Showing papers in "IEEE-ASME Transactions on Mechatronics in 2018"
TL;DR: In this article, a convolutional neural network (CNN) based approach for fault diagnosis of rotating machinery is presented, which incorporates sensor fusion by taking advantage of the CNN structure to achieve higher and more robust diagnosis accuracy.
Abstract: This paper presents a convolutional neural network (CNN) based approach for fault diagnosis of rotating machinery. The proposed approach incorporates sensor fusion by taking advantage of the CNN structure to achieve higher and more robust diagnosis accuracy. Both temporal and spatial information of the raw data from multiple sensors is considered during the training process of the CNN. Representative features can be extracted automatically from the raw signals. It avoids manual feature extraction or selection, which relies heavily on prior knowledge of specific machinery and fault types. The effectiveness of the developed method is evaluated by using datasets from two types of typical rotating machinery, roller bearings, and gearboxes. Compared with traditional approaches using manual feature extraction, the results show the superior diagnosis performance of the proposed method. The present approach can be extended to fault diagnosis of other machinery with various types of sensors due to its end to end feature learning capability.
449 citations
TL;DR: In this paper, an adaptive fault-tolerant controller is derived by incorporating backstepping control, the barrier Lyapunov function, and Nussbaum gains, which is able to guarantee the satisfaction of the prespecified constraints on the transformed errors, as well as the boundedness of all other closed-loop signals, without resorting to a judicious selection of the control parameters.
Abstract: The science objectives of a spacecraft mission place stringent performance requirements on the spacecraft attitude control system. However, it remains an open problem how to guarantee consistent control performance necessary to meet these requirements, especially in the event of actuator faults and input saturation. Motivated by this fact, in this paper, we address the problem of attitude tracking control with prescribed performance guarantees for a rigid spacecraft subject to unknown but constant inertia parameters, unexpected disturbances, actuator faults, and input saturation. First, certain performance functions specified a priori by the designer are adopted to impose desired performance metrics on the attitude tracking errors. Then, the original attitude tracking error dynamics with performance constraints is transformed into an equivalent “state-constrained” one whose robust stabilization is shown to be sufficient to solve the stated problem via a novel error transformation. Subsequently, based on the transformed system, an adaptive fault-tolerant controller is derived by incorporating backstepping control, the barrier Lyapunov function, and Nussbaum gains. It is proved that the designed controller is able to guarantee the satisfaction of the prespecified constraints on the transformed errors, as well as the boundedness of all other closed-loop signals, without resorting to a judicious selection of the control parameters. Finally, the effectiveness of the proposed control scheme is evaluated by means of simulation experiments carried out on a microsatellite.
240 citations
TL;DR: A new fault detector based on a recently developed unsupervised learning method, denoising autoencoder (DAE), which offers the learning of robust nonlinear representations from data against noise and input fluctuation is proposed.
Abstract: Data-driven approaches have gained increasing interests in the fault detection of wind turbines (WTs) due to the difficulty in system modeling and the availability of sensor data. However, the nonlinearity of WTs, uncertainty of disturbances and measurement noise, and temporal dependence in time-series data still pose grand challenges to effective fault detection. To this end, this paper proposes a new fault detector based on a recently developed unsupervised learning method, denoising autoencoder (DAE), which offers the learning of robust nonlinear representations from data against noise and input fluctuation. A DAE is used to build a robust multivariate reconstruction model on raw time-series data from multiple sensors, and then, the reconstruction error of the DAE trained with normal data is analyzed for fault detection. In addition, we apply the sliding-window technique to consider temporal information inherent in time-series data by including the current and past information within a small time window. A key advantage of the proposed approach is the ability to capture the nonlinear correlations among multiple sensor variables and the temporal dependence of each sensor variable simultaneously, which significantly enhanced the fault detection performance. Simulated data from a generic WT benchmark and field supervisory control and data acquisition data from a real wind farm are used to evaluate the proposed approach. The results of two case studies demonstrate the effectiveness and advantages of our proposed approach.
193 citations
TL;DR: In this article, a particle swarm optimization-based variational mode decomposition method was proposed for fault detection in rotating machinery, which adopts the minimum mean envelope entropy to optimize the parameters (α$ and K$ ) in the existing variational decomposition.
