Showing papers in "IEEE-ASME Transactions on Mechatronics in 2016"

Variable Stiffness Actuators: Review on Design and Components

Abstract: Variable stiffness actuators (VSAs) are complex mechatronic devices that are developed to build passively compliant, robust, and dexterous robots. Numerous different hardware designs have been developed in the past two decades to address various demands on their functionality. This review paper gives a guide to the design process from the analysis of the desired tasks identifying the relevant attributes and their influence on the selection of different components such as motors, sensors, and springs. The influence on the performance of different principles to generate the passive compliance and the variation of the stiffness are investigated. Furthermore, the design contradictions during the engineering process are explained in order to find the best suiting solution for the given purpose. With this in mind, the topics of output power, potential energy capacity, stiffness range, efficiency, and accuracy are discussed. Finally, the dependencies of control, models, sensor setup, and sensor quality are addressed.

Real-Time Trajectory Planning for Autonomous Urban Driving: Framework, Algorithms, and Verifications

Abstract: This paper focuses on the real-time trajectory planning problem for autonomous vehicles driving in realistic urban environments. To solve the complex navigation problem, we adopt a hierarchical motion planning framework. First, a rough reference path is extracted from the digital map using commands from the high-level behavioral planner. The conjugate gradient nonlinear optimization algorithm and the cubic B-spline curve are employed to smoothen and interpolate the reference path sequentially. To follow the refined reference path as well as handle both static and moving objects, the trajectory planning task is decoupled into lateral and longitudinal planning problems within the curvilinear coordinate framework. A rich set of kinematically feasible path candidates are generated to deal with the dynamic traffic both deliberatively and reactively. In the meanwhile, the velocity profile generation is performed to improve driving safety and comfort. After that, the generated trajectories are carefully evaluated by an objective function, which combines behavioral decisions by reasoning about the traffic situations. The optimal collision-free, smooth, and dynamically feasible trajectory is selected and transformed into commands executed by the low-level lateral and longitudinal controllers. Field experiments have been carried out with our test autonomous vehicle on the realistic inner-city roads. The experimental results demonstrated capabilities and effectiveness of the proposed trajectory planning framework and algorithms to safely handle a variety of typical driving scenarios, such as static and moving objects avoidance, lane keeping, and vehicle following, while respecting the traffic rules.

A Review of Algorithms for Compliant Control of Stiff and Fixed-Compliance Robots

Abstract: This survey presents the state of the art of basic compliant control algorithms in a unified view of past and present literature. Compliant control is fundamental when dealing with unstructured environments, as in the case of human–robot interaction. This is because it implicitly controls the energy transfer to the environment, providing a safe interaction. In this review, we analyze solutions from traditional robotics, usually involving stiff joints, and recent literature to find common control concepts and differences. To this aim, we bring back every schemas and relative mathematics formulation to a common and simplified scenario. Then, for each schema, we explain its intuitive meaning and report issues raised in the literature. We also propose an expansion of taxonomy to account for recent research.

Robust Trajectory Tracking Error Model-Based Predictive Control for Unmanned Ground Vehicles

Abstract: This paper proposes a new robust trajectory tracking error-based control approach for unmanned ground vehicles. A trajectory tracking error-based model is used to design a linear model predictive controller and its control action is combined with feedforward and robust control actions. The experimental results show that the proposed control structure is capable to let a tractor–trailer system track both linear and curvilinear target trajectories with low tracking error.

