Showing papers in "IEEE-ASME Transactions on Mechatronics in 2020"
TL;DR: In this article, a boundary control approach is used to control a two-link rigid-flexible wing, which is based on the principle of bionics to improve the mobility and the flexibility of aircraft.
Abstract: A boundary control approach is used to control a two-link rigid-flexible wing in this article. Its design is based on the principle of bionics to improve the mobility and the flexibility of aircraft. First, a series of partial differential equations (PDEs) and ordinary differential equations (ODEs) are derived through the Hamilton's principle. These PDEs and ODEs describe the governing equations and the boundary conditions of the system, respectively. Then, a control strategy is developed to achieve the objectives including restraining the vibrations in bending and twisting deflections of the flexible link of the wing and achieving the desired angular position of the wing. By using Lyapunov's direct method, the wing system is proven to be stable. The numerical simulations are carried out with the finite difference method to prove the effectiveness of designed boundary controllers.
245 citations
TL;DR: The proposed ESO-based adaptive controller theoretically achieves an excellent asymptotic tracking performance when time-invariant modeling uncertainties exist and preserves the performance results of both control methods while overcoming their practical performance limitations.
Abstract: Velocity signal is difficult to obtain in practical electrohydraulic servomechanisms Even though it can be approximately derived via numerical differentiation on position measurement, the strong noise effect will greatly deteriorate the achievable control performance Hence, how to design a high-performance tracking controller without velocity measurement is of practical significance In this paper, a practical adaptive tracking controller without velocity measurement is proposed for electrohydraulic servomechanisms To estimate the unmeasurable velocity signal, an extended state observer (ESO) that also provides an estimate of the mismatched disturbance is constructed The ESO uses the unknown parameter estimates updated by a novel adaptive law, which only depends on the actual position and desired trajectory Moreover, the matched parametric uncertainty is also handled by online parameter adaptation and the matched disturbance is suppressed via a robust control law The proposed ESO-based adaptive controller theoretically achieves an excellent asymptotic tracking performance when time-invariant modeling uncertainties exist In the presence of time-variant modeling uncertainties, guaranteed transient performance and prescribed final tracking accuracy can also be achieved The proposed control strategy bridges the gap between the adaptive control and disturbance observer-based control without using the velocity signal and preserves the performance results of both control methods while overcoming their practical performance limitations Comparative experiments are performed on an actual servovalve-controlled double-rod hydraulic actuator to verify the superiority of the proposed control strategy
162 citations
TL;DR: An radial basis function (RBF) neural network based adaptive robust control design is proposed for nonlinear bilateral teleoperation manipulators to cope with the main issues including the communication time delay, various nonlinearities, and uncertainties.
Abstract: The bilateral teleoperation system has raised expansive concern as its excellent behaviors in executing the tasks in the remote, unstructured, and dangerous areas via the cooperative operation systems. In this article, an radial basis function (RBF) neural network based adaptive robust control design is proposed for nonlinear bilateral teleoperation manipulators to cope with the main issues including the communication time delay, various nonlinearities, and uncertainties. Specifically, the slave environmental dynamics is modeled by a general RBF neural network, and its parameters are estimated and then transmitted for the environmental torque reconstruction in the master side. Since the parameters of the neural network (which are nonpower signals) are transmitted instead of the traditional environmental torque in the communication channel, the previous existing passivity problem under time delay is avoided. In both of master and slave sides, the trajectory creators are applied to generate the desired trajectories, and the RBF-neural-network-based adaptive robust controllers are designed subsequently to handle the nonlinearities and uncertainties. Theoretically, the proposed control algorithm can guarantee the global stability of bilateral teleoperation manipulators under time delay, and the good transparency performance with both position tracking and force feedback is also achieved simultaneously. The real platform comparative experiments are carried out, and the results show the good position tracking to execute precise operation and the good force feedback to detect the sudden disturbance in the environment dynamics.
132 citations
TL;DR: A novel dual Gaussian process regression model is designed to predict SOH over the entire cycle life and RUL near the end of life of battery packs, showing the prospect of health prognosis using multiple health indicators in automotive applications.
Abstract: Accurate, reliable, and robust prognosis of the state of health (SOH) and remaining useful life (RUL) plays a significant role in battery pack management for electric vehicles. However, there still exist challenges in computational cost, storage requirement, health indicators extraction, and algorithm design. This paper proposes a novel dual Gaussian process regression model for the SOH and RUL prognosis of battery packs. The multi-stage constant current charging method is used for aging tests. Health indicators are extracted from partial charging curves, in which capacity loss, resistance increase, and inconsistency variation are examined. A dual Gaussian process regression model is designed to predict SOH over the entire cycle life and RUL near the end of life. Experimental results show that the predictions of SOH and RUL are accurate, reliable, and robust. The maximum absolute errors and root mean square errors of SOH predictions are less than 1.3% and 0.5%, respectively, and the maximum absolute errors and root mean square errors of RUL predictions are 2 cycles and 1 cycle, respectively. The computation time for the entire training and testing process is less than 5 seconds. This article shows the prospect of health prognosis using multiple health indicators in automotive applications.