Abstract: The vibration signals of faulty rotating machinery are typically nonstationary, nonlinear, and mixed with abundant compounded background noise. To extract the potential excitations from the observed rotating machinery, signal demodulation and time–frequency analysis are indispensable. This work proposes a novel particle swarm optimization-based variational mode decomposition method, which adopts the minimum mean envelope entropy to optimize the parameters ( $\alpha$ and $K$ ) in the existing variational mode decomposition. The proposed fault-detection framework separated the observed vibration signals into a series of intrinsic modes. A certain number of the intrinsic modes are then selected by means of the Hilbert transform-based square envelope spectral kurtosis. Subsequently, in this study, the feature representations were reconstructed via the selected intrinsic modes; then, the envelope spectra of the real faulty conditions were generated in the rotating machinery. To verify the performance of the proposed method, a testbed platform of a gearbox with a combination of different faults was implemented. The experimental results demonstrated that the proposed method represented the patterns of the fault frequency more explicitly than the available empirical mode decomposition, the local mean decomposition, and the wavelet package transform method.
181 citations
TL;DR: A novel disturbance rejection framework based on a noncascade structure is proposed to simultaneously and accurately estimate multiple disturbances such that a composite controller can be designed to correspondingly compensate disturbances.
Abstract: Permanent magnet synchronous motors are extensively used in high-performance industrial applications. However, plenty of practical factors (e.g., cogging torques, load torques, friction torques, measurement error effects, dead-time effects, and parameter perturbations) in the closed-loop servo system inevitably bring barriers to the high-performance speed regulation, which can be regarded as generalized disturbances. Most of the existing control approaches only focus on one single kind of disturbances. However, the practical servo system is affected by multiple sources of disturbances simultaneously and these disturbances enter into the system through different channels. To this end, this paper systematically analyzes several representative disturbances, particularly including their features and distribution in the practical servo system, and then, specifically puts forward a novel disturbance rejection framework based on a noncascade structure. Under this framework, a comprehensive disturbance observer is proposed to simultaneously and accurately estimate multiple disturbances such that a composite controller can be designed to correspondingly compensate disturbances. Rigorous analysis of stability is established. Comparative experimental results demonstrate that the proposed method achieves a better speed dynamic response and a higher accuracy tracking performance even in the presence of multiple sources of disturbances.
160 citations
TL;DR: A reinforcement learning strategy for manipulation and grasping of a mobile manipulator is described, which reduces the complexity of the visual feedback and handle varying manipulation dynamics and uncertain external perturbations.
Abstract: It is important for humanoid-like mobile robots to learn the complex motion sequences in human–robot environment such that the robots can adapt such motions. This paper describes a reinforcement learning (RL) strategy for manipulation and grasping of a mobile manipulator, which reduces the complexity of the visual feedback and handle varying manipulation dynamics and uncertain external perturbations. Two hierarchies plannings have been considered in the proposed strategy: 1) high-level online redundancy resolution based on the neural-dynamic optimization algorithm in operational space; and 2) low-level RL in joint space. At this level, the dynamic movement primitives have been considered to model and learn the joint trajectories, and then the RL is employed to learn the trajectories with uncertainties. Experimental results on the developed humanoidlike mobile robot demonstrate that the presented approach can suppress the uncertain external perturbations.
156 citations
TL;DR: If and how DL can be applied to infrared thermal (IRT) video to automatically determine the condition of the machine is investigated and it is shown that by using the trained NNs, important regions in the IRT images can be identified related to specific conditions, which can potentially lead to new physical insights.
Abstract: The condition of a machine can automatically be identified by creating and classifying features that summarize characteristics of measured signals. Currently, experts, in their respective fields, devise these features based on their knowledge. Hence, the performance and usefulness depends on the expert's knowledge of the underlying physics or statistics. Furthermore, if new and additional conditions should be detectable, experts have to implement new feature extraction methods. To mitigate the drawbacks of feature engineering, a method from the subfield of feature learning, i.e., deep learning (DL), more specifically convolutional neural networks (NNs), is researched in this paper. The objective of this paper is to investigate if and how DL can be applied to infrared thermal (IRT) video to automatically determine the condition of the machine. By applying this method on IRT data in two use cases, i.e., machine-fault detection and oil-level prediction, we show that the proposed system is able to detect many conditions in rotating machinery very accurately (i.e., 95 and 91.67% accuracy for the respective use cases), without requiring any detailed knowledge about the underlying physics, and thus having the potential to significantly simplify condition monitoring using complex sensor data. Furthermore, we show that by using the trained NNs, important regions in the IRT images can be identified related to specific conditions, which can potentially lead to new physical insights.
