In a rehabilitation exoskeleton, stable and safe operation is of central importance in rehabilitation training. This article develops a soft prescribed performance (SPP)-based reinforcement learning (RL) control method to address the conflict between performance constraints and system degradation, ensuring high accuracy and safe operation. First, a tunnel-type prescribed performance function is used to achieve faster convergence and smaller overshoot. Safety boundaries are used to define the tolerable error range, and an intermediate system links the safety and soft boundaries. The soft boundaries are dynamically adjusted to ensure safe operation by temporarily relaxing constraints during performance degradation. An RL approach based on an actor-critic (AC) structure is employed to handle unknown lumped disturbance. Theoretical analysis confirms the stability of the closed-loop system. Furthermore, a series of experiments is conducted on a self-built upper-limb rehabilitation exoskeleton robot driven by pneumatic artificial muscles to validate the effectiveness and robustness of the proposed method.

The complex interactions between flexible needles and tissues present significant challenges in predicting the needle shape during the puncture procedure. In particular, the accurate prediction of flexible needle shape during insertion into complex multilayer tissues, especially when measurement feedback involves non-Gaussian noise, remains an open problem. In this article, we develop a novel reinforcement learning-based active modeling scheme to predict the deflection of the robotic flexible needle. First, the active modeling scheme is constructed by deriving an extended Kalman filter under the maximum correntropy criterion to enhance insensitivity to non-Gaussian noise. Subsequently, based on this scheme, the reinforcement active modeling (RAM) framework is built by incorporating reinforcement learning to compensate for the modeling residuals. Specifically, the theoretical convergence of the proposed scheme is proved by using the Banach fixed-point theorem, thereby ensuring the reliability of needle shape prediction. Finally, a series of comparative experiments is carried out on a self-built robotic flexible needle. The experimental results demonstrate the superior performance of the proposed deflection predictor. Under non-Gaussian noise conditions, the proposed RAM scheme achieves a generalization prediction error reduction of 46.4% in RMSE and over 76.1% in Var during insertion into unknown multilayer tissue.

Increasing evidence highlights the role of proprio-ceptive deficits in falls, emphasizing the need for targeted rehabilitation in populations with functional movement disorders. Despite advances in rehabilitation robots, movement constraints still hinder active engagement of the lower limb muscles, thereby limiting the effectiveness of proprioceptive training. In this work, We developed a neuro-rehabilitation robotic platform to address this need by physically and virtually simulating multi-terrain scenarios. The robot introduces common perturbations, such as uneven mountain trails, sandy beaches, and bumpy bus rides, to assess user stability and recovery, thereby assisting in the design of individualized training programs. The platform enhances neuromuscular responses across multiple directions and facilitates targeted muscle contraction through motor tasks that combine proprioceptive and visual feedback. Preliminary studies demonstrated that the robot successfully facilitated a complete range of ankle rotational movements. Electromyographic analysis revealed increased activation of specific muscle groups, changes in muscle loading and contraction patterns, suggesting that the system recruits multiple muscle groups while enhancing proprioceptive input to periarticular soft tissues. The proposed robot and control strategies established a feasible solution to enhance proprioception rehabilitation.

The integration of origami structures into soft robotics has enriched the adaptability and functionality of the soft robots. Our research group has developed a cable-driven origami robot attached to an arc frame, which enables its deployment in an MR bore and manipulation of medical tools. However, the control of such origami robots still faces challenges such as nonlinear dynamics, unstructured environment, and external payload. This paper introduces an active modeling compensation control method using Koopman operator (K-AMCC) for the Yoshimura origami manipulator, enabling its accurate trajectory tracking with payloads under different orientations. This active modeling method exploits Koopman operator theory and Kalman filter to estimate the model error and synergies the linear quadratic regulator to compensate the modeling errors. The rectangular and circular trajectory tracking experiments under varying payloads and orientations were carried out. The results demonstrate the K-AMCC method’s ability to improve trajectory accuracy significantly, which lays a solid foundation for further medical applications such as needle manipulation and laser ablation in an MR environment.

Needle puncture is a fundamental technique in minimally invasive surgical procedures. However, the limited flexibility of flexible needles and their complex interactions with tissues make it challenging to avoid critical organs along the puncture path. Preoperative path planning, which generates feasible collision-free trajectories, can effectively reduce repeated punctures and mitigate patient discomfort. To address this challenge, a flexible needle with increased maximum curvature is designed, which introduces more complex kinematic characteristics and poses greater challenges for trajectory planning under kinematic constraints. Then, for the first time, a convex feasible set (CFS)-based flexible needle trajectory planning method is developed to tackle the non-convex optimization problem posed by obstacle avoidance in unstructured surgical environments. Specifically, our method explicitly incorporates kinematic and curvature constraints, enabling direct generation of feasible trajectories without additional post-processing. Finally, comparative experiments on a self-developed robotic-assisted flexible needle system demonstrate the superior performance of the proposed algorithm. In particular, the proposed trajectory generation method allows the flexible needle to effectively avoid obstacles and accurately reach the target.

The unique tilt-servo mechanism of the tilt-rotor unmanned aerial vehicle (UAV) facilitates seamless transitions between multi-rotor and fixed-wing modes, enhancing both flexibility and maneuverability. However, traditional modeling methods, which treat each flight mode independently, fail to provide a unified dynamic representation, limiting the accurate description of aerobatic maneuvers during mode transitions. This paper introduces a novel modeling approach based on transient Computational Fluid Dynamics (CFD) to capture the aerodynamics of the transition mode, resulting in a multimodal, consistent dynamics model. This model simplifies the mathematical representation for specific tilt angles, ensuring compatibility with both multi-rotor and fixed-wing dynamics, and accurately describes aerobatic maneuvers. An autonomous feedback motion planning method, utilizing third-order Bézier curves for angular velocity planning, is applied, along with a modal switching strategy to address the limitations of traditional fixed-wing UAVs. The feasibility of this method was validated through numerical simulations, hardware-in-the-loop simulations, and outdoor flight experiments of a tilt-rotor UAV performing the Cobra maneuver in transition mode.

Aerial robots have demonstrated significant potential in suspended cargo transportation, especially in industries such as logistics and food delivery. Due to the underactuated and nonlinear dynamics of the cable-suspended system, directly tracking a given trajectory with a multicopter without modifying its controller often leads to significant payload swing. This compromises the safety and stability of the cargo. To address the aforementioned issue, this paper proposes an online trajectory refinement method for a variable-length cable-suspended aerial transportation robot, independent from the control layer. By incorporating payload swing angle information, the reference trajectory is refined in real-time, effectively suppressing payload oscillations during transportation. Specially, Lyapunov techniques and LaSalle’s invariance theorem are employed to rigorously guarantee the feasibility of the designed trajectory refinement scheme. Finally, hardware experiments are conducted to validate the effectiveness and superiority of the proposed method. The results demonstrate that the refined trajectory not only enables precise positioning of the multicopter, but also effectively suppresses payload oscillations during transportation, significantly enhancing the safety and reliability of the aerial cargo delivery.