106 citations
TL;DR: A novel reaching law is designed based on hyperbolic functions to guarantee that the sliding mode variable infinitely approaches to the equilibrium point instead of crossing it, so that the fast convergence and chattering-free property can be achieved simultaneously.
Abstract: This article proposes an unknown system dynamics estimator (USDE) based sliding mode control for servo mechanisms with unknown dynamics and modeling uncertainties. An invariant manifold is first constructed by introducing an auxiliary variable based on a first-order low-pass filter. This is used to design a USDE with only one tuning parameter (i.e., time constant for the filter) and a simpler structure than other estimators. The USDE is used to compensate for the effect of the lumped unknown system dynamics since it can be easily incorporated into control synthesis. Moreover, to avoid the chattering phenomenon in the conventional sliding mode control methods, a novel reaching law is designed based on hyperbolic functions to guarantee that the sliding mode variable infinitely approaches to the equilibrium point instead of crossing it. Consequently, the fast convergence and chattering-free property can be achieved simultaneously. Simulations and experiments are provided to validate the effectiveness and superior performance of the proposed method.
104 citations
TL;DR: By visualizing input errors and extrinsic disturbances as an unknown “disturbance-like” term, a new robust adaptive vibration control technology and online updating laws can be constructed for riser systems to guarantee the oscillation reduction and compensation of uncertainties and dead zone.
Abstract: This article provides a framework of dead zone compensation and robust adaptive vibration control for uncertain spatial flexible riser systems. First, nonsymmetric dead zone nonlinearity is represented in the form of the desired control input with the addition of an extra nonlinear input error. Second, by visualizing those input errors and extrinsic disturbances as an unknown “disturbance-like” term, a new robust adaptive vibration control technology and online updating laws can be constructed for riser systems to guarantee the oscillation reduction and compensation of uncertainties and dead zone. Third, the constructed control ensures and achieves bounded Lyapunov stability in the controlled system. Ultimately, control performances are demonstrated with appropriate design parameters.
102 citations
TL;DR: In this article, a data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNNs) in predicting the remaining useful life (RUL) for rolling element bearings (REBs).
Abstract: In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs is of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct the health index. In this article, a novel data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNNs) in predicting the RULs of bearings. More concretely, raw vibrations of training bearings are first processed using the Hilbert–Huang transform to construct a novel nonlinear degradation energy indicator which can be used as the training label. The CNN is then employed to identify the hidden pattern between the extracted degradation energy indicator and the raw vibrations of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings’ RULs are predicted through using an $\epsilon$ -support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by performance test on other bearings undergoing different operating conditions.
94 citations
TL;DR: This article studies the robust trajectory tracking control problem of a quadrotor unmanned aerial vehicle (UAV) and proposes a novel backstepping sliding-mode control scheme for position subsystem that is robust to the external disturbances and model uncertainties.
Abstract: This article studies the robust trajectory tracking control problem of a quadrotor unmanned aerial vehicle (UAV). In order to guarantee the desired trajectory tracking performance in the presence of external disturbances and model uncertainties, the design process of the quadrotor UAV controller is divided into two steps. First, by decomposing the attitude dynamic system into two serial-connected subsystems, a cascade active disturbance rejection control scheme is applied to the attitude subsystem. Second, by introducing an additional high-gain design parameter, a novel backstepping sliding-mode control scheme for position subsystem is constructed. Moreover, the Lyapunov stability analysis is provided to show that the trajectory tracking error can converge to an arbitrarily small residual set. Numerical results illustrate the effectiveness of the designed control method and its robustness to the external disturbances and model uncertainties. Finally, the proposed method is implemented on a quadrotor UAV to demonstrate its feasibility in practical application.
85 citations
TL;DR: This article solves the safe trajectory tracking control problem of robot manipulators with actuator faults, uncertain dynamics, and external disturbance by designing an adaptive control law designed by using the joint position, the estimated velocity, and the reconstructed knowledge of velocity measurement uncertainty.
Abstract: This article solves the safe trajectory tracking control problem of robot manipulators with actuator faults, uncertain dynamics, and external disturbance. Another key issue met in practical robot engineering, i.e., joint velocity measurement uncertainty is also investigated. A robust control framework is presented. In this approach, a novel reconstruction law is preliminarily developed to estimate velocity measurement uncertainty, while the estimation error is finite-time stable. An adaptive control law is, then, designed by using the joint position, the estimated velocity, and the reconstructed knowledge of velocity measurement uncertainty. The key advantage of this methodology is that actual joint velocity and any prior knowledge of actuator faults are not required. The effectiveness of this proposed scheme is experimentally validated on a real robot arm.