154 citations
TL;DR: This paper develops a framework that enables the robot to learn both movement and stiffness features from the human tutor and can be efficiently realized by the proposed framework.
Abstract: One promising approach for robots efficiently learning skills is to learn manipulation skills from human tutors by demonstration and then generalize these learned skills to complete new tasks. Traditional learning and generalization methods, however, have not well considered human impedance features, which makes the skills less humanlike and restricted in physical human–robot interaction scenarios. In particular, stiffness generalization has not been well considered. This paper develops a framework that enables the robot to learn both movement and stiffness features from the human tutor. To this end, the upper limb muscle activities of the human tutor are monitored to extract variable stiffness in real time, and the estimated human arm endpoint stiffness is properly mapped into the robot impedance controller. Then, a dynamic movement primitives model is extended and employed to simultaneously encode the movement trajectories and the stiffness profiles. In this way, both position trajectory and stiffness profile are considered for robot motion control in this paper to realize a more complete skill transfer process. More importantly, stiffness generalization and movement generalization can be efficiently realized by the proposed framework. Experimental tests have been performed on a dual-arm Baxter robot to verify the effectiveness of the proposed method.
150 citations
TL;DR: A novel handling on angle-of-attack (AOA) is proposed with the help of barrier functions, and Corresponding analysis shows that both the limited AOA and the desired performance can be guaranteed despite PLOE of actuators.
Abstract: This paper designs a high-performance adaptive controller for the uncertain model of hypersonic flight vehicles (HFVs) proceed by faulty and hysteretic actuators. A parameterized HFV model is derived, based on which adaptive tracking controllers for velocity and altitude are designed, sequentially. Compared with other adaptive strategies that mainly focus on the parametric uncertainty and asymptotic tracking, utilization of the prescribed performance control technique can largely improve the transient characteristics of HFVs. A novel handling on angle-of-attack (AOA) is proposed with the help of barrier functions. As a result, the magnitude of AOA is limited to match the requirement of the scramjet. Partial loss of effectiveness (PLOE) of actuators is also taken into account, while the backlash hysteresis in aerodynamic control surfaces is accommodated by an adaptive inverse compensation. Corresponding analysis shows that both the limited AOA and the desired performance can be guaranteed despite PLOE of actuators. A simulation study is provided to verify the effectiveness of the proposed controller.
147 citations
TL;DR: In this article, a detailed assessment of optimization-driven moving horizon estimation (MHE) framework by means of a reduced electrochemical model is conducted for state-of-charge estimation, the standard MHE and two variants in the framework are examined by a comprehensive consideration of accuracy, computational intensity, effect of horizon size, and fault tolerance.
Abstract: Efficient battery condition monitoring is of particular importance in large-scale, high-performance, and safety-critical mechatronic systems, e.g., electrified vehicles and smart grid. This paper pursues a detailed assessment of optimization-driven moving horizon estimation (MHE) framework by means of a reduced electrochemical model. For state-of-charge estimation, the standard MHE and two variants in the framework are examined by a comprehensive consideration of accuracy, computational intensity, effect of horizon size, and fault tolerance. A comparison with common extended Kalman filtering and unscented Kalman filtering is also carried out. Then, the feasibility and performance are demonstrated for accessing internal battery states unavailable in equivalent circuit models, such as solid-phase surface concentration and electrolyte concentration. Ultimately, a multiscale MHE-type scheme is created for State-of-Health estimation. This study is the first known systematic investigation of MHE-type estimators applied to battery management.
147 citations
TL;DR: Experimental results show that the proposed sensorless scheme for PMSM exhibits greater stability at lower speed than the classical SMO under parameter detuning and a phase-locked loop like speed estimation method is proposed.
Abstract: In order to reduce the adverse effect of parameter variation in position sensorless speed control of permanent magnet synchronous motor (PMSM) based on stator feedforward voltage estimation (FFVE), multiparameter estimation using a model reference adaptive system is proposed. Since the FFVE scheme relies on motor parameters, the stator resistance and rotor flux linkage are estimated and continuously updated in the FFVE model in a closed-loop fashion, and the sensitivity to multiparameter changes at low speed is eliminated. To improve the dynamics and stability of the overall system and eliminate transient oscillations in speed estimation, a phase-locked loop like speed estimation method is proposed, which is obtained by passing the q -axis proportional integrator (PI) current regulator output through a first-order filter in the FFVE scheme. The proposed control method is similar to V/f control as in induction motors; therefore, starting from zero speed is possible. The experimental tests are implemented with 1-kW PMSM drive controlled by a TMS320F28335 DSP. The proposed sensorless scheme is also compared with the classical sliding mode observer (SMO). Experimental results show that the proposed sensorless scheme exhibits greater stability at lower speed than the classical SMO under parameter detuning. Experimental results and stability analysis demonstrate the feasibility and effectiveness of the proposed sensorless scheme for PMSM under various load and speed conditions.
