Adaptive Actuator Research Articles

Enhancing and Decoding the Performance of Muscle Actuators with Flexures

Leveraging living muscle as an efficient and adaptive actuator for soft robots has been of increasing interest over the past decade, with a focus on proof‐of‐concept demonstrations of function. Reproducible design and scalable manufacturing of biohybrid machines requires methods to increase the stroke output of strain‐limited muscle actuators and enable accurate and precise quality control and performance monitoring. Compliant mechanical elements, termed flexures, are designed to enhance muscle contractile stroke to ≈5× previously reported values and decode contraction dynamics with high spatiotemporal resolution. Combining rigid and flexible elements within a linear elastic flexure enables us to outperform the sensitivity of gold standard elastomeric beam‐based measurements of muscle contraction at both low‐ and high‐frequency stimulations. Flexures are leveraged to make quantitative comparisons of force, work, and power outputs in muscle actuators, driving us to discover a new observation of frequency‐dependent fatigue in muscle, and also develop a novel method for tuning muscle contractile dynamics in a frequency‐independent manner. By enhancing the contractile stroke of muscle actuators and precisely tuning contractile dynamics and endurance with unprecedented precision, this study sets the stage for leveraging flexures to improve robust, reproducible, and predictive design and manufacturing of next‐generation biohybrid robots.

An Adaptive Actuator Driver for AC Contactors

The actuator drivers are rarely transplanted among different contactors because the self-tuning of excitation control parameters either takes a long time or is relevant to many contactor structural parameters. An adaptive actuator driver for AC contactors is proposed based on the coil impedance estimation and mathematical equations between the coil impedance and excitation control parameters. The coil impedance estimation and its error correction are established through the voltage balance equation when the contactor is in the open position. The coil inductance estimation is established through the zero-state response equation when the contactor is in the closed position. The mathematical equations are established based on the equivalence of the electromagnetic force under different excitation methods, the power consumption model, and the control system's transfer function. A geometric feature of the flux linkage waveform is established to identify whether a contactor is in the closed position and realize the self-transition of the driver's working state. Then, self-tuning regulators for the excitation control parameters are realized. This driver has a plug-and-play function for AC contactors and can reduce the contactor's power consumption. This driver may be prospectively reduced to a chip.

Numerical evaluation of doubly clamped self-adaptive fins acting as vortex generators inside micro heat sinks (MHS)

The continuous increase in the power density of integrated circuits (ICs) has placed thermal management as a key focus for the ICT community. Current liquid cooling systems are usually not optimized for the real operating conditions of advanced electronics, which have heat loads that can vary in time and space. Hence, conventional cooling devices lead to non-uniform temperature patterns at the package level and overcooled systems. This work proposes the use of thermally adaptive actuators, based on doubly clamped fins able to change its shape with temperature, to improve the performance of micro heat sinks with an optimized adaptation to temporal and spatially variable thermal parameters. The effect of this thermally responsive bistable fins on the efficiency of two different micro heat sinks (MHS), one based on microchannels and other on a matrix of microfluidic cells, is numerically evaluated. Results demonstrated a maximum heat transfer enhancement of 40% when adding the clamped-clamped fins within a microchannel and 20% when the fins were placed inside a microfluidic cell. Additionally, savings on hydraulic pumping power have been quantified between 8% and 11%.

Adaptive Actuator Failure Compensation Control of Uncertain Nonlinear PDE–ODE Cascaded Systems

Adaptive Actuator Failure Compensation Control of Uncertain Nonlinear PDE–ODE Cascaded Systems

Adaptive Actuator Failure Compensation Control: A New Scheme Based on Fault Detection and Isolation

In flight control systems, multiple actuator stuck (or hardover) failures would cause saturation of the remaining healthy actuators and result in system instability. In this article, a new adaptive actuator failure compensation control scheme is proposed for a class of nonlinear systems with multiple redundant inputs. Under a mild assumption on the time interval between two successive failures, an online fault detection and isolation (FDI) algorithm is introduced, with which an actuator failure can be exactly identified in a given finite time without using the persistent excitation (PE) condition. The main advantage of the proposed scheme is that with the help of the FDI algorithm, an actuator with stuck failure (SF) can be deactivated before a new failure occurs and therefore, the drawback of the current adaptive failure compensation control approach that may cause more control effort or even saturation with multiple actuators being stuck can be overcome.