79 citations
TL;DR: Comparisons among proportional–integral–differential, iterative learning control, and the proposed NNC controller consistently validate that NNC can basically achieve excellent contouring motion performance as ILC, significantly without need of motion repetition and iteration.
Abstract: This article proposes a gated recurrent unit (GRU) neural network prediction and compensation (NNC) strategy for precision multiaxis motion control systems with contouring performance orientation. First, some characteristic contouring tasks are carried out on a multiaxis linear-motor-driven motion system, and the true contouring error values obtained by the Newton numerical calculation method are used as the training data of a developed artificial GRU neural network. Essentially, the proposed GRU neural network structure can be viewed as a data-based black-box error model, which can capture the dynamic characteristics of contouring motion rather accurately. The well-trained GRU network can predict the contouring error precisely even under the tasks those have not been conducted during the training session. Moreover, the predicted contouring error is compensated into the reference contour as feedforward compensation to improve the final contouring performance. Comparison between the predicted contouring error and the actual contouring error practically proves the effective prediction ability of the proposed GRU neural network. Furthermore, comparative experiments among proportional–integral–differential, iterative learning control (ILC), and the proposed NNC controller are conducted. The results consistently validate that NNC can basically achieve excellent contouring motion performance as ILC, significantly without need of motion repetition and iteration. Due to the implementation convenience and excellent prediction/compensation ability, the proposed NNC would have good potential in industrial mechatronic applications.
70 citations
TL;DR: A novel capture method that uses a series of hollow-shaped end-effector pairs to cage the antipodal pairs of the nongraspable objects and a caging compatibility index is proposed to describe the capturing ability in this manner.
Abstract: Capture and removal of space debris are challenging in robotic on-orbit servicing activities. A large portion of space debris does not possess any graspable features, which makes the conventional grippers inapplicable. To handle such nongraspable objects, a space robotic capture system is presented. A dual-arm space robot simulator that has the advantages of miniaturization and scalability is designed for ground tests. Inspired by the robotic caging, we propose a novel capture method that uses a series of hollow-shaped end-effector pairs to cage the antipodal pairs of the nongraspable objects. To apply the caging-pair method steadily, space robots need exerting a squeezing action on objects, which can be characterized by the motion and force manipulation of two robotic arms in the assigned directions. Based on the velocity and force manipulability transmission ratios, a caging compatibility index is proposed to describe the capturing ability in this manner. Via the optimization of the desired caging compatibility index, an effective algorithm is proposed to plan the near-optimal joint configurations for the pregrasping cages. Finally, both simulation studies and experimental tests are conducted to evaluate the performance of the proposed capture method.
TL;DR: This article presents an active simultaneous localization and mapping (SLAM) framework for a mobile robot to obtain a collision-free trajectory with good performance in SLAM uncertainty reduction and in an area coverage task, based on a model predictive control framework.
Abstract: In this article, we present an active simultaneous localization and mapping (SLAM) framework for a mobile robot to obtain a collision-free trajectory with good performance in SLAM uncertainty reduction and in an area coverage task. Based on a model predictive control framework, these two tasks are solved by the introduction of a control switching mechanism. For SLAM uncertainty reduction, graph topology is used to approximate the original problem as a constrained nonlinear least squares problem. A convex half-space representation is applied to relax nonconvex spatial constraints that represent obstacle avoidance. Using convex relaxation, the problem is solved by a convex optimization method and a rounding procedure based on singular value decomposition. The area coverage task is addressed with a sequential quadratic programming method. A submap joining approach, called linear SLAM, is used to address the associated challenges of avoiding local minima, minimizing control switching, and potentially high computational cost. Finally, various simulations and experiments using an aerial robot are presented that verify the effectiveness of the proposed method, showing that our method produces a more accurate SLAM result and is more computationally efficient compared with multiple existing methods.
TL;DR: Compared with other fault diagnosis methods, the proposed MDELM algorithm has better learning efficiency, and it is more suitable for intelligent diagnosis of multichannel data fusion.