TL;DR: Experimental results show that the proposed model can significantly improve the calculation accuracy of core losses of the SSRM, and the accuracy is better than the conventional Epstein frame method.
Abstract: In this paper, a new nonlinear lumped parameter equivalent circuit model is proposed to calculate the core losses of a novel 16/10 segmented rotor switched reluctance motor (SSRM) for belt-driven starter generators. The model investigates the hysteresis, eddy current and anomalous losses by using the method of energy conservation. Four parameters are introduced in the proposed model to consider the effects of saturation and leakage flux in SSRM. They are the incremental leakage inductance, the incremental equivalent winding resistance, the incremental magnetizing inductance, and the incremental equivalent core-loss resistance. This model can overcome the hysteresis effects of winding resistance and leakage inductance on the current, and improve the accuracy of the parameters. To illustrate the advantages of the proposed model, an experiment platform is developed. Experimental results show that the proposed model can significantly improve the calculation accuracy of core losses of the SSRM. The accuracy is better than the conventional Epstein frame method. The proposed core-loss model and analysis method can be applied to other kinds of switched reluctance motors.
TL;DR: A novel, high-power, self-balancing, and passively and software-controlled actively compliant hip exoskeleton that can assist with movement and maintain balance in both the sagittal and frontal planes is developed.
Abstract: Most current hip exoskeletons emphasize assistance for walking rather than stability. The goal of this paper is to develop a novel, high-power, self-balancing, and passively and software-controlled actively compliant hip exoskeleton that can assist with movement and maintain balance in both the sagittal and frontal planes. The developed hip exoskeleton includes powered hip abduction/adduction and hip flexion/extension joints. Each actuation unit employs a modular and compact series elastic actuator (SEA) with a high torque-to-weight ratio. It provides mechanical compliance at the interface between the exoskeleton and the wearer to ensure safety and a natural gait in the coupled wearer-exoskeleton system. A new balance controller based on the extrapolated center of mass concept is presented for maintaining walking stability. This controller reacts to perturbations in balance and produces a compliant guidance force through a combination of the passive elasticity of the SEA and active compliant control based on adaptive admittance control. The function of the hip exoskeleton is not to override human control, but rather to involve the wearer in movement control in order to avoid conflicts between wearer and exoskeleton. Our preliminary experiments on a healthy subject wearing the hip exoskeleton demonstrate the potential effectiveness of the proposed hip exoskeleton and controller for walking balance control.
TL;DR: A novel design optimization framework is proposed, which maximizes the minimum-guaranteed control force/torque for any attitude while incorporating such important and useful aspects as interrotor aerointerference, anisotropic task requirement, gravity compensation, etc.
Abstract: We propose a novel aerial manipulation platform, an omnidirectional aerial robot, that is capable of omnidirectional wrench generation with opportunistically distributed/aligned Sectional rotors. To circumvent the tight thrust margin and weight budget of currently available rotor and battery technologies, we propose a novel design optimization framework, which maximizes the minimum-guaranteed control force/torque for any attitude while incorporating such important and useful aspects as interrotor aerointerference, anisotropic task requirement, gravity compensation, etc. We also provide a closed-form solution of infinity-norm optimal control allocation to avoid rotor saturation with the tight thrust margin. Further, we elaborate the notion of electronic speed controller induced singularity and devise a novel selective mapping algorithm to substantially subdue its destabilizing effect. Experiments are performed to validate the theory, which demonstrate such capabilities not possible with typical aerial manipulation systems, namely, separate translation and attitude control on SE(3), hybrid pose/wrench control with downward force of 60 N much larger than its own weight (2.6 kg), and peg-in-hole teleoperation with a radial tolerance of 0.5 mm.
TL;DR: A model-free method based on an adaptive Kalman filter is developed to accomplish path tracking for a continuum robot using only pressures and tip position, which shows good robustness against the system uncertainty and external disturbance, and lowers the number of sensors.