Hand exoskeletons can recognize user’s intent and provide active resistance training to enhance finger strength in stroke patients. However, achieving fine human-robot interaction (HRI) while maintaining system simplicity for lightweight design remains a key challenge. In this work, we present an improved flexible hand exoskeleton with series elastic actuator (SEA) for hand strength estimation and progressive resistance exercise. The SEA design allows the hand exoskeleton to have backdrivability to improve HRI performance. By combining the flexible linkage with the flex sensor, we propose a novel user interface that is able to sensitively acquire hand motion intent. An Extended Kalman Filter (EKF) based tracking errors estimation is designed to evaluate the finger strength. The results of the finger strength estimation are used to adjust the parameters of the admittance model to provide small or large damping when the user’s finger strength is low or high, achieving active admittance control based progressive resistance exercise. The feasibility has been demonstrated by two sets of experiments, and this work has established a hand exoskeleton solution for finger strength estimation and fine human-robot interaction.

Open-environment machine learning is crucial for category discovery in synthetic aperture radar automatic target recognition (SAR ATR). However, SAR ATR toward intelligent applications requires addressing not only open-world distributions but also data imbalance. In this letter, we first propose the distribution-unknown generalized category discovery (DUGCD) problem and introduce the balanced knowledge distillation-contrastive learning (BKD-CL) framework, which includes the frequency attention ViT (FAViT) module and a multilayer perceptron (MLP) projection head. Second, we optimize the loss function using both supervised and self-supervised contrastive learning methods to learn feature representations from labeled and unlabeled data. We also implement self-distillation and entropy regularization to facilitate knowledge training for a parameterized classifier aimed at classification learning. Finally, to tackle the issue of data imbalance, we introduce balanced knowledge distillation, which selectively transfers knowledge using weighted coefficients to address the poor recognition performance caused by imbalanced data distributions. Extensive experiments conducted on the MSTAR dataset demonstrate the superiority of our proposed method.

In robotic orthopedic surgery, the optical tracking system (OTS) is typically placed in a fixed location. In surgery, the OTS’s line of sight is likely to be blocked. This will interrupt the navigation and affect surgical safety. To solve this occlusion problem, an RGB-D camera is used to detect possible occluders and a navigation robot is utilized to actively adjust the OTS viewpoint before occlusion occurs. To guarantee the applicability, the occluder is enveloped using a convex polytope, and a two-phase optimization method is proposed based on duality of convex optimization. The effectiveness of the proposed method is verified via simulations and experiments. Experimental results show that the proposed method can avoid occlusion between the OTS and the occluder, and the targets are located near the center of the measurement volume. This active navigation guarantees the continuity of intraoperative navigation, and thus helps to improve the safety in robotic orthopedic surgery.

The unmanned aerial manipulator system has shown great application potential in rotor blade repairing, bridge inspection, and goods delivery. Allowing additional alternatives in tasks, dual-arm always provides more flexibility, versatility, and manipulability compared to a single arm. Unfortunately, the inherent defects of unignorable nonlinearities and complex dynamic coupling between the multirotor and the manipulator have limited the practical application of dual-arm aerial manipulator systems. It is noteworthy that the dynamic coupling between the multirotor UAV (unmanned aerial vehicle) and the dual-arm manipulator is much more complicated than the case of the single-arm, and may degrade the control performance significantly as the CoM (center of mass) of the system changes with the movement of the manipulator. To this end, this paper presents a novel control method based on dual-arm movement compensation. Specifically, the kinematic and dynamic model of the system is first established, based on which the force effect of the manipulator exerting on the multirotor UAV is estimated by the disturbance observer and then compensated. By using Lyapunov techniques, it is proven that the error signal can converge asymptotically. As far as we know, this paper presents the first controller design for dual-arm aerial manipulator systems with rigorous stability analysis. Finally, the effectiveness and robustness of the proposed method are verified through a significant number of comparison experiments, and the results of these experiments demonstrate that the proposed method can reduce the positioning error obviously compared to the comparison methods. Taking the PID method as the benchmark for comparison, it is obvious that the proposed method exhibits the greatest reduction in both maximum and average errors compared to the baseline method, indicating superior control precision than the other comparison methods. For the result of the proposed method in$\bm x$-direction, one can find a substantial reduction ranging from 16.69% to 38.57% for the maximum error, and 22.24% to 45.66% for the mean error. Shifting focus to the$\bm y$-direction, the error reduction for the proposed method is even more remarkable, ranging from 68.10% to 81.80% at maximum, and 66.31% to 86.33% for the mean error. As for the$\bm z$-direction, the error reduction by the proposed method remained significant, ranging from 50.67% to 86.38% at maximum, and with a mean error reduction of 33.11% to 80.39%.Note to Practitioners—This paper is motivated by the problem of executing such tasks as load transportation and coordinate manipulation for aerial robots in flight. By integrating the dual-arm manipulator, the flexibility, versatility, and manipulability of the unmanned aerial manipulator system is further extended. However, the uncertain center of mass of the system during operation may badly increase the control difficulty of the dual-arm aerial manipulator system. Moreover, the strong nonlinearity and complex coupling existing between the multirotor and the manipulator also induce urgently solved problems in practical aerial manipulation tasks. To this end, this paper proposes a novel dual-arm movement compensation based control scheme by utilizing an elaborately designed disturbance observer to deal with the unestimated part of disturbance exerting on the multirotor by arm operation. With rigorous theoretical analysis, the convergence of the error signal is proven. Additionally, groups of hardware experiments further verify the effectiveness and robustness of the suggested control method. In future studies, we will improve the autonomy level of the system by integrating onboard sensors.