Abstract: Nowadays, the measurement technology of multichannel information fusion provides a solid research foundation for digital and intelligent fault diagnosis of mechatronics equipment To implement the rapid fusion of multichannel data and intelligent diagnosis, a new fault diagnosis method for multichannel motor–rotor system via multimanifold deep extreme learning machine (MDELM) algorithm is first proposed in this article Specifically, the designed MDELM algorithm is divided into two main components: 1) unsupervised self-taught feature extraction via the designed extreme learning machine based-modified sparse filtering feature extractor; 2) semisupervised fault classification via the designed MELM classifier with multimanifold constraints to mine the intraclass and interclass discriminant feature information Experimental and industrial data from motor–rotor system demonstrates the superiority of the proposed method and algorithms Compared with other fault diagnosis methods, the proposed MDELM algorithm has better learning efficiency, and it is more suitable for intelligent diagnosis of multichannel data fusion
TL;DR: The key technologies of IAI are summarized and their typical application scenarios with respect to three major aspects of production monitoring: fault diagnosis, remaining useful life prediction, and quality inspection are discussed.
Abstract: Over the past few decades, intelligentization, supported by artificial intelligence (AI) technologies, has become an important trend for industrial manufacturing, accelerating the development of smart manufacturing. In modern industries, standard AI has been endowed with additional attributes, yielding the so-called industrial artificial intelligence (IAI) that has become the technical core of smart manufacturing. AI-powered manufacturing brings remarkable improvements in many aspects of closed-loop production chains from manufacturing processes to end product logistics. In particular, IAI incorporating domain knowledge has benefited the area of production monitoring considerably. Advanced AI methods such as deep neural networks, adversarial training, and transfer learning have been widely used to support both diagnostics and predictive maintenance of the entire production process. It is generally believed that IAI is the critical technologies needed to drive the future evolution of industrial manufacturing. This article offers a comprehensive overview of AI-powered manufacturing and its applications in monitoring. More specifically, it summarizes the key technologies of IAI and discusses their typical application scenarios with respect to three major aspects of production monitoring: fault diagnosis, remaining useful life prediction, and quality inspection. In addition, the existing problems and future research directions of IAI are also discussed. This article further introduces the papers in this focused section on AI-based monitoring in smart manufacturing by weaving them into the overview, highlighting how they contribute to and extend the body of literature in this area.
TL;DR: A duplet classifier is developed by combining two 1-D convolutional neural networks that are responsible for the diagnosis of the rotor and bearing faults, respectively and can reliably identify the onset and nature of mixed faults.
Abstract: Fault diagnosis for rolling elements in rotating machinery persistently receives high research interest due to the said machinery's prevalence in a broad range of applications. State-of-the-art methods in such setups focus on effective identification of faults that usually involve a single component while rejecting noise from limited sources. This article studies the data-based diagnosis of mixed faults coming from multiple components with an emphasis on model robustness against a wide spectrum of external perturbation. A dataset is collected on a rotor and bearing system by varying the levels and types of faults in both the rotor and bearing, which results in 48 machine health conditions. A duplet classifier is developed by combining two 1-D convolutional neural networks (CNNs) that are responsible for the diagnosis of the rotor and bearing faults, respectively. Experimental results show that the proposed classifier can reliably identify the onset and nature of mixed faults. In addition, one-vs-all classifiers are built using the features generated by the developed 1-D CNNs as predictors to recognize previously unlearned fault types. The effectiveness of such classifiers is demonstrated using data collected from four new fault types. Finally, the robustness and ability to reject external perturbation of the duplet classification model are analyzed using kernel density estimation. The code for the proposed classifiers is available at https://github.com/siyuanc2/machine-fault-diag .
TL;DR: An integrated prognosis method based on signal processing and an adaptive Bayesian algorithm is proposed to predict the RUL of various faulty bearings in wind turbines to improve accuracy.
Abstract: In North America, many utility-scale turbines are approaching the half-way point of their anticipated initial lifespan. Accurate remaining useful life (RUL) estimation can provide wind farm owners insight into how to optimize the life and value of their farm assets. An improved understanding of the RUL of turbine components is particularly essential as many owners consider retiring, life-extending, or repowering their farms. In this article, an integrated prognosis method based on signal processing and an adaptive Bayesian algorithm is proposed to predict the RUL of various faulty bearings in wind turbines. The signal processing leverages feature extraction, feature selection, and signal denoising to detect the dynamics of various failures. Then, RUL of the faulty bearings is forecast via the adaptive Bayesian algorithm using the extracted features. Finally, a new fusion method based on an ordered weighted averaging (OWA) operator is applied to the RUL obtained from the features to improve accuracy. The efficacy of the method is evaluated using data from historical failures across three different Canadian wind farms. Experimental test results indicate that the OWA operator delivers a higher accuracy for RUL prediction compared to the other feature-based methods and Choquet integral fusion technique.
TL;DR: A flexible three-prismatic-universal parallel mechanism employs superelastic nickel–titanium rods to achieve compliant movements beyond the conventional rigid-body parallel mechanisms and a stiffness model is derived to evaluate the stiffness of the manipulator quantitatively.