Adaptive Actuation of Magnetic Soft Robots Using Deep Reinforcement Learning

Magnetic soft robots (MSRs) have attracted growing interest due to their unique advantages in untethered actuation and excellent controllability. However, actuation strategies of these robots have long been designed out of heuristics. Herein, it is aimed to develop an intelligent method to solve the inverse problem of finding workable magnetic fields for the actuation of strip‐like soft robots entirely based on deep reinforcement learning algorithms. Magnetic torques and a dissipation force to the Cosserat rod model are introduced, and the developed model to simulate the dynamics of MSRs is utilized. Meanwhile, under the reinforcement learning framework, soft robots to move forward without human guidance are successfully trained, and the results intelligently adapt to different magnetization patterns and magnetic field restrictions. The learned actuation strategies by directly applying simulated magnetic fields to real MSRs in an open loop way are validated. The experimental results show good accordance with simulations. By presenting the first case of using strategies entirely generated by reinforcement learning to control real MSRs, the potential of using reinforcement learning to achieve autonomous actuation of MSRs is demonstrated, which can be used to establish a route for the creation of highly adaptive design framework.

E-Morph as a New Adaptive Actuator for Soft Robotics

The paper presents an electromagnetically-controlled shape morphing composite (e-morph) as a new variable stiffness changing actuator for soft robotic applications. The structure is composed of a granular core and an outer shell made of silicon, with channels filled with liquid metal. Under an electromagnetic force, the actuator can change its shape in a controlled way, while the jamming mechanism allows the granules used in the core to maintain the desired shape and change their stiffness at a rate higher than 6. This paper provides a detailed description of the concept, empirical and simulation results of the samples tested. Finally, suggestions are made for initial applications using this actuator.

Adaptive Simultaneous Magnetic Actuation and Localization for WCE in a Tubular Environment

Simultaneous magnetic actuation and localization is a promising technology to realize active wireless capsule endoscopy. In this article, an adaptive approach is proposed to efficiently propel and precisely locate a magnetically actuated capsule in unknown complex tubular environments. In order to track the capsule in real time in a large workspace, we improve upon our previous external sensor-based magnetic localization strategies by proposing an adaptive method to automatically activate an optimal subset of sensors during the capsule movement and developing a simplified multiple objects tracking algorithm to estimate the six-dimensional pose of the capsule in real time. Moreover, based on a study of capsule locomotion in tubular environments with different shapes, we propose to dynamically adjust the pose of the actuator to achieve efficient and robust propulsion of the capsule in an unknown tubular environment. The effectiveness of our proposed system is validated in extensive experiments on phantoms and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex vivo</i> animal organs. Compared with the state-of-the-art, our method can realize the closed-loop control of a magnetic capsule without requiring sensing modules to be placed inside the capsule, which can reduce the space constraints and energy consumption of the capsule. Moreover, the system and algorithms for adaptive actuation and localization can provide an improved propulsion speed, workspace range, and localization update frequency as well as a comparable localization accuracy.

Fractional order robust adaptive control design for actuator failure compensation in dual ball-beam system

This paper is to proposes a fractional order adaptive control scheme design for actuator failure compensation in order to balance of a ball-beam system with redundant actuators. The proposed controller is based on a fractional order sliding surface from which an adaptive actuator failure compensation control scheme is obtained. In order to control the dual ball-beam system with redundant motors, a linearized model is derived, which is subjected to actuator failures. A set of numerical simulation tests representing few selected types of actuator failures scenarios are carried out to assess the output tracking performance of the proposed controller design when dealing with such failures

Design of an Eye-in-Hand Smart Gripper for Visual and Mechanical Adaptation in Grasping

With the advancement of robotic technologies, more and more tasks in industrial and commercial applications rely on the use of robots to assist or even replace humans. To fulfill the needs of grasping and handling different objects, the development of a universal grasping device acting as an end-effector to a robotic manipulator has been one of the main robotic research and development focuses. Therefore, this study was aimed at the development of a general robotic gripper with three fingers for adaptive actuation and an eye-in-hand vision system. With the adaptive actuation feature, each finger of the robotic gripper contained multiple degrees of freedom that allowed the finger to change its shape to wrap around an object’s geometry adaptively for stable grasping. With the eye-in-hand configuration in the adaptive gripper, it offered advantages including occlusion avoidance, intuitive teleoperation, imaging from different angles, and simple calibration. This study proposed and integrated a plug-and-play gripper module, controller module, and visual calculation module all in the model smart gripper, of which the gripper was further validated by calibrated experiments. The proposed gripper featured mechanical adaptation and visual servoing adaptivity to achieve 100% gripping success rate when gripping a moving target of any shape that was carried by conveyor belt with moving speed less than 70 mm/s. By integrating mechanical and visual adaptivity, the proposed gripper enabled the inclusion of intelligence in robotic applications and can further be used in smart manufacturing and intelligent robotic applications.

Designing Prosthetic Hands With Embodied Intelligence: The KIT Prosthetic Hands.