$\mathcal{H}_{\infty}$ Observer Design for LPV Systems With Uncertain Measurements on Scheduling Variables: Application to an Electric Ground Vehicle

Abstract: In this paper, we aim to study the observer design problem for polytopic linear-parameter-varying (LPV) systems with uncertain measurements on scheduling variables. Due to the uncertain measurements, the uncertainties are considered in the weighting factors. It is assumed that the vertices of polytope are the same when the measurements on scheduling variables are uncertain and perfect. Then, an LPV system with the uncertain weighting factors can be transferred to an LPV system with uncertainties. To deal with the uncertainties and unknown disturbance in the observer design problem, we propose a gain-scheduling sliding mode observer. Defining the estimation error as the state vector minus the estimated state vector, the estimation error dynamics is established. The sliding mode observer design method is developed based on analysis results of the established estimation error system. The proposed observer design method is then applied to an electric ground vehicle (EGV) in which the measurement of longitudinal velocity is assumed to be uncertain. Experimental tests and comparisons are given to show the advantages of the proposed design method and the designed observer.

Approximate Back-Stepping Fault-Tolerant Control of the Flexible Air-Breathing Hypersonic Vehicle

Abstract: This paper deals with fault-tolerant output tracking control for the flexible air-breathing hypersonic vehicle (AHV) subject to parametric uncertainties, external disturbances, and actuator constraints. By regarding the flexible dynamics as equivalent disturbances, the vehicle model can be split into three functional subsystems, namely, horizontal translation subsystem, vertical translation subsystem, and rotation subsystem. Then, for each subsystem, a disturbance observer is utilized to estimate the lumped effect of model uncertainties, external disturbances, and actuator faults, while a novel auxiliary system combined with the command prefilter is constructed to handle the physical constraints on actuators. Furthermore, sliding mode control is employed to design control commands for the three subsystems, sequentially. The proposed controller modifies the reference trajectories dynamically when one or more actuators become constrained, and can steer the AHV to the desired trim finally. Simulation results are provided to demonstrate the effectiveness of the designed controller.

Adaptive Sliding-Mode Observer Design for a Selective Catalytic Reduction System of Ground-Vehicle Diesel Engines

Abstract: In this work, we investigate the adaptive sliding-mode observer design problem for the selective catalytic reduction (SCR) system in diesel-engine aftertreatment systems. First, an uncertain three-state model is obtained. Two kinds of uncertainties are considered: uncertainties with slow variations and uncertainties with fast variations. For the uncertain model, an adaptive sliding-mode observer is proposed. Then, the observer gain tuning method is developed based on the stability analysis of the estimation error system. The proposed observer design method is applied to an SCR system of a medium-duty diesel engine. Experimental results and comparisons are provided to illustrate the advantages of the designed observer according to the proposed algorithm. Compared with the open-loop SCR model and the Luenburger-like observer, the proposed adaptive sliding-mode observer can achieve better performance.

Vibration Control of Flexible Marine Riser Systems With Input Saturation

Abstract: In this paper, boundary control is designed to suppress transverse vibration of a flexible marine riser with input saturation in the ocean environment. Two cases are investigated: 1) state feedback boundary control, and 2) output feedback boundary control. In order to compensate for the input saturation, we propose an auxiliary system. First, state feedback boundary control with an auxiliary system is proposed when the boundary states of the riser can be measured. Subsequently, output feedback boundary control is developed when there are unmeasurable system states. High-gain observers are employed to estimate those unmeasurable states. Based on Lyapunov's direct method, the proposed control ensures that the deflection of the riser is uniformly bounded in the presence of the ocean disturbances. By choosing a set of appropriate parameters, numerical simulations are provided to verify the effectiveness of the proposed boundary control.

Impact Dynamic Modeling and Adaptive Target Capturing Control for Tethered Space Robots With Uncertainties

Abstract: Target capturing is an essential and key mission for tethered space robot (TSR) in future on-orbit servicing, and it is quite meaningful to investigate the stabilization method for TSR during capture impact with target. In this paper, the stabilization control of TSR during target capturing is studied. The space tether is described by the lumped mass model, and the impact dynamic model for target capturing is derived using the Lagrange method with the consideration of space tether length, in/out-plane angles, and gripper attitude. Given the structure of the TSR's gripper, a position-based impedance control method is proposed for target capturing operation. The neural network is used to estimate and compensate the uncertainties in the dynamic model of TSR, and an adaptive robust controller is designed to overcome the influence of the space tether and track the desired position generated by impedance controller. Numerical simulations suggest that the proposed controller can realize the stabilization of TSR during target capturing; besides, the uncertainties of the TSR can effectively be compensated via adaptive law and the influence of the space tether can be suppressed via the robust control strategy, which lead to smaller overshoot, less convergence time, and higher control accuracy during capturing operation.

