This article focuses on the vibration issue of flexible two-link manipulators (FTLMs) with input saturation. An efficient system model is represented by a set of ordinary differential equations (ODEs) based on the assumed mode method (AMM). Subsequently, a reinforcement learning (RL)-based adaptive vibration control strategy, which is a model-free control approach, is proposed by employing the actor–critic algorithm structure. Additionally, an auxiliary system is constructed to tackle the influence of input saturation, ensuring trajectory tracking while achieving vibration suppression. Furthermore, the stability of the closed-loop system under RL control is examined using the Lyapunov direct method, which demonstrates the semi-global uniform ultimate boundedness (SGUUB) of tracking and vibration errors. Finally, to verify the effectiveness and superiority of the proposed RL strategy, the comparative simulations and experimental studies are conducted on the Quanser experimental platform. The experimental results demonstrate that RL control reduces steady-state errors by 40% and 96.6% against PSF control and by 50% and 97.2% against neural network (NN) control, respectively.

Focused on the scientific issues of collision avoidance and compliant operation of physical human-robot interaction (pHRI) systems, this paper proposes a reinforcement learning (RL) strategy based on admittance control to achieve compliant collision avoidance and accurate trajectory tracking of pHRI. Firstly, a differentiable reference trajectory is generated using a soft saturation function with an admittance model. Subsequently, a reinforcement learning strategy based on an actor-critic structure is implemented to address dynamic uncertainty and enhance tracking performance and compliance. Different from existing studies, a reinforcement learning admittance controller containing an integral barrier Lyapunov function (IBLF) is constructed to attain accurate tracking while ensuring that the end-effector achieves the position constraints. Lyapunov stability theory is employed to proof that all states of the closed-loop system remain semiglobally uniformly ultimately bounded (SGUUB). Finally, a suite of tests on Baxter robot experimental platform have been conducted to validate the superiority of the proposed algorithm compared with adaptive impedance control and conventional admittance control.

This paper investigates the feasibility of robotic grasping of various objects in complex scenarios, with our method aiming to achieve grasping capabilities for any scene and any object. Firstly, a Target Edge Collision-Avoidance Strategy is proposed that systematically incorporates the edge features of grasping objects. This strategy is specifically designed to address two critical challenges: bridging the significant performance gap between offline training data and real-world operating conditions, and effectively preventing collision incidents between the robotic end-effector and target objects during physical grasping operations. Furthermore, the Grasp Detection Network based on Global and Local Information Perception (GLIP-Net) is proposed, featuring two intricately designed components: the Global Information Perception and Local Aggregation Module, and the Multi-dimensional Multi-scale Attention and Adaptive Feature Fusion Module. The GLIP-Net enhances the network’s perceptiveness to global information, strengthening the correlation between features and the spatial parameters of grasping. To validate the effectiveness of the presented method, extensive tests and grasping experiments is conducted on the Cornell Dataset and Jacquard Dataset, as well as in practical scenarios. The empirical outcomes indicate an accuracy level of 99.2% on the Cornell Dataset and 96.8% on the Jacquard Dataset, respectively. Furthermore, by employing the Kinova robot in both single-object and multi-object complex scenarios within real-world environments, the grasping success rates of 97.0% and 95.8% is achieved. Note to Practitioners—This paper was inspired by the problem of robotic object grasping in various scenarios, but it is also applicable to tasks such as object grasping, sorting, and transportation in unstructured environments. Existing robotic grasping methods are typically limited to structured scenarios, where robots can only grasp objects at fixed positions. When the object or its pose changes, the entire grasping task is likely to fail. Additionally, robots often fail to consider the edge information of the object when grasping, leading to collisions between the end effector and the object. In this paper, we propose a novel approach that employs a graping detection network to process an input color image containing depth information. The neural network takes both global and local information into account, fuses useful feature data, and adaptively outputs a set of grasp configurations. To address the issue of collisions between the end effector and the object, we design a target edge collision-avoidance strategy, prioritizing regions adjacent to the gripper side. Preliminary experiments demonstrate the feasibility of our method, which has been tested in several real-life scenarios. In future research, we aim to address the robot vision closed-loop control problem, enabling robots to perform grasping tasks in dynamic environments.