Aerial manipulator (AM) systems are significantly more effective than both conventional manipulators and flying robots, especially in disaster rescue settings, because they can perform human-like interaction tasks in flight. However, controlling the contact force of an AM is difficult due to the coupling between its flying platform and manipulator. This paper proposes a contact force control framework to address this problem. First, the UAV/AM position response under an external force is studied, and it is theoretically shown that a closed-loop UAV/AM behaves as a spring-mass-damper system. Second, a contact force controller is designed using an inverse-dynamics method. Third, an attitude feed-forward approach is employed to improve the force tracking performance. Then, the real-time contact position is introduced into the control system to achieve interaction with an actively moving environment. Finally, an innovative AM is developed and subjected to flight experiments, validating the proposed framework. An AM’s characteristics in interaction operations are summarized, and general conclusions are drawn. This study is novel in that 1) the contact force control is implemented without relying on force sensors, 2) the whole framework is applicable for controlling a constant/variable contact force with high closed-loop performance, and 3) it can also perform reliable interaction with a dynamic and unknown environment.Note to Practitioners—This study is motivated by the contact force control problem of an aerial manipulator (AM) during interaction operations. Previous approaches have explored the feasibility of controlling contact forces to some extent, but they lack universality and assume a static environment. Ensuring sufficient safety and providing reliable solutions in real-world applications remain challenging. This paper aims to investigate the position response of a closed-loop aircraft system under external forces, without disrupting its existing steady flight. The proposed method transforms contact force control into position control, eliminating the need for a force sensor. A series of aerial experiments were conducted to validate the effectiveness and applicability of the method in various scenarios. Our ongoing work will focus on migrating such an AM system from a laboratory scenario to the real world.

Hysteresis is a typical nonlinear characteristic that exists in mechanical systems, which brings significant challenges to the robust tracking control of twisted tendon-sheath systems. In this paper, an active data-driven model is proposed to describe the hysteresis phenomenon of a twisted tendon-sheath system based on the Koopman operator, and a robust controller is designed to cancel the effect of the model error and deal with the physical constraints in practical applications. First, by utilizing the Koopman theory, an active data-driven model is built to describe the twisted tendon-sheath hysteresis system in a straightforward linear form. Then, an active model is proposed based on a modified set-membership filter to estimate the finite-dimensional approximation error. Furthermore, a robust controller is developed by taking advantage of both the magnitude and bound of the model error (obtained by the active model) to enhance the control performance while considering security constraints. To the best of our knowledge, the rule-based constraint term is first considered in the data-driven model-based control scheme to prevent potential instabilities for the twisted tendon-sheath system. The theoretical stability of the closed-loop system is proven by using the barrier Lyapunov theory to ensure the security boundary. Extensive experiments are also carried out on a self-built robotic ureteroscopy prototype to demonstrate the superior tracking performance and robustness of the proposed method. Note to Practitioners—This paper is motivated by the accurate transmission problems of twisted tendon-sheath hysteresis systems, which aims to provide a precise active modeling method and a robust controller for the robotic-assisted instrument (e.g., endoscope, catheter, etc.) twisting in the sheath/orifice. Most existing studies on tendon-sheath hysteresis systems realize trajectory tracking controllers by using parametric-model-based compensation, which still lacks a practical data-driven modeling approach to characterize the hysteresis phenomenon in the linear form, and ignore the security constraints of tendon outputs. Based on the set-membership filter, this paper builds an active Koopman-based model, which is a practical method to follow for systems characterized by complex dynamics. Subsequently, by employing the constructed active model and a rule-based term to handle output constraints, a robust controller is elaborately designed to realize accurate tracking control for twisted tendon-sheath hysteresis systems. In particular, no priori knowledge of the complex dynamics is required in the implementation and gains selection of the proposed controller, which holds theoretically and practically significance for various tendon-sheath hysteresis systems. A series of comparative hardware experiments further validate the effectiveness and robustness of the suggested control scheme. In future work, we will aim to extend the applicability of the proposed active modeling and control scheme to interventional procedures of endoscopic operation robots for complex steerings with varying sheath configurations.

By utilizing a suspension cable to connect the payload with the quadrotor, transport tasks can be accomplished while preserving the unmanned aerial vehicle’s agility and maneuverability, particularly in environments that are impassable for ground vehicles. Equipping onboard visual sensors and utilizing image-based visual servoing techniques, the application range of aerial transportation systems is poised to be significantly expanded in scenarios like autonomous landing and goods release. Unfortunately, within the system, there exist multiple layers of dynamic couplings between image features, quadrotor rotation, translation, and payload motion. These intricacies give rise to numerous difficulties in achieving smooth anti-swing transportation. To overcome the aforementioned difficulties, this paper presents the first image-based visual servoing control scheme for the aerial transportation system with variable-length cable. Specifically, the image moments defined on the rotated virtual image plane are taken as the image features, whose dynamics is independent of the quadrotor rotational motion. Subsequently, a generalized virtual image feature signal is introduced by organically combining the cable length and payload swing angles with the image feature, which is further exploited in the anti-swing control scheme design. The equilibrium point of the overall closed-loop system is proved to be asymptotically stable through Lyapunov techniques and LaSalle’s Invariance Theorem. Hardware experiments are conducted on a self-built aerial transportation platform to verify the proposed controller’s basic and functional performance in terms of rapid anti-swing and accurate target position and cable length tracking. Note to Practitioners—This paper is motivated by the requirement to improve the autonomy level and payload swing suppression ability of the aerial transportation system through visual servoing techniques. By installing onboard monocular camera and the cable length adjustment mechanism, the application scope of the aerial transportation system can be significantly expanded. However, due to the “double” underactuated characteristic, the visual features couple with both the quadrotor motion and the payload motion, hence, it is quite challenging to realize visual servoing control for cable-suspended aerial transportation systems with simultaneous payload swing suppression and quadrotor positioning. Accounting for the foregoing problems, this paper proposes an image-based visual servoing anti-swing control scheme. With the elaborately constructed generalized virtual image feature signal, the designed controller could improve the anti-swing ability with a completed theoretical analysis. Furthermore, two groups of hardware experiments are conducted to validate the effectiveness of the suggested control method. In future studies, we intend to design more effective control scheme for payload delivery issue with consideration of the visibility of the mobile platform.

As a low-cost cargo delivery manner, cable-suspended aerial transportation system is highly regarded by researchers. However, existing works seldom consider the relative distance adjustment between the payload and the multirotor, which greatly limits the application scope, such as tunnel traversing or payload releasing. In addition, treating the hook and the payload as a single point mass while ignoring the hook effect results in an inaccurate description of the dynamic model. To address the aforementioned problems, the dynamic model of the variable-length cable-suspended aerial transportation system is established accurately through Lagrange's equation with consideration of the motion of the multirotor, the payload, and the hook. Subsequently, an adaptive control method is presented through energy-based analysis, and swing angle related fuzzy rules are established to dynamically adjust the control parameters, which can simultaneously achieve multirotor positioning, payload hoisting/lowering, and hook/payload swing suppression. Moreover, the cable length is constrained within a feasible range by an elaborately designed auxiliary control signal. Lyapunov techniques and LaSalle's invariance theorem are utilized to prove the asymptotic convergence of the closed-loop system. Finally, a series of simulations are conducted to verify the control performance of the designed method.