Abstract: Continuum robots with structural compliance have promising potential to operate in unstructured environments. However, this structural compliance brings challenges to the controller design due to the existence of considerable uncertainties in the robot and its kinematic model. Typically, a large number of sensors are required to provide the controller the state variables of the robot, including the length of each actuator and position of the robot tip. In this paper, a model-free method based on an adaptive Kalman filter is developed to accomplish path tracking for a continuum robot using only pressures and tip position. As the Kalman filter operates only with a two-step algebraic calculation in every control interval, the low computational load and real-time control capability are guaranteed. By adding an optimal vector to the control law, buckling of the robot can also be avoided. Through simulation analysis and experimental validation, this control method shows good robustness against the system uncertainty and external disturbance, and lowers the number of sensors.
TL;DR: In this article, a funnel non-affine controller applying neural approximation for prescribed tracking of air-breathing hypersonic vehicles (AHVs) is proposed, and the desired transient performance and steady-state performance are ensured for both tracking errors.
Abstract: This paper presents a funnel non-affine controller applying neural approximation for prescribed tracking of air-breathing hypersonic vehicles (AHVs). We propose a new funnel control to force velocity and altitude tracking errors to fall within bounded funnels, while the desired transient performance and steady-state performance are ensured for both tracking errors. To handle the non-affine dynamics, a simplified neural controller is addressed for a velocity subsystem based on implicit function theorem, and a new back-stepping control without virtual control laws is exploited for the altitude subsystem via a model transformation combined with low-pass-filter approach. Neural approximations and regulation laws for guaranteeing approximation performance are employed to reject system unknown dynamics. The semiglobally uniformly ultimate boundedness of all the closed-loop system signals is guaranteed via Lyapunov synthesis. Finally, the tracking performance of the proposed control approach is verified by simulation results.
TL;DR: This paper develops an enhanced robust fault tolerant control using a novel adaptive fuzzy proportional-integral-derivative-based nonsingular fast terminal sliding mode (AF-PID-NFTSM) control for a class of second-order uncertain nonlinear systems.
Abstract: This paper develops an enhanced robust fault tolerant control using a novel adaptive fuzzy proportional-integral-derivative-based nonsingular fast terminal sliding mode (AF-PID-NFTSM) control for a class of second-order uncertain nonlinear systems. In this approach, a new type of sliding surface, called proportional-integral-derivative (PID)-nonsingular fast terminal sliding mode (NFTSM) (PID-NFTSM) which combines the benefits of the PID and NFTSM sliding surfaces, is proposed to enhance the robustness and reduce the steady-state error, whilst preserving the great property of the conventional NFTSM controller. A fuzzy approximator is designed to approximate the uncertain system dynamics and an adaptive law is developed to estimate the bound of the approximation error so that the proposed robust controller does not require a need of the prior knowledge of the bound of the uncertainties and faults and the exact system dynamics. The proposed approach is then applied for attitude control of a spacecraft. The simulation results verify the superior performance of the proposed approaches over other existing advanced robust fault tolerant controllers.
TL;DR: A dynamics control strategy is presented, which uses the forward and inverse kinematics of multilevel mapping for motion resolution and compensation, and computes the feedforward torques for the motors using recursive dynamics and “cable force–motor torque” relationship.
Abstract: A cable-driven hyper-redundant manipulator has superior dexterity for confined space applications. However, the modeling and control considering the cables are very complex. In this paper, we established the kinematics and dynamics models and proposed a dynamics control strategy. The multilevel mapping between the motors, cables, joints, and end-effector was first analyzed. The corresponding kinematics equations were derived and solved by combining analytical and numerical methods. Especially, the cable coupling relationship was established and a decoupling method was addressed to compensate the coupled motion between cables. Furthermore, we derived the dynamics equations including the cable forces and the joint variables. Considering practical control requirements, the cables’ forces were distributed by simplifying the dynamics equations and obtaining the minimal solutions. Then, we presented a dynamics control strategy, which uses the forward and inverse kinematics of multilevel mapping for motion resolution and compensation, and computes the feedforward torques for the motors using recursive dynamics and “cable force–motor torque” relationship. Finally, a prototype and a truss inspection experiment system were developed to verify the corresponding models and methods. Experiment results show that the derived kinematic and the dynamic equations, and the proposed dynamic control strategy are effective.
TL;DR: In this paper, a non-resonant type piezoelectric motor with a precise driving ability was proposed, and the operating principle of the proposed motor is different from the previous non-reonant motors using either the clamping and feeding mechanism (inchworm mechanism) or the inertia drive mechanism.