Hand prostheses should provide functional replacements of lost hands. Yet current prosthetic hands often are not intuitive to control and easy to use by amputees. Commercially available prostheses are usually controlled based on EMG signals triggered by the user to perform grasping tasks. Such EMG-based control requires long training and depends heavily on the robustness of the EMG signals. Our goal is to develop prosthetic hands with semi-autonomous grasping abilities that lead to more intuitive control by the user. In this paper, we present the development of prosthetic hands that enable such abilities as first results toward this goal. The developed prostheses provide intelligent mechatronics including adaptive actuation, multi-modal sensing and on-board computing resources to enable autonomous and intuitive control. The hands are scalable in size and based on an underactuated mechanism which allows the adaptation of grasps to the shape of arbitrary objects. They integrate a multi-modal sensor system including a camera and in the newest version a distance sensor and IMU. A resource-aware embedded system for in-hand processing of sensory data and control is included in the palm of each hand. We describe the design of the new version of the hands, the female hand prosthesis with a weight of 377 g, a grasping force of 40.5 N and closing time of 0.73 s. We evaluate the mechatronics of the hand, its grasping abilities based on the YCB Gripper Assessment Protocol as well as a task-oriented protocol for assessing the hand performance in activities of daily living. Further, we exemplarily show the suitability of the multi-modal sensor system for sensory-based, semi-autonomous grasping in daily life activities. The evaluation demonstrates the merit of the hand concept, its sensor and in-hand computing systems.

A Biomimetic Drosera Capensis with Adaptive Decision‐Predation Behavior Based on Multifunctional Sensing and Fast Actuating Capability (Adv. Funct. Mater. 13/2022)

Adaptive Actuation In article number 2110296, Huaping Wu, Jizhou Song, Aiping Liu, and co-workers develop a biomimetic Drosera capensis that is capable of multifunctional self-sensing, automatic regulation, and adaptive actuation to diverse stimuli in a closed-loop fashion. With the synergistic effect of multifunctional sensing and fast actuating schemes, the artificial drosera capensis deconvolutes multiple catching features and thus holds impressive predatory behavior for ideal-sized prey.

Adaptive Actuator Failure Compensation on the Basis of Contraction Metrics

This letter develops an adaptive actuator failure compensation method for nonlinear systems with unmatched parametric uncertainty based on contraction metrics. The proposed method, which is constructed by benefiting from the recent achievements on contraction metrics based adaptive control techniques, ensures the closed-loop stability and asymptotic tracking of the desired trajectory in the presence of actuator failures. In particular, a sufficient convex condition is derived for constructing a valid metric, by which a quadratic program-based controller is obtained to determine the inputs of the actuators. The introduced method is more general than the common adaptive actuator failure compensation methods, as it does not require the system to have an identical relative degree for all inputs and be transformable into the parametric strick-feedback or feedback linearization form. Besides, it can be enriched with learning-based algorithms and common robust modifications. Simulation results are presented to verify the effectiveness of the proposed controller.

A Biomimetic Drosera Capensis with Adaptive Decision‐Predation Behavior Based on Multifunctional Sensing and Fast Actuating Capability

AbstractThe sophistication, adaptability, and complexity of biological systems have provided enormous inspiration and have been a continuous source of numerous innovations. Soft living organisms like drosera capensis have amazing predatory behavior that can capture prey of ideal size, enabling them to interact with environmental stimuli efficiently. Mimicking such natural intelligence in artificial systems with systematical functions of multiple information perception, neuronal transmission, and adaptive motility remains a grand challenge. Here, a biomimetic drosera capensis is reported that is capable of multifunctional self‐sensing, automatic regulation, and adaptive actuation in response to diverse stimuli with intelligent predation capability in an entirely closed‐loop fashion. The functional system heterogeneously integrates the thermal‐responsive soft actuator as the muscle‐like motor and flexible tactile, strain, and piezoelectric multimodal sensors as somatosensory receptors. With the synergistic effect of multifunctional sensing and fast actuating schemes, the artificial drosera capensis deconvolutes multiple characteristics of the catching process (e.g., strain rate, magnitude, and direction) and thus holds impressive predatory behavior for ideal‐sized prey. This electronically innervated artificial drosera capensis with multimodal sensing and self‐regulated actuating capability through the closed‐loop control of sensing and actuating system paves the way for the development of adaptive soft robots.