Abstract: To possess sufficient compliance while keeping an acceptable stiffness level for manipulation with precision, transoral robotic surgery (TORS) demands a flexible robotic system with variable stiffness (VS) as presented in this paper. In our study, a flexible three-prismatic-universal parallel mechanism employs superelastic nickel–titanium rods to achieve compliant movements beyond the conventional rigid-body parallel mechanisms. With a compact structure and flexible-shaft transmission, the adjustable tube-based VS mechanism allows the stiffness of the manipulator to be continuously tuned in real time according to the surgical requirements. A stiffness model is derived to evaluate the stiffness of the manipulator quantitatively. A parallel mechanism with three prismatic-revolute-spherical chains is adopted as the master device, to improve the maneuverability and decrease the learning curve for less experienced surgeons. The TORS manipulators are characterized and verified in the laboratory and cadaveric trials, showing the VS and the execution of the master–slave teleoperated configuration. Furthermore, the cadaveric trials attested the effectiveness of the VS mechanism and the preclinical feasibility of the robotic system.
TL;DR: The design and human–robot interaction modeling of a portable hip exoskeleton based on a custom quasi-direct drive actuation with performance improvement compared with state-of-the-art exoskeletons is described and demonstrated.
Abstract: High-performance actuators are crucial to enable mechanical versatility of wearable robots, which are required to be lightweight, highly backdrivable, and with high bandwidth. State-of-the-art actuators, e.g., series elastic actuators, have to compromise bandwidth to improve compliance (i.e., backdrivability). In this article, we describe the design and human–robot interaction modeling of a portable hip exoskeleton based on our custom quasi-direct drive actuation (i.e., a high torque density motor with low ratio gear). We also present a model-based performance benchmark comparison of representative actuators in terms of torque capability, control bandwidth, backdrivability, and force tracking accuracy. This article aims to corroborate the underlying philosophy of “design for control,” namely meticulous robot design can simplify control algorithms while ensuring high performance. Following this idea, we create a lightweight bilateral hip exoskeleton to reduce joint loadings during normal activities, including walking and squatting. Experiments indicate that the exoskeleton is able to produce high nominal torque (17.5 Nm), high backdrivability (0.4 Nm backdrive torque), high bandwidth (62.4 Hz), and high control accuracy (1.09 Nm root mean square tracking error, 5.4% of the desired peak torque). Its controller is versatile to assist walking at different speeds and squatting. This article demonstrates performance improvement compared with state-of-the-art exoskeletons.
TL;DR: This article presents the design, analysis, and prototype test of a novel spatial deployable three-degree of freedom (DOF) compliant nano-positioner with a three-stage motion amplification mechanism (MAM).
Abstract: This article presents the design, analysis, and prototype test of a novel spatial deployable three-degree of freedom (DOF) compliant nano-positioner with a three-stage motion amplification mechanism (MAM). Inspired by deployable structures, a new design concept, namely monolithically spatial compliant mechanism (MSCM) is proposed to minimize the overall structure. Based on MSCM, a folding operation is employed uniquely by arranging three typical kinds of basic MAM modules with two sets of hooke joints. Due to the spatial structure, the dimensions in horizontal plane is reduced by 60.94%. Furthermore, the proposed nano-positioner demonstrates the simultaneous design of large-ratio amplification mechanism, compact, highly flexible, and assembly-free spatial XY platform with integrated Z platform. Analytical modeling is carried out, and finite element analysis is conducted to optimize the geometric parameters. A prototype is fabricated to verify the performances of the nano-positioner through tests. Experimental results demonstrate that the maximum displacements in $x$ -, $y$ -, and $z$ -axes can reach 177.33, 179.30, and 17.45 $\mu$ m, respectively. The motion amplification ratios in the $x$ - and $y$ -axes can reach 10.19 and 10.30, respectively. Moreover, by adopting proportional-integral-derivative feedback controller, the closed-loop control experiments are conducted. The results show that the motion resolution in three axes can all reach 5 nm. As the MSCM has been verified to be feasible and favorable, it can be anticipated that the design concept will contribute to the multiformity and development of compliant mechanisms.
TL;DR: A new algorithm, an extended complementary filter (ECF), to derive 3-D rigid body orientation from inertial sensing suites addressing these challenges, and combines computational efficiency of classic complementary filters with improved accuracy compared to popular optimization filters.