Abstract: A nonresonant-type piezoelectric motor with a precise driving ability was proposed. The operating principle of the proposed motor is different from the previous nonresonant piezoelectric motors using either the clamping and feeding mechanism (inchworm mechanism) or the inertia drive mechanism. An oblique linear motion formed by the hybrid of two bending motions of a sandwich transducer was used to push the runner step-by-step. Two square-wave voltages were applied to the horizontal and vertical PZT elements to obtain the desired oblique linear motion. The mechanism of the proposed piezoelectric motor was illustrated in detail. Then, transient analyses were performed by ANSYS software to simulate the motion trajectory and to find the response characteristics of the motor. Finally, a prototype was fabricated to verify the mechanism and to test the mechanical output characteristics of the proposed motor. Under the input square-wave voltages of 500 V $_{\text{p-p}}$ , the prototype achieved a step displacement of 5.96 μm, a maximum no-load velocity of 59.64 μm/s, and a maximum thrust of 30 N. This paper provides a new mechanism for the design of a nonresonant piezoelectric motor with long stroke and precise driving ability.
TL;DR: This paper proposes a novel approach of RRT in collaboration with a double-tree structure to separate the extension and optimization procedure, and demonstrates improved performance of this approach in comparison with the original RRT and its variants.
Abstract: As a variant of rapidly exploring random tree (RRT), RRT $^*$ is an important improvement of sampling-based algorithms. Although it can provide a feasible planning solution with a higher quality, more resources on optimization are required, resulting in a very slow convergence rate, which cannot satisfy the real-time requirements of most autonomous systems. In this paper, we propose a novel approach of RRT $^*$ in collaboration with a double-tree structure to separate the extension and optimization procedure. In our algorithm, the original RRT is employed to explore the unknown environment and to search feasible connecting areas, represented by piecewise lines. Different from the method of anytime RRT $^*$ , the RRT phase in our method is to find different homotopic paths during each iteration. Thereafter, a modified RRT $^*$ is used to obtain an optimal solution. Simulation results on two benchmarks demonstrate an improved performance of our approach in comparison with the original RRT $^*$ and its variants (e.g., DT-RRT). An additional evaluation on two real robotic systems further proves the efficiency of our approach.
TL;DR: In this article, the authors proposed a new methodology for battery charging control enabling an optimal tradeoff between the charging time and battery state-of-health (SOH) for Li-ion batteries.
Abstract: Charging time and lifetime are important performances for lithium-ion (Li-ion) batteries, but are often competing objectives for charging operations. Model-based charging controls are challenging due to the complicated battery system structure that is composed of nonlinear partial differential equations and exhibits multiple time-scales. This paper proposes a new methodology for battery charging control enabling an optimal tradeoff between the charging time and battery state-of-health (SOH). Using recently developed model reduction approaches, a physics-based low-order battery model is first proposed and used to formulate a model-based charging strategy. The optimal fast charging problem is formulated in the framework of tracking model predictive control (MPC). This directly considers the tracking performance for provided state-of-charge and SOH references, and explicitly addresses constraints imposed on input current and battery internal state. The capability of this proposed charging strategy is demonstrated via simulations to be effective in tracking the desirable SOH trajectories. By comparing with the constant-current constant-voltage charging protocol, the MPC-based charging appears promising in terms of both the charging time and SOH. In addition, this obtained charging strategy is practical for real-time implementation.
TL;DR: A novel method is presented to address the stochastically stability analysis and satisfies a given $H_{2}$ performance index simultaneously and an event-triggered scheme is proposed to determine whether the networks should be updated at the trigger instants decided by the event-threshold.
Abstract: This paper is concerned with the fault detection filtering for complex systems over communication networks subject to nonhomogeneous Markovian parameters. A residual signal is generated that gives a satisfactory estimation of the fault, and an event-triggered scheme is proposed to determine whether the networks should be updated at the trigger instants decided by the event-threshold. Moreover, a random process is employed to model the phenomenon of malicious packet losses. Consequently, a novel method is presented to address the stochastically stability analysis and satisfies a given $H_{2}$ performance index simultaneously. The condition of the existence of the filter design algorithm is derived by a convex optimization approach to estimate the faults and to generate a residual. Finally, the proposed fault detection filtering method is then applied to an industrial nonisothermal continuous stirred tank reactor under realistic network conditions. Simulation results are given to show the effectiveness of the proposed design method and the designed filter.
TL;DR: In this article, a new shared control method for lane keeping assist (LKA) systems of intelligent vehicles is presented, which allows the LKA system to effectively share the control authority with a human driver by avoiding or minimizing the conflict situations between these two driving actors.