Safety Operation Consciousness Realization of a MR Fluids-Based Novel Haptic Interface for Teleoperated Catheter Minimally Invasive Neurosurgery

Abstract: In catheter minimally invasive neurosurgery (CMINS), catheter tip collision with the blood vessel detection during the surgery practice is important. Moreover, successful CMINS is dependent on the discrimination of collision by a skilled surgeon in direct operation. However, in the context of teleoperated scenario, the surgeon was physically separated. Therefore, the lack of haptic sensation is a major challenge for a telesurgery scenario. A human operator-centered haptic interface is adopted to address this problem. In this paper, a teleoperated robotic-assisted surgery and psychophysics-based safety operation consciousness theory was presented. Moreover, a human operator-centered haptic interface design concept is first introduced into actuator choice and design. A semiactive haptic interface was designed and fabricated through taking full advantage of MR fluids. Furthermore, a mechanical model (force/torque model) was established. In addition, in case of no collision, transparency of a teleoperated system was realized; in case of collision, psychophysics-based collision discrimination control scheme was first presented to provide safety operation consciousness. Experiments demonstrate the usability of the designed haptic interface and correctness of the safety operation consciousness control scheme.

Vision-Guided Aerial Manipulation Using a Multirotor With a Robotic Arm

Abstract: This paper presents a vision guidance approach using an image-based visual servo (IBVS) for an aerial manipulator combining a multirotor with a multidegree of freedom robotic arm. To take into account the dynamic characteristics of the combined manipulation platform, the kinematic and dynamic models of the combined system are derived. Based on the combined model, a passivity-based adaptive controller which can be applied on both position and velocity control is designed. The position control is utilized for waypoint tracking such as taking off and landing, and the velocity control is engaged when the platform is guided by visual information. In addition, a guidance law utilizing IBVS is employed with modifications. To secure the view of an object with an eye-in-hand camera, IBVS is utilized with images taken from a fisheye camera. Also, to compensate underactuation of the multirotor, an image adjustment method is developed. With the proposed control and guidance laws, autonomous flight experiments involving grabbing and transporting an object are carried out. Successful experimental results demonstrate that the proposed approaches can be applied in various types of manipulation missions.

Design and Control of a Compliant Microgripper With a Large Amplification Ratio for High-Speed Micro Manipulation

Abstract: The design and control of a novel piezoelectric actuated compliant microgripper is studied in this paper to achieve fast, precise, and robust micro grasping operations. First, the microgripper mechanism was designed to get a large jaw motion stroke. A three-stage flexure-based amplification composed of the homothetic bridge and leverage mechanisms was developed and the key structure parameters were optimized. The microgripper was manufactured using the wire electro discharge machining technique. Finite element analysis and experimental tests were carried out to examine the performance of the microgripper mechanism. The results show that the developed microgripper has a large amplification factor of 22.6. Dynamic modeling was conducted using experimental system identification, and the displacement and force transfer functions were obtained. The position/force switching control strategy was utilized to realize both precision position tracking and force regulation. The controller composed of an incremental proportional-integral-derivative control and a discrete sliding mode control with exponential reaching law was designed based on the dynamic models. Experiments were performed to investigate the control performance during micro grasping process, and the results show that the developed compliant microgripper exhibits good performance, and fast and robust grasping operations can be realized using the developed microgripper and controller.