With excellent maneuver performance and flexibility, quadrotor unmanned aerial vehicles (UAVs) are widely used in aerial transportation. However, the aerial transportation system with dual quadrotors exhibits high degrees of freedom, strong nonlinearities, and complex state couplings, which makes it more difficult to realize simultaneous quadrotor positioning and cargo swing suppression. Compared with the traditional description of cargo swing dynamics with four angles in the previous work, the spatial swing angle is introduced in a more intuitive way to reflect the swing dynamics of the cargo. On this basis, the dynamic model of the system is established according to Lagrange's equations. Then, a nonlinear adaptive controller is proposed, in which a dynamic compensation term is introduced to compensate for the lateral forces along the cables, and a spatial swing angle-related term is designed to enhance cargo swing damping. Meanwhile, considering the influence of unknown air resistance on quadrotors and cargo during transportation, an adaptive term is applied. Subsequently, Lyapunov techniques and LaSalle's invariance principle are used to prove the stability of the closed-loop system. Finally, based on the self-built general experimental platform, both indoor and outdoor experiments have been carried out to validate the practicability and effectiveness of the proposed method.

Autonomous, accurate, and dynamic 3-D reconstruction for wide-area environments is crucial for unmanned aerial vehicle monitoring and rescue tasks, however, when conducted in an unknown complex terrain, the reconstruction result obtained from a single flight suffers poor quality. In this article, we present an Active Iterative Optimization framework for trajectory planning and visual reconstruction. Firstly, the trajectory is planned under the photogrammetric constraints based on rough terrain. Due to the visual field deviation caused by pose error during actual flight, the view loss evaluation is established and keyframes are selected to conduct 3-D reconstruction. A comprehensive metric is designed to quantitatively evaluate reconstruction effect without ground truth. The point cloud is then rasterized and divided into normal or low-scoring region according to the evaluation metric. In the next iteration, trajectory is replanned in low-scoring region to purposefully optimize the point cloud of local area. Thus the reconstruction result can be iteratively optimized. We validated the effectiveness of the proposed framework in simulation and physical experiments.

In the realm of robotics, the quest for achieving real-world autonomy, capable of executing large-scale and long-term operations, has positioned place recognition (PR) as a cornerstone technology. Despite the PR community's remarkable strides over the past two decades, garnering attention from fields like computer vision and robotics, the development of PR methods that sufficiently support real-world robotic systems remains a challenge. This article aims to bridge this gap by highlighting the crucial role of PR within the framework of simultaneous localization and mapping 2.0. This new phase in robotic navigation calls for scalable, adaptable, and efficient PR solutions by integrating advanced artificial intelligence technologies. For this goal, we provide a comprehensive review of the current state-of-the-art advancements in PR, alongside the remaining challenges, and underscore its broad applications in robotics. This article begins with an exploration of PR's formulation and key research challenges. We extensively review literature, focusing on related methods on place representation and solutions to various PR challenges. Applications showcasing PR's potential in robotics, key PR datasets, and open-source libraries are discussed.

In this article, we introduceiLoc, an innovative visual localization system designed to enhance the autonomy and adaptability of robotic agents in long-term and large-scale applications.iLocspecializes in: 1) extracting stable and consistent descriptors for place recognition, unaffected by changes in viewpoint and illumination; 2) performing swift and precise global relocalization to establish a robot's position within a large and complex environment; and 3) generating real-time tracking trajectories aligned with reference maps, ensuring continual orientation within known spaces. Distinctively,iLocincorporates a transformer-based learning module and an attention-enhanced recognition approach, enabling it to adapt to diverse environmental and viewpoint conditions.iLocleverages a coarse-to-fine global feature matching technique for enhanced localization and integrates robust state estimation combining visual odometry and loop closures through local refinement and pose graph optimization.iLocdemonstrates remarkable proficiency in place recognition, achieving localization over distances of up to 2 km within 0.5 s with average accuracy at 1 m. It maintains stable localization accuracy, even under variable conditions. Its versatile design allows integration across various environments, significantly broadening the scope of universal localization capabilities in robotics.iLocrepresents a substantial step forward in visual-based localization systems, delivering unparalleled speed and accuracy in place recognition. Its ability to adapt and respond to diverse environmental stimuli marks it as a crucial tool in advancing the field of robotic localization.

Hysteresis has posed significant challenges to the modeling and control of flexible endoscopic robots, which impedes the advancement of automated endoscopic operation. Despite numerous hysteresis modeling approaches aimed at improving accuracy, there are still several unresolved issues, such as inappropriate model selection and non-ideal assumption of noise. Focusing on these challenges, a novel reinforcement active modeling (RAM) scheme is proposed in this paper. By incorporating reinforcement learning, this method augments an Extended Kalman Filter (EKF)-based active modeling strategy, which improves the insensitivity and generalization ability to non-Gaussian noise that is not introduced in training. Finally, a series of comparative experiments are conducted on the self-built flexible endoscopic robot to validate the improvement achieved by the proposed scheme. Compared with some widely-applied methods, the proposed scheme achieved at least 63.8% improvement in the root mean square error (RMSE) in modeling accuracy under Gaussian noise conditions, and at least 36.5% improvement in RMSE under Poisson noise conditions.

The development of powered lower-limb pros-theses and exoskeletons (LLPE) for assisting individuals in activities of daily living has been gaining increasing interest in the robotic community. To assist wearers walking on various terrains in daily environments, accurate gait-mode recognition and seamless transition of control strategies are crucial for these devices. Due to the high diversity of terrains, such capabilities are usually subject to terrain conditions, making terrain detection an essential issue for LLPE control. In the paper, we proposed a method for terrain detection and modeling aimed at reconstructing terrains rather than merely classifying them to provide richer information for LLPE control, including online 2D terrain information and the relative foot position. The implementation of the proposed method relies on a sensor group consisting of a low-cost single-point laser sensor and two inertial measurement units (IMU) to simultaneously and continuously capture lower limb kinematic features and terrain features. A time-varying Kalman filter is employed to fuse these features, facilitating rapid and accurate modeling of different terrains such as level ground, stairs ascend/descend, and ramp ascend/descend. The performance of the proposed method was evaluated via experiments with two healthy subjects. The results show that the reconstructed terrains can provide accurate information for LLPE control, demonstrating the effectiveness and adaptability of the proposed method.