Abstract: This paper presents a new shared control method for lane keeping assist (LKA) systems of intelligent vehicles. The proposed method allows the LKA system to effectively share the control authority with a human driver by avoiding or minimizing the conflict situations between these two driving actors. To realize the shared control scheme, the unpredictable driver-automation interaction is explicitly taken into account in the control design via a fictive driver activity variable. This latter is judiciously introduced into the driver–road–vehicle system to represent the driver's need for assistance in accordance with his/her real-time driving activity. Using Lyapunov stability arguments, Takagi–Sugeno fuzzy model-based design conditions are derived to handle not only the time-varying driver activity variable, but also a large variation range of vehicle speed. Both simulation and hardware experiments are presented to demonstrate that the proposed control strategy together with a linear matrix inequality design formulation provide an effective tool to deal with the challenging shared steering control issue. In particular, a fuzzy output feedback control scheme is exploited to achieve the shared control goal without at least two important vehicle sensors. These physical sensors are widely employed in previous works to measure the lateral speed and the steering rate for the control design and real-time implementation. The successful results of this idea of sensor-reduction control has an obvious interest from practical viewpoint.
TL;DR: The Lyapunov theory proves that the proposed VP-CDNN solver can globally converge to an optimal solution to the standard QP problem corresponding to redundant robot manipulators, and the joint-angular-drift problems are solved.
Abstract: In order to solve the joint-angular-drift problems of redundant robot manipulators, a novel varying-parameter convergent-differential neural network (VP-CDNN) is proposed and exploited. To do so, a quadratic program (QP)-based feedback-considered joint-angular-drift-free (FC-JADF) scheme is first designed and presented. The FC-JADF scheme adopted in this paper is composed of an optimization criterion simultaneously optimizing quadratic and linear terms, and a velocity layer kinematic equation with adding feedback. Second, the FC-JADF scheme is formulated as a standard QP. Third, the VP-CDNN is proposed to solve the resultant standard QP problem. The Lyapunov theory proves that the proposed VP-CDNN solver can globally converge to an optimal solution to the standard QP problem corresponding to redundant robot manipulators, and the joint-angular-drift problems are solved. Two computer simulations and physical experiments based on a six-degree-of-freedom Kinova Jaco $^2$ robot, i.e., a starfish path and a cardioid path, verify the effectiveness, accuracy, safety, and practicability of the QP-based FC-JADF scheme and the VP-CDNN solver for solving the joint-angular-drift problems of redundant robot manipulators.
TL;DR: In this article, an advanced fault-tolerant control (FTC) scheme that comprises of higher order sliding mode (HOSM) based observers and controllers is proposed.
Abstract: In general, permanent magnet synchronous motor (PMSM) drives require four sensors (one position, one dc-link voltage, and at least two current sensors) to obtain good dynamic control performance. If an unpredictable fault occurs in any of these sensors, the performance of the drive deteriorates or even becomes unstable. Most of the existing works are limited to fault diagnosis of one or two sensors due to complexity. Therefore, to provide a continuous drive operation regardless of any of the sensor faults, an advanced fault-tolerant control (FTC) scheme that comprises of higher order sliding mode (HOSM) based observers and controllers is proposed. Two HOSM observers and one Luenberger observer are designed to generate the respective residuals and provide the detection of all sensor faults. Moreover, HOSM controllers are developed to ensure finite-time convergence of the error trajectories after the fault reconfiguration. The proposed FTC scheme reduces the existing chattering phenomenon with good performance in terms of convergence speed and steady-state error. Evaluation results on a three-phase PMSM are presented to validate the effectiveness of the proposed FTC approach.
TL;DR: A novel proportional-integral-derivative (PID)-type motion controller for a quadrotor is introduced, and better tracking accuracy is obtained with the introduced nonlinear PID-type algorithm.
Abstract: A novel proportional-integral-derivative (PID)-type motion controller for a quadrotor is introduced in this paper A rigorous analysis of the closed-loop system trajectories is provided, and gain tuning guidelines are discussed Real-time experimental results consisting of the implementation of a PID-based scheme, a sliding-mode controller, and the new scheme are given Gains are selected so that the three tested controllers present the same energy consumption In order to assess the robustness of the controllers tested, experiments are carried out in the presence of disturbances in one of the actuators Specifically, the disturbance consists in attenuating the force delivered Better tracking accuracy is obtained with the introduced nonlinear PID-type algorithm
TL;DR: Control strategy is designed to track the desired sideslip angle and yaw rate and experiment results show that the designed controller can make the vehicle well track the reference model and improve the vehicle maneuverability.