A 5-D Localization Method for a Magnetically Manipulated Untethered Robot Using a 2-D Array of Hall-Effect Sensors

Abstract: This paper introduces a new 5-D localization method for an untethered meso-scale magnetic robot, which is manipulated by a computer-controlled electromagnetic system. The developed magnetic localization setup is a 2-D array (8 × 8) of mono-axial Hall-effect sensors, which measure the perpendicular magnetic fields at their given positions. We introduce two steps for localizing a magnetic robot more accurately. First, the dipole-modeled magnetic field of the electromagnet is subtracted from the measured data in order to determine the robot's magnetic field. Second, the subtracted magnetic field is twice differentiated in the perpendicular direction of the array, so that the effect of the electromagnetic field in the localization process is minimized. Five variables regarding the position and orientation of the robot are determined by minimizing the error between the measured magnetic field and the modeled magnetic field in an optimization method. The resulting position error is 2.1 $\pm$ 0.8 mm and angular error is 6.7 $\pm$ 4.3 $^\circ$ within the applicable range (5 cm) of magnetic field sensors at 200 Hz. The proposed localization method would be used for the position feedback control of untethered magnetic devices or robots for medical applications in the future.

Cooperative Dynamic Positioning of Multiple Marine Offshore Vessels: A Modular Design

Abstract: In this paper, a new cooperative control scheme is presented for the dynamic positioning of multiple offshore vessels, subject to the influence of persistent ocean disturbances induced by wind, waves, and ocean currents. The vessels are interconnected through an underlying directed network. Unlike the traditional dynamic positioning of individual marine surface vessels, cooperative dynamic positioning controllers are developed based on a modular design approach. Specifically, a predictor module is proposed for estimating the unknown ocean disturbances, which is able to achieve the disturbance estimation as fast as possible. Then, the controller module is designed based on a dynamic surface control technique. The input-to-state stability of the closed-loop network system is established via cascade theory. Furthermore, this result is extended to output feedback, where only the position-yaw information is available. Another predictor module is developed for estimating the unmeasured velocities, as well as unknown ocean disturbances. Then, the dynamic surface control technique is employed to devise the output feedback controller. The proposed designs result in decoupled estimate and control, and can achieve fast adaptation for both state and output feedbacks. Results of comparative studies are given to substantiate the efficacy of the proposed methods.

Robust Relative Navigation by Integration of ICP and Adaptive Kalman Filter Using Laser Scanner and IMU

Abstract: This paper presents a robust six-degree-of-freedom relative navigation by combining the iterative closet point (ICP) registration algorithm and a noise-adaptive Kalman filter in a closed-loop configuration together with measurements from a laser scanner and an inertial measurement unit (IMU). In this approach, the fine-alignment phase of the registration is integrated with the filter innovation step for estimation correction, while the filter estimate propagation provides the coarse alignment needed to find the corresponding points at the beginning of ICP iteration cycle. The convergence of the ICP point matching is monitored by a fault-detection logic, and the covariance associated with the ICP alignment error is estimated by a recursive algorithm. This ICP enhancement has proven to improve robustness and accuracy of the pose-tracking performance and to automatically recover correct alignment whenever the tracking is lost. The Kalman filter estimator is designed so as to identify the required parameters such as IMU biases and location of the spacecraft center of mass. The robustness and accuracy of the relative navigation algorithm is demonstrated through a hardware-in-the loop simulation setting, in which actual vision data for the relative navigation are generated by a laser range finder scanning a spacecraft mockup attached to a robotic motion simulator.

Shape Detection Algorithm for Soft Manipulator Based on Fiber Bragg Gratings

Abstract: The shape of soft a manipulator cannot be sensed by the operator directly, when applied to rescue of mine disaster, science exploration, or minimally invasive surgery due to the narrow and closed environment. Shape information is sometimes important for the soft manipulator to be controlled. In order to deal with the problem of shape sensing, a shape sensing algorithm and sensor network based on Fiber Bragg Gratings (FBGs) are introduced in this paper. The shape sensing algorithm is based on piecewise constant curvature and torsion assumption, and can translate the curvature and torsion measured by sensor network into global positions and orientations of nodes. Three-dimensional experiments show that the algorithm introduced in this paper can achieve high accuracy for 3-D shapes.