Intraoperative optical navigation is widely utilized in robotic surgery systems. Typically, the observation pose of the optical tracking system (OTS) is manually adjusted and then fixed throughout the surgery. However, fixed OTS suffers from limited measurement volume (MV) and visual interferences, making consistent navigation challenging in clinics. In this paper, an operation-navigation dual-robot collaborative system is proposed for orthopedic surgeries. An extra navigation robot is introduced to actively adjust the observation pose of the OTS. A collaborative preoperative planning method is proposed for this dual-robot system, including osteotomy path planning of the operation robot and collaborative planning of the navigation robot. Firstly, osteotomy paths of the operation robot are generated according to the surgery regulations and the geometric features of the vertebral foramen. Secondly, based on the generated osteotomy paths, the collaborative planning of the navigation robot is formulated into a multi-objective optimization problem to find the optimal poses of the OTS for each osteotomy plane. Compared with fixed OTS, active navigation is capable of keeping all the targets within the MV of the OTS throughout the surgery. Semi-laminectomy on a human spine phantom is adopted as an example to experimentally evaluate the effectiveness of the proposed method.Note to Practitioners—As the demand for robot-assisted surgery is increasing, the precision of operation has become one of the key safety requirements. Preoperative planning provides guidance for the surgeon, and intraoperative navigation monitor the status of the lesion and the surgical tool in real-time. In conventional intraoperative optical navigation, the OTS is manually adjusted and remains stationary. However, the limited MV and the visual interferences introduce risks and uncertainties to the surgical system. In order to address the limitations of fixed OTS, an operation-navigation dual-robot collaborative system is proposed for orthopedic surgeries, which is composed of a surgical operation module and an active navigation module. The navigation robot is used to actively adjust the pose of the OTS. A collaborative preoperative planning for the operation-navigation dual-robot orthopedic surgery system is proposed in this paper. The effectiveness of the proposed method is verified on a human spine phantom. Experimental results show that the active navigation provides more freedom to the overall system by freely adjusting the OTS, which ensures the stability of the surgical navigation. In future work, efforts will be directed toward the identification and avoidance of the obstacle.

Pneumatic artificial muscle (PAM), featuring good flexibility and safety, has been widely used in rehabilitation and bionic robots. However, the complex hysteretic nonlinearities and uncertainties of the PAM cause great difficulties and challenges to the accurate modeling and controller design, especially when confronted with unknown external disturbances in applications. This paper proposes a robust control strategy with disturbance compensation for the hysteresis compensation and trajectory tracking of PAMs. Considering the high hysteretic nonlinearity of the PAM, a modified Prandtl-Ishlinskii model is used as a feedforward hysteresis compensator. For the linearized system, adaptive set-membership filtering (ASMF) is used to estimate the nonlinear terms and external disturbances of the overall system. A sliding mode controller (SMC) with disturbance compensation is designed and cascaded to the feedforward hysteresis compensator in series. The stability of the closed-loop system is theoretically proved. The proposed method guarantees that the tracking error of the PAM system is bounded. Finally, the effectiveness and robustness of the proposed controller are verified via a series of experiments on an in-house built testbench for PAMs. Note to Practitioners—With the increasing demand on human-robot interaction, the safety and compliance of robots have become one key requirement. PAM is a compliant actuator, exhibiting good flexibility, safety, and clean energy. PAM is widely used in rehabilitation robots, whereas its strong hysteresis nonlinearity and sensitivity to external disturbances affect its motion accuracy. This paper proposes an ASMF-based discrete SMC, which uses an inverse hysteresis model to compensate for the strong hysteresis of the PAM and uses ASMF to estimate the lumped disturbance of the system. Compared with the other filters, ASMF is unique in that its estimation error is bounded, which is very useful in the stability proof of the overall system. The effectiveness of the proposed controller is experimentally verified. Experimental results show that PAM’s hysteresis can be efficiently compensated, and the influence of external disturbances can be attenuated by the proposed controller, resulting in improved motion accuracy and robustness. In future work, efforts will be directed towards the modeling and control of PAMs in multi-DOF robots.

Recently, it is becoming increasingly possible to apply aerial transportation systems to real-world applications. However, current research works on cable-suspended transportation systems present practical limitations due to the fixed-length cable. With the introduction of the cable adjustment mechanism, various complicated tasks, such as limited space crossing, offshore sample collection, and even landing the payload on a mobile platform, can be accomplished by actively changing the distance between the quadrotor and the payload. In order to complete the aforementioned tasks, a trajectory tracking control method is in urgent need for the variable-length-cable-suspended aerial transportation systems. To this end, an adaptive tracking control approach with the consideration of unknown resistance coefficients is designed in this article. Subsequently, Lyapunov techniques and Barbalat's Lemma are utilized to prove the convergence for the equilibrium point of the closed-loop system. Finally, hardware experiments are meticulously conducted based on a self-built experimental platform, which verify the satisfactory performance of the proposed method in antiswing aerial transportation and payload landing onto the mobile platform.

This paper studies a novel application of an aerial manipulator (AM)-the contact-based landing on a mobile platform. An AM is inherently unstable, under-actuated, and usually loses some DOFs while contacting environments. Meanwhile, the AM's flight state is susceptible to uncertain movements of the mobile platform, such as acceleration, sudden stopping, and reversing. To accomplish the contact-based landing mission, a robust controller is first designed to maintain a steady contact-based flight. Then a hierarchical control framework is applied, integrating the controllers in free-flight and restricted-flight stages. An AM and a mobile platform are developed for contact-based flight experiments. The proposed scheme is reliable and has good repeatability in experiments. To the best of our knowledge, this is the first time an AM has been implemented to conduct a contact-based landing, which is also an innovative landing approach for rotorcraft UAVs.

This article proposes a framework for multidifferent-target search in unknown environments based on swarm intelligence. In this framework, the idea of distributed model predictive control is introduced in the target search method. The use of a hierarchical prediction strategy further improves the robot’s path prediction ability in unknown environments. Compared with swarm intelligence methods—adaptive robotic particle swarm optimization (A-RPSO), improved group explosion strategy (IGES), and other existing works, this strategy significantly improves the multidifferent-target search functionality and the task success rate in unknown complex obstacle environments. Moreover, two effective efforts are then introduced to reduce computational complexity and speed up online decision making. One is to select cooperative individuals based on the line of sight, and the other is to reduce both the frequency of decision making and the amount of data transmitted. A comparison between obstacle-free map experiments and obstacle map experiments confirms the effectiveness of the ideas and methods presented in this article.