Abstract: A four-wheel steer-by-wire vehicle (FSV), which is a combination of a steer-by-wire (SBW) system and a four wheel steering (4WS) system, not only can improve vehicle safety and maneuverability, but also steering flexibility. Considering the parameters, uncertainties of vehicle speed, and tire cornering stiffness, weighted function is solved to express the uncertain system. Aiming at the multiple-input multiple-output (MIMO) system, the structured singular value $\mu $ is used to research FSV under multiple perturbations in this paper. Based on $\mu $ control strategy, $\mu $ controller is designed to track the desired sideslip angle and yaw rate. Thus, the vehicle gets better performance. Compared with SBW and 4WS systems, FSV has better state response under steering angle step input simulation experiment. The advantages of $\mu $ control compared with $H$ ∞ control on FSV have been explained in the simulation experiments. Furthermore, experiment results show that the designed controller can make the vehicle well track the reference model and improve the vehicle maneuverability.
TL;DR: An inverse multiplicative structure (IMS) is employed to find the inverse of the Krasnoselskii-Pokrovskii model, and a new expression of the KP model is developed, where the input variable is expressed explicitly and can fit into IMS.
Abstract: The Krasnoselskii-Pokrovskii (KP) model, as one of the popular operator-based hysteresis models, is commonly used to describe the hysteresis nonlinearities, especially in the smart materials-based actuators. Due to the complex formulation of the KP model, it is a great challenge to construct the inverse for the KP model. In this paper, an inverse multiplicative structure (IMS) is employed to find the inverse of the KP model. The merit of IMS is that this approach is simple to implement and the detailed knowledge of the hysteresis model is not required. However, the IMS technique cannot be directly applied to construct the inverse compensator for the KP model due to its complexity. Toward this problem, a new expression of the KP model is developed, where the input variable is expressed explicitly. With this new expression, the KP model can fit into IMS. Experiments are conducted to validate the effectiveness of the developed inverse compensator on a piezoelectric platform.
TL;DR: This paper proposes a distributed formation control approach for a team of vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs) subject to switching topologies, and an applied torque is synthesized for the attitude to track the command attitude.
Abstract: This paper proposes a distributed formation control approach for a team of vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs) subject to switching topologies. The communication topology among UAVs is allowed to be with weak connectivity, in the sense of satisfying a uniformly jointly connected assumption. Since VTOL UAV systems are typically underactuated, a hierarchical framework is introduced such that a distributed control scheme can be established using neighboring positions and velocities. In particular, a distributed command force is developed to fulfill the formation objective, and an applied torque is synthesized for the attitude to track the command attitude. This command attitude is extracted from the command force by using a backstepping idea. In addition, an auxiliary system with appropriate parameters is introduced to preserve thrust saturation constraint, to guarantee nonsingular command attitude extraction and to avoid usage of neighboring acceleration information. With proper choices of Lyapunov functions, explicit selection criteria for the control parameters are formulated to ensure the asymptotic stability of the closed-loop system. Simulations and experiments are provided to validate the proposed theoretical results.
TL;DR: A backstepping approach integrated with time-delay estimation is presented to provide an accurate estimation of unknown dynamics and to compensate for external bounded disturbances to perform passive rehabilitation movements with a 7-DOF exoskeleton robot.
Abstract: In this paper, we present a backstepping approach integrated with time-delay estimation to provide an accurate estimation of unknown dynamics and to compensate for external bounded disturbances. The control was implemented to perform passive rehabilitation movements with a 7-DOF exoskeleton robot named ETS-Motion Assistive Robotic-Exoskeleton for Superior Extremity. The unknown dynamics and external bounded disturbances can affect the robotic system in the form of input saturation, time-delay errors, friction forces, backlash, and different upper-limb's mass of each subject. The output of the time-delay estimator is coupled directly to the control input of the proposed adaptive tracking control through a feed-forward loop. In this case, the control system ensures a highly accurate tracking of the desired trajectory, while being robust to the uncertainties and unforeseen external forces, and flexible with variation of parameters. Due to the proposed strategy, the designed control approach does not require accurate knowledge of the dynamic parameters of the exoskeleton robot to achieve the desired performance. The stability of the exoskeleton robot and the convergence of its state errors are established and proved based on Lyapunov–Krasovskii functional theory. Experimental results and a comparative study are presented to validate the advantages of the proposed strategy.