A Swing Constraint Guaranteed MPC Algorithm for Underactuated Overhead Cranes

Abstract: In practice, for an overhead crane, the payload swing needs to be kept within an acceptable domain to avoid accidents. However, as an unactuated state, the swing angle is usually very difficult to be controlled properly. Besides that, the constraints on the control input should also be carefully considered to avoid possible actuator saturation. These problems bring much challenge for control development of an underactuated crane. Motivated by this observation, a novel model predictive control (MPC) algorithm, which guarantees swing constraints theoretically and is free of actuator saturation, is proposed in this paper for a 2-D overhead crane system to achieve satisfactory performance. That is, for an overhead underactuated crane, a discrete model is first obtained by some linearization and discretization technique, based on which a novel MPC algorithm is constructed, which theoretically ensures that the payload swing is kept within the allowable range and that the control is always free of saturation. Specifically, the control input constraint is successfully addressed by solving a constrained optimization problem for the MPC method, while the kinematic equation of the crane system, which plays the role of the connection between the payload swing and the trolley movement, is utilized to convert the swing bound into some constraints on the control input so as to handle it conveniently. Both simulation and experimental results are investigated to illustrate the superior performance of the proposed method.

An Inversion-Free Predictive Controller for Piezoelectric Actuators Based on a Dynamic Linearized Neural Network Model

Abstract: Piezoelectric actuators (PEAs) are widely used in high-precision positioning applications. However, the inherent hysteresis nonlinearity seriously deteriorates the tracking performance of PEAs. To deal with it, the compensation of the hysteresis by using its inverse model (called inversion-based) is the popular method in the literature. One major disadvantage of this method is that the tracking performance of PEAs highly relies on its inverse model. Meanwhile, the computational burden of obtaining the inverse model is overwhelming. In addition, the physical constraints of the input voltage of PEAs is hardly handled by the inversion-based method. This paper proposes an inversion-free predictive controller, which is based on a dynamic linearized multilayer feedforward neural network (MFNN) model. By the proposed method, the inverse model of the inherent hysteresis is not required, and the control law can be obtained in an explicit form. By using the technique of constrained quadratic programming, the proposed method still works well when dealing with the physical constraints of PEAs. Moreover, an error compensation term is introduced to reduce the steady-state error if the dynamic linearized MFNN cannot approximate the PEA's dynamical model satisfactorily. To verify the effectiveness of the proposed method, experiments are conducted on a commercial PEA. The experiment results show that the proposed method has a satisfactory tracking performance even with high-frequency references. Comparisons demonstrate that the proposed method outperforms some existing results.

New Applications of an Automated System for High-Power LEDs

Abstract: An automatic system based on thermoelectric cooler (TEC), a microfan, and microcontroller is first applied to thermal management of high-power light-emitting diodes (LEDs). Its hardware is composed of microcontroller as a control core, K-type thermocouples as acquisition devices, and TEC and a microfan with heatsink as cooling vehicles. The experiment confirms that the LEDs substrate temperature can be controlled effectively, and indicates that the LED chips are operating reliably. Specifically, in high-temperature environments of 43 °C, the system can automatically drop to the low set temperature (30 °C) due to thermoelectric effect driven by TEC. Heat transfer analysis shows that maximum LED power cooled by the system is 106.7 W, and the total power consumption of the automatic cooling system is only 8.85 W. The automatic cooling system has a high cooling efficiency.