Abstract: Inertial sensing suites now permeate all forms of smart automation, yet a plateau exists in the real-world derivation of global orientation. Magnetic field fluctuations and inefficient sensor fusion still inhibit deployment. In this article, we introduce a new algorithm, an extended complementary filter (ECF), to derive 3-D rigid body orientation from inertial sensing suites addressing these challenges. The ECF combines computational efficiency of classic complementary filters with improved accuracy compared to popular optimization filters. We present a complete formulation of the algorithm, including an extension to address the challenge of orientation accuracy in the presence of fluctuating magnetic fields. Performance is tested under a variety of conditions and benchmarked against the commonly used gradient decent inertial sensor fusion algorithm. Results demonstrate improved efficiency, with the ECF achieving convergence 30% faster than standard alternatives. We further demonstrate an improved robustness to sources of magnetic interference in pitch and roll and to fast changes of orientation in the yaw direction. The ECF has been implemented at the core of a wearable rehabilitation system tracking movement of stroke patients for home telehealth. The ECF and accompanying magnetic disturbance rejection algorithm enables previously unachievable real-time patient movement feedback in the form of a full virtual human (avatar), even in the presence of magnetic disturbance. Algorithm efficiency and accuracy have also spawned an entire commercial product line released by the company x-io. We believe the ECF and accompanying magnetic disturbance routines are key enablers for future widespread use of wearable systems with the capacity for global orientation tracking.
TL;DR: A novel control scheme utilizing fractional-order terminal sliding mode control with sliding perturbation observer (SPO) with high precision, fast convergence, and robust performance against the lumped uncertainties was used to solve the trajectory tracking problem on a seven-degree-of-freedom robot manipulator.
Abstract: A novel control scheme utilizing fractional-order terminal sliding mode control with sliding perturbation observer (FOTSMCSPO) is proposed herein. It was used to solve the trajectory tracking problem on a seven-degree-of-freedom robot manipulator. The fractional-order terminal sliding mode in FOTSMCSPO provides a finite convergence time for reaching the sliding surface. In this article, a sliding perturbation observer (SPO) is used to estimate the disturbance from the environment and modeling uncertainties. The new control scheme exhibits high precision, fast convergence, and robust performance against the lumped uncertainties. These advantages are ensured through the newly designed fractional-order terminal sliding surface and the SPO. The stability is analyzed based on the Lyapunov functions for general and fractional-order systems. An implementation on a real robot manipulator demonstrated the effects and performance of the new control method.
TL;DR: An advanced position controller incorporated with effective disturbance observers (DOs) applied for a pump-controlled hydraulic system is proposed in this article and uncertainties are considered as certainties (nominal terms) and their deviations.
Abstract: Researches for improving the control performance of hydraulic systems in both control accuracy and energy efficiency have never stopped in aerospace and industrial applications. The existence of nonlinearities, uncertainties, and unknown terms in the system dynamics, however, significantly limits the desired performance. To realize improvements by dealing with these problems, an advanced position controller incorporated with effective disturbance observers (DOs) applied for a pump-controlled hydraulic system is proposed in this article. Here, uncertainties are considered as certainties (nominal terms) and their deviations. To eliminate certain nonlinearities in the system dynamics, the proposed controller is designed based on a simplified robust sliding-mode-backstepping scheme. The lumped unknown terms, which mainly degrade the performance of the controller, in pressure dynamics and force dynamics are expanded by using equivalent nonautonomous models. To effectively approximate the terms and to ensure usability of the estimated results inside the control framework, two different high-order DOs are developed. Asymptotic convergences of these observers are achieved by adopting nonlinear combinations of the estimation errors. Effectiveness and feasibility of the designed observers and the closed-loop system for an asymptotically tracking performance in the presence of bounded time-varying disturbances are then confirmed by Lyapunov-based proofs and extensive experiments.
TL;DR: The key innovation of the proposed method is the design of the nonlinear gain to suppress the position tracking error according to the disturbance estimation error, so the use of the high observer gain to accurately estimate the disturbance can be avoided.
Abstract: In this article, a method for nonlinear control is proposed using an extended state observer for position tracking of an electro-hydraulic system (EHS) with only position feedback. The proposed method consists of a high-gain extended state observer (HGESO) and a nonlinear controller. The EHS model is transformed into the normal form to lump a system function and an external disturbance into a disturbance. The HGESO observer is accordingly designed to estimate the full state and disturbance. The nonlinear controller is then developed using backstepping to suppress the position tracking error using the input-to-state stability property. The key innovation of the proposed method is the design of the nonlinear gain to suppress the position tracking error according to the disturbance estimation error. Thus, the use of the high observer gain to accurately estimate the disturbance can be avoided. The stability of the closed loop including the EHS, controller, and observer is then mathematically demonstrated without the need for an approximation of the “sgn” function in the EHS dynamics. The proposed method is verified using simulated and experimental testing.
TL;DR: A hierarchical control system was proposed to recognize locomotion modes in real time for a unilateral knee exoskeleton on different terrains with specific assistive control strategies using support vector machine classifier to recognize the locomotion mode based on two inertia measurement units.