The robotic flexible endoscope is developed rapidly in the field of surgery robots due to its high flexibility and safety. However, some inherent features, e.g., high nonlinearity, material creep, complex dynamic hysteresis behaviors, and the unknown coupling effects between bending and twisting motions, can lead to the significant degradation on three-dimensional (3-D) positioning performance of the endoscope. Aiming at these challenges, this paper built a practical multi-motion hysteresis phenomenon model for the bending and twisting motions of the robotic flexible endoscope with consideration of the coupling effects. Then, the time-optimal synchronous terminal motion planner is first proposed for the 3-D motions of the robotic endoscope to decouple the coupling effects in an intuitive separate control scheme. Finally, a series of hardware experiments are conducted on a robotic flexible ureteroscope platform. The accuracy of the proposed model and the trajectory-planning-based decoupling strategy is comprehensively validated. Particularly, the experimental results with the proposed trajectory planner show the satisfactory performance of vibration suppression and over-shoot suppression.

Functional near-infrared spectroscopy (fNIRS) classification of mental states is of important significance in many neuroscience and clinical applications. Existing classification algorithms use all signal-collected brain regions as a whole, and brain sub-region contributions have not been well investigated. This paper proposes a functional region decomposition (FRD) method to incorporate brain sub-region contributions and enhance fNIRS classification of mental states. Specifically, the method iteratively decomposes the brain region into multiple sub-regions to maximize their contributions with respect to the validation accuracy and coverage of brain sub-regions. Then for the fNIRS data in brain sub-regions, features are extracted and classified to output the predictions. The final predictions are determined by fusing predictions from multiple brain sub-regions with stacking. Experiments on a publicly available fNIRS dataset showed that the proposed functional region decomposition method led to 9.01% and 10.58% increase of classification accuracy for the methods related to slope-based features and mean concentration change features, respectively. Therefore, the proposed method can decompose the brain region into sub-regions with respect to their functional contributions and fundamentally enhance the performance of mental state classification.

In sEMG-based recognition systems, accuracy is severely worsened by disturbances, such as electrode shifts by doffing/donning. Traditional recognition models are fixed or static, with limited abilities to work in the presence of the disturbances. In this paper, a transfer learning method is proposed to reduce the impact of electrode shifts. In the proposed method, a novel activation angle is introduced to locate electrodes within a polar coordinate system. An adaptive transformation is utilized to correct electrode-shifted sEMG samples. The transformation is based on estimated shifts relative to the initial position. The experiments acquisition data from ten subjects consist of sEMG signals under eight gestures in seven or nine arbitrary positions, and recorded shifts from a 3D-printed annular ruler. In our extensive experiments, the errors between recorded shifts (as the reference) and estimated shifts is about -0.017±0.13 radians. Eight gestures recognition results have shown an average accuracy around 79.32%, which represents a significant improvement over the 35.72% ( ) average accuracy of results obtained using nonadaptive models, and 60.99% ( ) results of the other method iGLCM (an improved gray-level co-occurrence matrix). More importantly, by only using one-label samples, the proposed method updates the pre-trained model in an initial position. As a result, the pre-trained model can be adaptively corrected to recognize eight-label gestures in arbitrarily rotary positions. It is proven a highly efficient way to relieve subjects' re-training burden of sEMG-based rehabilitation systems.

Glass curtain walls have been widely used in modern architecture. This makes it urgent to inspect and clean these glasses at regular intervals. Up to now, most of these work is performed by workers, which is expensive and inefficient. Therefore, a novel robot-the contact aerial manipulator system-is developed. The new designed system presents priorities in the aspects of high flexibility and easy operation. In this paper, the system mechanical structure is first introduced. Subsequently, the hybrid force/motion control framework is utilized to realize the precise and steady motion on the two-dimensional plane and maintain a certain sustained contact force, simultaneously. Finally, two flight experiments (including continuous square-wave trajectory tracking and aerial drawing task) are performed and the results indicate that the developed contact aerial manipulator works and presents good performance.

Contact-based operation in moving process is a challenging problem for aerial manipulators. It requires the whole system to maintain steady contact with external environment, to track some predefined trajectories on surfaces, and simultaneously to present some fixed contact force. Aiming at this problem, a hybrid force/motion control framework is proposed in this paper. In this framework, contact force control and position control are performed separately in two orthogonal subspaces: constrained space and free-flight space. To control the contact force, the closed-loop unmanned aerial vehicle is first theoretically shown to behave dynamically as a spring-mass-damper system. Further, an inverse-dynamics-based controller is proposed. To control the moving along the contact surface, trajectory planning and position controller are combined to achieve the steady behavior in a free-flight subspace. In the end, an aerial manipulator system with a roller-type end-effector was designed, and practical flight experiments was performed. The results indicate that the proposed framework is effective and validity.

Grasping a moving target has been investigated extensively for fixed-base manipulator. However, such a task becomes much more challenging when the manipulator is free flying in the air with an UAV. Towards moving target grasping, this paper presents an aerial manipulator system composed of a hex-rotor and a 7-DoF (Degree of Freedom) manipulator. An independent control structure is used in the aerial manipulator control system, i.e., the hex-rotor and the manipulator are controlled separately. In the hex-rotor's controller, the system CoM (Center of Mass) offset motion is used to compensate disturbance of the robotic arm. In the manipulator's controller, the relative kinematics between the target and the aerial vehicle is taken into consideration to grasp the target. At last aerial grasping experiments are conducted to validate the feasibility of the proposed control scheme and the reliability of our aerial manipulator system.

The dangerous work situation in industrial leakage accidents urgently needs a flexible and small robot to help workers perform operations and to protect them from being injured. An aerial manipulator system consisting of a hexa-rotor UAV and a one-DOF manipulator is developed, and is used to press an emergency switch to shut off machinery in an emergency. In practical application, an aerial manipulator usually performs contact operations as the UAV platform is in hover flight. The hovering UAV acting as a spring-mass-damper system is firstly proved. Then, based on the derived spring-mass-damper system model and the impedance control algorithm, the force-sensorless contact force control method is presented. That is, the force is indirectly controlled through controlling the UAV's position error and pitch angle simultaneously. The practical operation experiment of pressing an emergency button shows that the proposed method is able to control the contact force as the aerial manipulator interacts with the external environment.

Traditional rehabilitation techniques have limited effects on the recovery of patients with tetraplegia. A brain–computer interface (BCI) provides an interactive channel that does not depend on the normal output of peripheral nerves and muscles. In this paper, an integrated framework of a noninvasive electroencephalogram (EEG)-based BCI with a noninvasive functional electrical stimulation (FES) is established, which can potentially enable the upper limbs to achieve more effective motor rehabilitation. The EEG signals based on steady-state visual evoked potential are used in the BCI. Their frequency domain characteristics identified by the pattern recognition method are utilized to recognize intentions of five subjects with average accuracy of 73.9%. Furthermore the movement intentions are transformed into instructions to trigger FES, which is controlled with iterative learning control method, to stimulate the relevant muscles of upper limbs tracking desired velocity and position. It is a useful technology with potential to restore, reinforce or replace lost motor function of patients with neurological injuries. Experiments with five healthy subjects demonstrate the feasibility of BCI integrated with upper extremity FES toward improved function restoration for an individual with upper limb disabilities, especially for patients with tetraplegia.