An Attitude Estimation Algorithm for Mobile Robots Under Unknown Magnetic Disturbances

Abstract: Attitude estimation is a crucial aspect for navigation and motion control of autonomous vehicles. This concept is particularly true in the case of unavailability of localization sensors when navigation and control rely on dead reckoning strategies; in this case, indeed, the orientation estimate is also used along with speed measurements to update the position estimate. Among the different approaches proposed in the literature, the de facto state of the art in this field is represented by nonlinear complementary filters: they fuse the measurements of angular rate obtained through gyroscopes, and a measurement of gravity and Earth's magnetic field vectors respectively obtained through accelerometers and magnetometers. This paper is focused on an attitude estimation strategy for autonomous underwater vehicles (AUV). The proposed novelty includes the identification of some critical issues that arise when AUV attitude estimation algorithms are applied in practice. They are mainly due to the use of low-accuracy low-cost microelectromechanical systems (MEMS) sensors and on different sources of magnetic disturbances. Some strategies to overcome the identified issues are proposed, including the integration of a single-axis fiber optic gyroscope (FOG) that ensures a considerable performance improvement with a moderate cost increase. The proposed strategies for detection of issues and sensor fusion have been experimentally tested and validated in a real application scenario estimating the attitude of an AUV performing a lawn mower path. The expected performance improvement is confirmed; the obtained results are described and analyzed in this paper.

Origami Robot: A Self-Folding Paper Robot With an Electrothermal Actuator Created by Printing

Abstract: A piece of paper has many useful characteristics; it is affordable, lightweight, thin, strong, and highly absorbent. These features allow inexpensive and flexible devices to be fabricated easily and rapidly. We have proposed a new field, “paper mechatronics,” which merges printed robotics and paper electronics, and to realize electronic and mechanical systems by printing. Herein, we develop a method to print an actuator and a structure on a sheet of paper. A trilayer electrothermal actuator is printed to activate a printed robot. The paper self-folds along the printed pattern to form the three-dimensional (3-D) structure of the robot body. We also investigate important factors necessary to develop a printed robot. Experiments, including finite element analysis (FEA), confirm our bimetal modeling assumption for the printed actuator and improve the locomotive ability. The key factors in self-folding are paper thickness and humidity. Our findings can improve the reliability of printed robot designs. A self-folding A7-sized paper robot demonstrates locomotion at 10 mm per step.

Design and Control of an Agile Robotic Fish With Integrative Biomimetic Mechanisms

Abstract: Flying insects and swimming fishes have high efficiency and high maneuverability in air and water, respectively. Their wings and fins have evolved for many ages to adapt to propelling in the complex environment. In this paper, an integrative biomimetic robotic fish is proposed and developed, which combines the advantages of insect wings and fish fins to achieve a high agility underwater. In the robotic fish, two caudal fins were equipped at the tail of the robotic fish in parallel as the main propulsion mechanism, the opposite flapping of the two caudal fins generates mutually opposing lateral forces during cruising, which leads to a stable and high-performance swimming. In addition, two pectoral fins that mimic the function of insect wings were equipped at two sides of the robotic fish, which enhances the robotic fish maneuverability in vertical plane. Moreover, a central pattern generator model was designed to achieve the versatile maneuvering motions, motion switching, and autonomous swimming with an obstacle avoiding ability. The experiments have demonstrated that the robotic fish can swim more stably and efficiently with versatile maneuver motions by taking advantage of the integrative propulsion mechanism. The developed robotic fish have many potential applications for its agility, stable swimming, and low-cost structure.

