uncertainties such as handling disturbances and modeling uncertainties through the concepts of sliding surface design and equivalent control, SM techniques have been studied for observer states by many researchers [7,8]. However, in SM applications, chattering is the major drawback in the practical realization. To avoid chattering, different approaches have been proposed [9-11]. The most widely used in practical applications to eliminate the chattering are using higher order sliding mode [12,13]. Especially, second order sliding mode [14], for instance, sub-optimal algorithm [15], super-twisting algorithm have been proposed for states observer [16,17]. However, in the second order sliding mode approach, the unknown input is constructed from the discontinuous term which provides the undesired chattering. Hence, to reduce the chattering, the filtration is required in these designs to obtain the unknown input. On the other hand, the filtration provides the delay and error that reduce the fault estimation performance. To avoid filtration which is required of second order sliding mode, the third order sliding mode observer is investigated [18,19]. In [20], the third-order sliding mode observer is designed to estimate the velocities and external perturbation. The obtained estimation of an external perturbation is used to design the controller to compensate the effect of external perturbation in the system. This paper extends earlier results of our previous work [19], the third-order sliding mode based robust fault diagnosis scheme is designed. The fault information is constructed directly from the equivalent output injection (EOI) of SM without filtration. The obtained fault estimation is used for fault detection, isolation as well as fault accommodation. To verify the effectiveness of the third order sliding mode to fault diagnosis, the simulation is performed on PUMA 560 robot. The remainder of this paper is organized as follows: in section II, the robot dynamics and faults are investigated and problems are given. In section III, the fault diagnosis scheme is designed. The simulation results for a PUMA 560 robot is described in section IV. Section V includes some conclusions. II. PROBLEM FORMULATION Let consider a robot dynamics is described by