Adaptive actuator fault-tolerant control for non-minimum phase air-breathing hypersonic vehicle model

In this paper, the tracking control problem of non-minimum phase flexible air-breathing hypersonic vehicles (AHSV) is investigated subject to actuator fault, external disturbances and parameters uncertainties. The study is began with a series of control-oriented manipulations: first, the input–output dynamics are derived by using feedback linearization method and the internal dynamics of AHSV are constructed; then, the zero dynamics stability analysis is conducted to verify the non-minimum phase characteristic of AHSV. In order to realize output tracking of the non-minimum phase system with sufficient accuracy, an adaptive fault tolerant controller (FTC) is proposed based on an output-redefinition making the zero-dynamics with respect to the new output stable. Additionally, robust adaptive laws are utilized for the estimation of unknown parameters and actuator failure compensation of the AHSV model. Furthermore, the stability of the closed-loop system is analyzed based on the Lyapunov stability theory. At last, the numerical simulation results are provided to demonstrate the effective tracking performance of the proposed FTC scheme.

Adaptive actuator failure compensation for cooperative robotic manipulators with parameter uncertainties

SummaryThis article develops a new framework of adaptive actuator failure compensation control for cooperative manipulator systems with parameter uncertainties in addition to actuator failures, and designs and analyzes effective actuator failure compensation schemes for such robotic systems. The new adaptive control design uses an integration of multiple individual failure compensators and direct adaptation to handle various types of uncertainties in such robotic systems. The design can also be used for concurrent actuator failure cases, to expand the capability of adaptive actuator failure compensation. With a complete proof and performance analysis, it is shown that the proposed control scheme guarantees the desired closed‐loop stability and asymptotic output tracking, despite actuator failures whose patterns, time instants and values are all unknown. Simulation results of a benchmark cooperative manipulator system are presented to verify the desired control performance of the system with both typical constant and square‐wave actuator failure signals.

A New Strategy of Discretionarily Reconfigurable Actuators Based on Self‐Healing Elastomers for Diverse Soft Robots

AbstractActuators have shown great promise in many fields including soft robotics. Since reconfiguration allows actuators to change their actuation mode, it is considered a key characteristic for new‐generation adaptive actuators. However, it remains a challenge to design simple and universal methods to fabricate actuators that can be reconfigured to allow diverse actuation modes. Here, a macroscopically discretionary healing‐assembly strategy to fabricate reconfigurable soft actuators based on intrinsic self‐healing poly(dimethylglyoxime‐urethane) (PDOU) elastomers is developed. The PDOU elastomers with different degrees of crosslinking show different responsiveness to solvents, and are seamlessly healed. Crosslinked and non‐crosslinked PDOU elastomers as building units are healing‐assembled into actuators/robots with diverse actuation behaviors. Notably, the assembled actuators/robots are readily reprogrammed to exhibit multiple actuation modes by simply tailoring and reassembling without any external stimuli. This work paves a new, simple, powerful, and universal method to construct sophisticated soft robots.

Neural networks-based adaptive control of uncertain nonlinear systems with unknown input constraints

In this work, we solve the adaptive actuator backlash compensation control problem of uncertain nonlinear systems. A new generalized backlash model is first proposed, which takes both the actuator perturbation and unidentifiable coupling into account, and hence captures the practical backlash behavior more accurately. Nevertheless, such a model makes the adaptive control design difficult, where the most challenging one is that the unrecognizable coupling makes traditional compensation structure no more feasible. To address this issue, we propose an adaptive compensation control structure synthesizing neural networks learning and novel smooth backlash inverse model. With the established compensator and the iterative control design of compensator input, an adaptive neural controller is subsequently proposed to guarantee that all signals of the closed-loop system are bounded, and the tracking error converges to residual of zero asympotically. Simulation results are given to verify the effectiveness of the proposed control scheme.

Linear Adaptive Fault Tolerant Control against Aircraft Actuator Failures for Wing Rock Suppression

In this paper we consider the problem of the suppression of wing rock phenomenon in presence of actuator faults. The main aim of this work is the application of a linear adaptive actuator failure compensation control scheme for wing rock motion of a slender delta wing. First, the analytical nonlinear model that characterizes the phenomenon of wing rock is given, and its behavior for different angles of attack is presented. Then, an adaptive actuator failure compensation redundant control design is proposed, and applied for a rolling motion regulation in the presence of actuator faults. Finally, numerical simulations are conducted to show the effectiveness of the control scheme for wing rock suppression for different actuator failures scenarios.

Adaptive Robust Failure Compensation Control for Servo System Driven by Twin Motors

In the context of failure control of servo system driven by twin motors, there are still no available results to compensate unknown actuator failures which seem inevitable in practice by adaptive backstepping technique. Therefore, to rise the reliability of the system, we aim at addressing such a problem by proposing an adaptive robust actuator failure compensation control scheme based on backstepping technique for servo system driven by twin motors. Unlike the traditional backstepping, the estimation of unknown coefficient of intermediate state variable is introduced in coordinate changes. In addition, matching and non-matching uncertainties have been fully considered in the controller design. Simulation results show that the designed controller can ensure the boundedness of all the signals no matter actuator failures occur or not.