Nonlinear Control of Robotic Manipulators Driven by Pneumatic Artificial Muscles

Abstract: Lightweight, compliant actuators are particularly desirable in safety-conscious robotic systems intended for interaction with humans. Pneumatic artificial muscles (PAMs) exhibit these characteristics and are capable of higher specific work than comparably sized hydraulic actuators and electric motors. However, control of PAM-actuated systems has proven difficult due to the highly nonlinear nature of the actuators and the pneumatic systems driving their actuation. This study develops and investigates the performance of three advanced control strategies—sliding mode control, adaptive sliding mode control, and adaptive neural network (ANN) control—each containing a distinct level of a priori model knowledge, to enable smooth and accurate motion tracking of a single degree-of-freedom PAM-actuated manipulator. Originally developed by J.-J. Slotine and R.M. Sanner, the specific controllers employed in this study are significantly modified for application to pneumatically actuated open-chain manipulators with complex nonlinear dynamics. The two adaptive controllers are updated online and require no pretraining step. Several experiments are performed with each controller to evaluate and compare closed-loop tracking performance. Results highlight the dependence of a preferred control strategy on the level of model completeness and quality, and suggest that in most PAM-actuated manipulator scenarios, the ANN controller is preferable because it does not require a model of the pneumatic system or joint mechanism design, which can be difficult and time consuming to characterize, and is robust to changes in PAM actuator characteristics (due to fatigue or replacement).

Modeling and Identification of Piezoelectric-Actuated Stages Cascading Hysteresis Nonlinearity With Linear Dynamics

Abstract: In this paper, we propose a new modeling and identification approach for piezoelectric-actuated stages cascading hysteresis nonlinearity with linear dynamics, which is described as a Hammerstein-like structure. In the proposed approach, the hysteresis and linear dynamics together with the delay time and higher order dynamic behaviors are obtained with three data-driven identification steps under designed input signals. In the first step, the step input signal is applied to estimate the delay time of the piezoelectric-actuated stages. In the second step, the autoregression with exogenous signal identification algorithm is adopted to identify the linear dynamics using a small-amplitude band-limited white noise input signal. In the third step, with the identified linear dynamics model, the parameters of the rate-independent Prandtl–Ishlinskii hysteresis model are identified by the particle swarm optimization algorithm using a simple low-frequency triangle input signal with different amplitudes. Finally, the experimental results on a piezoelectric-actuated stage show that both the hysteresis and dynamic behaviors of the piezoelectric-actuated stage are well predicted by the proposed modeling method. In addition, we provide the analysis of quantitative prediction errors of the identified model with comparison to experimental data, which clearly demonstrate the effectiveness of the proposed approach.

Robust Control of Networked Systems With Variable Communication Capabilities and Application to a Semi-Active Suspension System

Abstract: This paper is concerned with a robust control problem of a class of networked systems operated within a multiple communication channels (MCCs) environment. A practical scenario is considered that the active channel in such MCCs for the data communication is switched and the switching is governed by a Markov chain. For each channel, two network-induced imperfections, time delays, and packet dropouts with different characteristics are taken into account. Suppose that the practical plant is subject to energy-bounded disturbance and norm-bounded uncertainties, a robust controller is designed to ensure that the closed-loop system is robustly stable and achieves a disturbance attenuation index against the phenomenon of channel switching. A semi-active suspension system is introduced to illustrate the effectiveness, applicability of the proposed approach, and to demonstrate the advantages of the MCCs scheme within the channel-switching framework.

Frequency-Domain ILC Approach for Repeating and Varying Tasks: With Application to Semiconductor Bonding Equipment

Abstract: Iterative learning control (ILC) enables high performance for exactly repeating tasks in motion systems. Besides such tasks, many motion systems also exhibit varying tasks. In such cases, ILC algorithms are known to deteriorate performance. An example is given by bonding equipment in semiconductor assembly processes, which contains motion axes with tasks that can vary slightly. The aim of this paper is to develop an ILC approach that obtains high machine performance for possibly varying tasks, while enabling straightforward and effective industrial design rules. In particular, a frequency-domain-based design of ILC filters is pursued, which is combined with basis functions to cope with variations in tasks. Application to a high-speed axis of an industrial wire bonder shows that high servo performance is obtained for both repeating and varying tasks.

Wave-Variable-Based Passivity Control of Four-Channel Nonlinear Bilateral Teleoperation System Under Time Delays

Abstract: An innovative wave-based time-domain passivity approach applied to a four-channel nonlinear teleoperation system is proposed. The primary objective of this approach is to enhance system transparency while maintaining stability in the presence of random time delays. The system stability for different scenarios of human and environment condition is analyzed. The method is validated through experimental work based on a 3-DOF bilateral teleoperation system. The experimental results show that the proposed control algorithm can robustly guarantee the system stability, and simultaneously provide better performance than methods developed in previous work.