Abstract: Stroke may lead to considerable physical impairment and functional disability, which affects walking ability. As a potential way to assist gaits, lower limb exoskeletons have been developed. To supply appropriate assistive torque, real-time accurate recognition of current gait mode is important. In this article, a hierarchical control system was proposed to recognize locomotion modes in real time for a unilateral knee exoskeleton on different terrains with specific assistive control strategies. Support vector machine classifier was used to recognize the locomotion mode based on two inertia measurement units. The corresponding assistive control strategy was designed according to the recognition result. Real-time recognition experiments under assistive torque control were conducted on five able-bodied subjects and one stroke patient, respectively. For the able-bodied subjects: first, no significance was found on the total recognition accuracies whichever the leading leg was for the five subjects ( p = 0.057 ), which indicated the proposed method in this article was suitable whichever the leading leg was as far as the overall classification accuracy was concerned. Second, transitions occurred in swing phase when the leading leg was the paretic leg and transitions occurred in stance phase when the leading leg was the sound leg. No significance was found on mean delay time for the five subjects ( p = 0.785 ) whichever the leading leg was, which indicated that the proposed method in this article was suitable for these two leading legs as far as the mean delay time were concerned. Third, the method of generating the assistance based on the previous gait cycle time was demonstrated to be reasonable and the tracking performance of the torque could meet the requirement. For the stroke patient, similar experimental results were obtained.
TL;DR: This article presents an innovative, motor-driven, three-finger compliant gripper for adaptive grasping of size-varied delicate objects and prototyped by three-dimensional printing using thermoplastic elastomer, which agrees well with simulation results.
Abstract: This article presents an innovative, motor-driven, three-finger compliant gripper for adaptive grasping of size-varied delicate objects. An optimized compliant finger design is identified numerically through a topology optimization method. A stepper motor is used to actuate three identical compliant fingers, which can operate through elastic bending deformation. Finite-element models are developed to investigate the maximum equivalent stress, input force, and output displacement relations corresponding to the amount of input displacement of the compliant finger. Simulation results show that the proposed finger design is with a lower driving force and a lower maximum equivalent stress during operation comparing to one previous design. The proposed compliant finger is prototyped by three-dimensional printing using thermoplastic elastomer. Experimental results for the input displacement versus input force and input displacement versus geometric advantage relationships of the prototyped finger agree well with simulation results. The developed three-finger soft robotic gripper is mounted on an industrial robot arm to demonstrate its capability in handing size-varied delicate objects, such as egg, fruits, and glass products. Experimental results show that the proposed three-finger gripper can be used to grip object with a maximum weight of 4.2 kg and a maximum object size of 140 mm. The overall weight of the developed three-finger soft robotic gripper is 1.2 kg. The load capacity of the developed gripper can vary according to the friction between gripper and object. The maximum payload of the gripper can be increased to 9.5 kg when an additional antislip foam tape is applied on the grip surfaces of the compliant fingers.
TL;DR: The fusion of the UDE technique with the unit quaternion is fused to achieve the robust global full degrees of freedom trajectory tracking control with experimental demonstrations to demonstrate the global singularity-free tracking property and the superior robustness of the proposed controller.
Abstract: This article presents an uncertainty and disturbance estimator (UDE) based global trajectory tracking control strategy for a quadrotor. The main contribution of this article is the fusion of the UDE technique with the unit quaternion to achieve the robust global full degrees of freedom trajectory tracking control with experimental demonstrations. The novelty of this article lies in the development of the UDE-based global tracking technique to the overall system (attitude and position) with a quaternion-based nonlinear reference model. The UDE-based attitude and position controllers are derived from the unit quaternion-based quadrotor dynamics with a cascade control structure to deal with underactuation, model uncertainties, and external disturbances. The attitude controllers that are developed with the backstepping techniques avoid the rotation matrix calculation and the unwinding problem. The position controllers are derived using the thrust-vectoring approach. A nonlinear unit quaternion-based reference model is developed to achieve the time-scale separation for the cascade control loops. The stability analysis of the closed-loop system is conducted with the Lyapunov method. Extensive flight experiments are conducted to demonstrate the global singularity-free tracking property and the superior robustness of the proposed controller.
TL;DR: This article presents a design of an electrically powered waist-assistive exoskeleton H-WEXv2 to reduce back-muscle fatigue and prevent back-injury of industrial workers to achieve system performances in aspect of cost, weight, operational time, and system endurance and maintainability for industrial feasibility.