In the 2nd International Unmanned Aerial Vehicle (UAV) Innovation Grand Prix (UAVGP) sponsored by Aviation Industry Corporation of China (AVIC), an autonomous barrel transportation mission was proposed for Rotary-wing UAV (RUAV) which aims at validating the feasibility of marine vertical replenishment by RUAVs. The RUAV was supposed to track the movable platform, pick up barrels on the supplier platform, stack barrels on the replenishment platform, take off and land autonomously without any human interference. The final score was determined based on the mission completion time and the stacking accuracy. The main challenges involved in this mission include attitude stabilizing with varying payload, accurate movable platform tracking and task-scheduling. An unmanned helicopter (UH) automatic barrels transportation system was established by our team, and the navigation system, control system and task scheduling system will be detailed in this paper, along with the flight tests before and in the competition. The system's performance has been verified in the competition and won the first prize finally.

Reliable mapping and hazard detection are prerequisites for autonomous navigation for unmanned ground vehicles. Because of the uncertainty and vibration induced by high-speed navigation and rugged terrain, the problem of mapping for high-speed off-road autonomous navigation has not been completely solved yet. A relative probabilistic mapping (RPM) algorithm is introduced to address the problem. Firstly, the relative probabilistic map is updated by Kalman filter and Gaussian Mixture algorithm based on the probabilistic exteroceptive measurements model. Then, terrain traversability is evaluated to identify obstacles in the map. Experiments on off-road high-speed autonomous vehicle, which suffers from severe vibration, with different sensor configurations are carried out to demonstrate the capability of the RPM algorithm.

sEMG recognition has been used extensively in prosthetic device control, human-assisting manipulators and sign language recognition, etc. However, the sEMG recognition model, trained with one subject's sEMG data, is not applicable to the other subjects, which hinders the practical application of myoelectric interfaces immensely. In this paper, a sEMG recognition method which is applicable to multi-users is proposed. Firstly, single channel sEMG is decomposed into 30 MUAPTs, which includes four steps: two-order differential filter, threshold calculation, spike detection and hierarchical clustering. Secondly, the MUAPTs are updated with the templates orthogonalization; and Deep Boltzman Machine is employed to classify the MUAPTs into five classes corresponding to the predefined five gestures. Six participants participated in this experiment to validate the effectiveness of the proposed method. Results indicated that this method can achieve a mean accuracy of 81.5%.

This paper considers the problem of generating dynamically feasible and collision free trajectory for unmanned aerial vehicles(UAVs) in cluttered environments. General random-based searching algorithms output piecewise linear paths, which cause big discrepancy when used as navigation reference for UAVs with high speed. Meanwhile, the disturbance may also occur to lead the UAVs into danger. In order to obtain agile autonomy without potential dangers, this paper introduces a three-step method to generate feasible reference. In the first step, a six-order Bezier curve, which uses Tuning Rotation to decrease the curvature, is introduced to smooth the output of the path planner. Then a forward simulation is implemented to find the potential dangerous regions. Finally, the path is reshaped by local optimal reshaping planner to eliminate residual dangers. The three steps form a circulation, the reshaped path sent to the first step again to check dynamic feasibility and safety. The method combining Six-order Bezier curve, Tuning Rotation, and local optimal reshaping is proposed by us for the first time, where the Tuning Rotation is able to meet various curvature requirements without violating the previous path, local optimal reshaping obtains both temporal and spatial reshaping with high time efficiency. The method addresses the system dynamics to achieve agile autonomy, which provides the geometry reference as well as the low level control. The effectiveness of the proposed method is demonstrated by the simulations.

To generate local trajectory between initial states and target states for autonomous vehicles, a feasible trajectory generation algorithm based on quartic Bézier curve is proposed. The problem of trajectory generation is firstly separated into generating continuous and bounded curvature profile to shape the trajectory and generating linear velocity profile to execute the trajectory. The curvature profile generation is further converted to an optimization problem with only 3 parameters owing to the specific properties of quartic Bézier curve. Sequential quadratic programming is employed to find optimal solution with respect to specific objective function. To avoid sideslip and ensure velocity-continuity and acceleration limits, the framework of linear velocity profile generation is also proposed. A simple profile with constant acceleration is also provided as an example. Simulation results on lane keeping and changing and path following demonstrate the capability and the real-time performance of the proposed algorithm.

A multifunctional myoelectric prosthetic hand is a perfect gift for an upper-limb amputee, however, the myoelectric control for a prosthetic hand is not so good now. Here, the paper presents a comparative study on electromyography (EMG) pattern recognition based on PCA and LDA for an anthropomorphic robotic hand. Four channels of surface EMG (sEMG) signals were recorded from the subject's forearm. Time-domain analysis, frequency-domain analysis, wavelet transform analysis, nonlinear entropy analysis and fractal analysis were done and fourteen kinds of features were extracted from sEMG signals. The features were divided into four groups, and the performances of the four groups were compared and analyzed. In the feature projection stage, three schemes were proposed and their performances were compared with each other. The first one only used the principal component analysis (PCA) for dimension reduction. And the second one only used the linear discriminant analysis (LDA) for dimension reduction. The third one used PCA for the first step of dimensionality reduction, and then used LDA for the next step of dimensionality reduction. In the classification stage, minimum distance classifier (MDC) was employed for identifying nine kinds of hand/wrist motions in the projected space. Comparative experiments of four groups of features and three projection schemes were done and evaluated. The online experiment of real-time myoelectric control for an anthropomorphic robotic hand was done as well.

Estimation of the joint angle from the surface electromyography (sEMG) is a quite complex task due to the complicated relationship between the kinematical variables and the raw sEMG. In this paper, we build a sEMG-to-motion model with the artificial neural network (ANN). EMG features, including Integral of absolute value (IAV), Zero crossing (ZC), Auto-regression coefficients (ARC), Median frequency (MDF), are extracted as the input of the ANN, and the output of the ANN is the operator's elbow joint angle and the wrist motion. In addition, a 3D upper extremity model, which is built in SolidWorks and then transformed into MATLAB, will imitate the operator's motion simultaneously with the estimations of the ANN. Thus, we accomplish a virtual reality system to realize the real-time simulation and validate the effectiveness of the sEMG-to-motion model. Experiment results show that the system achieves well in model accuracy, hardware compatibility and real time performance with a small mean square error of 1.921 degrees.