Fall Detection and Prevention Control Using Walking-Aid Cane Robot

Abstract: An intelligent walking-aid cane robot is developed for assisting the elderly and the physically challenged with walking. A motion control method is proposed for the cane robot based on human walking intention estimation. Moreover, the safety is investigated for both the cane robot and the elderly. The fall detection and prevention concepts are proposed to guarantee the safety of the elderly while walking with the cane robot. However, the deficiency of the cane robot is that it can be overturned easily because of its small size and light weight. Therefore, a controllable universal joint is designed for adjusting the tilted angle of its stick. The stability of the cane robot during the fall prevention procedure can then be enhanced by controlling the tilted angle of stick to an optimal position. A center of pressure (COP)-based fall detection (COP-FD) method is used to detect the risk of falling. In this method, the user's COP is calculated in real time using an integrated force sensory system, which comprises a six-axis force/torque sensor and an inshoe load sensor. When the COP reaches the boundary of the specified safety area, i.e., the support polygon, it is assessed that the user is going to fall down. The COP-FD method can be used in various cases of falling. However, for cases of stumbling, a rapid fall detection method is proposed based on leg motion detection, and Dubois' fuzzy possibility theory is applied to adapt to different users. When the risk of falling has been detected, a fall prevention impedance control is executed considering both the interaction compliance and system stability. In the study, a control simulation platform was established to obtain the optimal controller parameters, and all the proposed methods were finally verified through simulations and experiments.

A Particle Filter-Based Matching Algorithm With Gravity Sample Vector for Underwater Gravity Aided Navigation

Abstract: Gravity matching algorithm is a key technique of gravity aided navigation for underwater vehicles. The reliability of traditional single point matching algorithm can be easily affected by environmental disturbance, which results in mismatching and decrease of navigation accuracy. Therefore, a particle filter (PF)-based matching algorithm with gravity sample vector is proposed. The correlation between adjacent sample points of inertial navigation system is considered in the vector matching algorithm in order to solve the mismatching problem. The current sampling point matching result is rectified by the vectors composed by the selected sampling points and matching point. The amount of selected sampling points is determined by the gravity field distribution and the real-time performance of the algorithm. A PF-based on Bayesian estimation is introduced in the proposed method to overcome the divergence disadvantage of the traditional point matching algorithm in some matching areas with obvious gravity variation. Simulation results prove that compared with the traditional methods, the proposed method is robust to the changes of gravity anomaly in the matching areas, with more accurate and reliable matching results.

Comprehensive Design Methodology of Input- and Output-Split Hybrid Electric Vehicles: In Search of Optimal Configuration

Abstract: Despite high potentials of power-split hybrid electric vehicles (PS-HEV), their design and control problems are nontrivial. For instance, there exist 24 ways of connecting four components (two electric machines, an engine, and a vehicle wheel) with a planetary gear (PG), and more than thousand ways with two PGs. Furthermore, when PG and final drive ratios are considered design variables, finding an optimal design that fulfills both high fuel economy and short acceleration time is a challenge. In this paper, a systematic configuration searching methodology is proposed to find an optimal single PG PS-HEV configuration for both performance metrics. First, by identifying all the possible single PG configurations and reorganizing them into a compound lever design space, the performance metrics are explored in the continuous design space. Then, the designs are mapped onto the “fuel economy—acceleration performance” plane to solve the multiobjective configuration selection problem. Thus, a highly promising configuration (“o6”), which outperforms Prius design in the acceleration performance, is selected among Pareto Frontier. A case study has been conducted on a sport utility vehicle specification. The study illustrates that the performance metrics of candidate configurations change significantly, and thus, selecting a proper configuration is crucial to evoke full potential of the given powertrain components.