Abstract: This article presents a design of an electrically powered waist-assistive exoskeleton H-WEXv2 to reduce back-muscle fatigue and prevent back-injury of industrial workers. It utilizes a wire-driven mechanism based on a singular series elastic actuation to achieve system performances in aspect of cost, weight, operational time, and system endurance and maintainability for industrial feasibility. This proposed mechanism consisting of a ball screw drive and a series elastic actuator enables H-WEXv2 to accomplish better control maneuverability on powered flexion/extension while maintaining the advantage of allowing natural human walking with almost zero impedance. Also, to transfer torque determined by a high-level controller, a SEA force controller was implemented by measuring elastic displacements of installed spring elements at both hip joints. In order to evaluate the effectiveness of the developed robot installed with the proposed mechanism, electromyographic (EMG) signals of relevant muscles of ten subjects related to target waist motions were measured and compared for three cases: 1) wearing H-WEXv1, 2) wearing H-WEXv2, and 3) wearing nothing. Finally, the statistical analysis on acquired EMG signals verified the effectiveness of waist assistance provided by H-WEXv2.
TL;DR: The effectiveness of the proposed synergistic technique is examined experimentally, with results demonstrating advantages over conventional methods in terms of accurately differentiating between a healthy and faulty motor, as well as estimating the fault severity, even under zero-load IM conditions.
Abstract: Reliable induction motor (IM) fault detection techniques are very useful in industries to diagnose IM defects and improve operational performance. A smart sensor-based technology is proposed in this article to synergistically use vibration and current harmonics for rotor bar fault detection in IMs. The vibration signal is used for analysis of shaft speed variations and the current harmonics information is applied for rotor bar fault detection. A wireless smart sensor network is developed and used for data collection, allowing for low-cost, low space footprint, and noninvasive installation. The effectiveness of the proposed synergistic technique is examined experimentally, with results demonstrating advantages over conventional methods in terms of accurately differentiating between a healthy and faulty motor, as well as estimating the fault severity, even under zero-load IM conditions. A means to quantify the fault states as diagnostic indices is also proposed for online IM health condition monitoring.
TL;DR: In this article, a force sensor made up of a 1 mm fiber Bragg grating attached to a 3 mm long nitinol tube was developed to measure the compression force exerted on the tendon sheath.
Abstract: This article presents a novel force sensor to detect the distal force of tendon-sheath mechanisms (TSMs) in flexible endoscopic surgical robots. We propose to measure the compression force on the sheath at the distal end so that the tension force on the tendon, which equals the compression force on the sheath, can be obtained. With this approach, a new force sensor made up of a 1 mm fiber Bragg grating attached to a 3 mm long nitinol tube was developed to measure the compression force exerted on the sheath. Mechanics analysis and verification tests were conducted to characterize the relationship between tension and compression on a TSM. Force calibrations, hysteresis study, and temperature compensation verification tests on the sensor were carried out. The force sensor has a measurement error of 0.178 N and a sensitivity of 34.14 pm/N. Applications of the sensor in a TSM-driven robotic grasper and a tendon-driven continuum robot were demonstrated. This force sensor has salient advantages: it is small, structurally simple, electrically passive, temperature-compensated, easy to assemble and disassemble, flexible, and biocompatible. This proposed approach with the new force sensor can also be applied to both TSM-driven systems and tendon-driven systems such as robotic fingers/hands, wearable devices, surgical catheters, and rehabilitation devices.
TL;DR: A novel health evaluation method based on stacking ensemble learning and generalized multiclass support vector machine (GMSVM) algorithm, which achieves high efficiency and, meanwhile, low variance and deviation.
Abstract: Accurate health evaluation is crucial to reliable operation of complex degradation systems. Although traditional machine learning methods such as artificial neural network (ANN) and support vector machine (SVM) have been used widely, state assessment schemes based on a single classification model still suffer from low multiclass classification efficiency, high variance, and deviation. To solve these problems, this article proposes a novel health evaluation method based on stacking ensemble learning and generalized multiclass support vector machine (GMSVM) algorithm. The proposed health evaluation framework includes three parts: 1) abnormal value elimination and missing value processing are applied for multiple sensor data; 2) statistical features are extracted from the observed data and the Pearson correlation coefficient is applied for feature selection; and 3) ensemble generalized multiclass support vector machines (EGMSVMs) are utilized to evaluate the health situation of a degradation system. Unlike the binary classifiers and deep-learning-based classifiers, EGMSVMs utilize the stacking-based method to combine several GMSVMs as submodels and random forest as a metamodel, and the metamodel ensembles the results of submodels to reach a satisfied performance. Compared to traditional SVM- and ANN-based algorithms, EGMSVMs, in processing multiclass problems, achieve high efficiency and, meanwhile, low variance and deviation. The proposed method is verified using a hydraulic test rig. The experimental results show the feasibility and applicability of the proposed health evaluation framework.