Flexible needle with bevel tip offers greater mobility for puncture surgery. This would expand the scope of the puncture surgery. However, motion planning for flexible needle is still a challenge due to its non-holonomic property and the complicated interactions with soft tissues. In this paper, a multilayer tissue model is constructed to simulate human tissue, and a dynamic programming is employed to plan the motion of flexible needle in the multilayer environment. In order to improve the security of the puncture process, the obstacles are fuzzed up. Then, an optimal algorithm is developed to determine a more suitable puncture angle. In addition, to deal with more complex environment, we develop a reverse algorithm to confirm the entry point in line with the target. Finally, we take some simulations to verify the proposed algorithms, and analyze the results.

Electromyography (EMG) has been widely used as control commands for prosthesis, powered exoskeletons and rehabilitative robots. In this paper, an EMG-driven state space model is developed to estimate continuous joint angular displacement and velocity, demonstrated by elbow flexion/ extension. The model combines the Hill-based muscle model with the forward dynamics of joint movement, in which kinematic variables are expressed as a function of neural activation levels. EMG features including integral of absolute value and waveform length are then extracted, and two quadratic equations which associate the kinematic variables with EMG features are constructed to represent the measurement equation. The proposed model are verified by extensively experiments, where the angular movements of human elbow joint are estimated only using the EMG signals, and the estimations are compared with the IMU measurements to validate the accuracy. As a demonstration, a robotic arm is commanded to follow the human elbow movement estimated by the proposed model, which shows the possibility of EMG-based robotic assisted rehabilitation.

For the control of unmanned helicopters in full flight envelope, an active model based control scheme is developed in this paper. An adaptive set-membership filter (ASMF) is used to online estimate both the model error due to flight mode change and its boundary, taking advantage of ASMF, so that the model error can be assumed unknown but bounded (UBB). The proposed approach is practical because the model error depends on both helicopter dynamics and flight states, and may not be assumed as white noise. An active modeling based stationary increment predictive control (AMSIPC) is also proposed based on the estimated model error and its boundary to optimally compensate the model error, as well as the aerodynamics time delay. The proposed method has been implemented on the ServoHeli-20 unmanned helicopter platform and experimentally tested, and the results have demonstrated its effectiveness.

In this paper the path planning for obstacle-avoided pursuit problem (OAP) is studied. The OAP models based on the mixed integer linear programming (MILP) is presented. In the OAP models, the dynamic equation of mass point with linear damping is taken as the state equation of vehiclepsilas motion. Integer variables are used to describe the relative position of vehicle and obstacles. ldquoExpansible Target Sizerdquo is proposed to describe the pursuit process for target step-by-step. ldquoPursuit Directionrdquo of vehicle is defined. The Isometric Plane Method selected integer variables is used to solve MILP pursuit problem. How to select the integer variables of inner point is also given. Finally, simulations are given to show the efficiency of the method.

The trajectory tracking control problem of a tracked vehicle with slipping is considered in this paper. The slipping effects are analyzed and modeled as three time-varying parameters, which can be estimated simultaneously with robotpsilas pose using nonlinear estimators such as unscented Kalman filter. Dynamic feedback linearization integrated with a globally exponential stabilizing state feedback is applied to achieve the tracking control objective. Simulation results are provided to demonstrate the effectiveness of proposed method.

A novel adaptive unscented Kalman filter (UKF) based on dual estimation structure is proposed. The filter is composed of two parallel master-slave UKFs, while the master one estimates the states and the slave one estimates the diagonal elements of the noise covariance matrix for the master UKF. By estimating the noise covariance online, the proposed method is able to compensate the errors resulting from the change of the noise statistics. Such a mechanism improves the adaptive ability of the UKF and enlarges its application scope. Simulations conducted on the dynamics of an omni-directional mobile robot indicate that the performance of the adaptive UKF is superior to the standard one in terms of fast convergence and estimation accuracy.

In this paper, we deal with the yaw control problem of a small-scale helicopter mounted on an experimental platform. The yaw dynamics of helicopter involve input nonlinearity, time-varying parameters and the couplings between main and tail rotor. An attractive control strategy that combines neural networks with traditional adaptive controls has been successfully used for yaw control with input nonlinearities. In contrast to conventional adaptation law, the sliding condition is taken as the objective function instead of the error function used in MIT rule. From the concept of the sliding mode control, the adaptive controller guarantees the stability of the closed-loop system and convergence of the output tracking error to a desired bound, even if the model parameters are unknown or in the presence of disturbance. The simulation results are further compared with those obtained by normal PID control to demonstrate the improvements of the proposed algorithm

This paper presents a new robust controller design approach to the yaw control of a small-scale helicopter mounted on an experimental platform. The yaw dynamic system is linearized into a linear system, which is modelled by an affine uncertainty model. We proposed a novel robust Hinfinfeedback controller with adaptive mechanisms for the linear system with guaranteed control performances. The feedback gains are obtained by the solutions of a series of linear matrix inequalities (LMIs). The design approach reduces conservatism inherent in robust control with a fixed gain controller and improves performances in time-response. Numerical simulations illustrate the theoretical results

Classical PD control for a direct drive motor system is enhanced to suppress disturbing torque by incorporating an acceleration feedback loop. This is intended to achieve a stable and high stiffness control without the need of adjusting the PD controller, either the structure or the parameters. The acceleration feedback control is presented with a focus on designing the acceleration closed-loop in terms of its stability and ability in resisting the dynamic disturbances. The sensing and modeling of angular acceleration via servo-type linear accelerometers is dealt with from the viewpoint of practical implementation. Extensive experiments are conducted on the second joint of a three-link direct drive robot. Results are compared to those obtained by classical PD control without the acceleration feedback loop, to mainly investigate the ability of the acceleration feedback control in resisting disturbing torque, and its influence on conventional PD control.

Joint acceleration feedback control is employed to damp oscillations during the contact transition with non-zero approaching speed. A classical integral force controller is refined by means of joint acceleration and velocity feedback. The intention is to achieve a stable contact transition without need of adjusting the controller parameters adaptive to the unknown or changing environments. Extensive experiments are conducted on the third joint of a three-link direct-drive robot to verify the proposed scheme for various stiffness of the contacted environments, including elastic (sponge), less elastic (hardboard) and hard (steel plate) surfaces. Results are compared with those experimental ones by the transition control with only velocity feedback damping. The advantages offered by our approach are addressed.