In the field of robot grasping detection, due to uncertain factors such as different shapes, distinct colors, diverse materials, and various poses, robot grasping has become very challenging. This article introduces a integrated robotic system designed to address the challenge of grasping numerous unknown objects within a scene from a set of $\alpha $ -channel images. We propose a lightweight and object-independent pixel-level generative adaptive residual depthwise separable convolutional neural network (GARDSCN) with an inference speed of around 28 ms, which can be applied to real-time grasping detection. It can effectively deal with the grasping detection of unknown objects with different shapes and poses in various scenes and overcome the limitations of current robot grasping technology. The proposed network achieves 98.88% grasp detection accuracy on the Cornell dataset and 95.23% on the Jacquard dataset. To further verify the validity, the grasping experiment is conducted on a physical robot Kinova Gen2, and the grasp success rate is 96.67% in the single-object scene and 94.10% in the multiobject cluttered scene.

This article develops a finite-dimensional dynamic model to describe a stand-alone tall building-like structure with an eccentric load by using the assumed mode method (AMM). To compensate for the dynamic uncertainties, a new neural-network (NN) control strategy is designed to suppress vibrations of the tall buildings. The output constraint on the angle of the pendulum is also considered, and such an angle can be ensured within the safety limit by incorporating a barrier Lyapunov function. The semiglobally uniform ultimate boundness (SGUUB) of the closed-loop system is proved via Lyapunov's stability. The simulation results reveal that the new NN strategy can effectively realize vibration suppression in the flexible beam and pendulum. The effectiveness of the new NN approach is further verified through the experiments on the Quanser smart structure.

The bionic flapping-wing robotic aircraft is inspired by the flight of birds or insects. This article focuses on the flexible wings of the aircraft, which has great advantages, such as being lightweight, having high flexibility, and offering low energy consumption. However, flexible wings might generate the unexpected deformation and vibration during the flying process. The vibration will degrade the flight performance, even shorten the lifespan of the aircraft. Therefore, designing an effective control method for suppressing vibrations of the flexible wings is significant in practice. The main purpose of this article is to develop an adaptive fault-tolerant control scheme for the flexible wings of the aircraft. Dynamic modeling, control design, and stability verification for the aircraft system are conducted. First, the dynamic model of the flexible flapping-wing aircraft is established by an improved rigid finite element (IRFE) method. Second, a novel adaptive fault-tolerant controller based on the fuzzy neural network (FNN) and nonsingular fast terminal sliding-mode (NFTSM) control scheme are proposed for tracking control and vibration suppression of the flexible wings, while successfully addressing the issues of system uncertainties and actuator failures. Third, the stability of the closed-loop system is analyzed through Lyapunov's direct method. Finally, co-simulations through MapleSim and MATLAB/Simulink are carried out to verify the performance of the proposed controller.

This article discusses the control design and experiment validation of a flexible two-link manipulator (FTLM) system represented by ordinary differential equations (ODEs). A reinforcement learning (RL) control strategy is developed that is based on actor–critic structure to enable vibration suppression while retaining trajectory tracking. Subsequently, the closed-loop system with the proposed RL control algorithm is proved to be semi-global uniform ultimate bounded (SGUUB) by Lyapunov’s direct method. In the simulations, the control approach presented has been tested on the discretized ODE dynamic model and the analytical claims have been justified under the existence of uncertainty. Eventually, a series of experiments in a Quanser laboratory platform are investigated to demonstrate the effectiveness of the presented control and its application effect is compared with PD control.

In this paper, the n-dimensional discretized model of the two-link flexible manipulator is developed by the assumed mode method (AMM). Subsequently, based on the discretized dynamic model, both full-state feedback control and output feedback control are investigated to achieve the trajectory tracking and vibration suppression. In order to guarantee the stability strictly, uniform ultimate boundedness (UUB) of the closed-loop system is realized by the Lyapunov's stability. Furthermore, through appropriately choosing control parameters, the states of the system will converge to zero within a small neighborhood. Eventually, extensive simulations and experiments on the Quanser platform for a two-link robotic manipulator are carried out to demonstrate the feasibility of the proposed neural network controller.

In this paper, in order to analyze the single-link flexible structure, the assumed mode method is employed to develop the dynamic model. Based on the discrete dynamic model, fuzzy neural network (NN) control is investigated to track the desired trajectory accurately and to suppress the flexible vibration maximally. To ensure the stability rigorously as the goal, the system is proved to be uniform ultimate boundedness by Lyapunov's stability method. Eventually, simulations verify that the proposed control strategy is effective, and the control performance is compared with the proportion derivative control. The experiments are implemented on the Quanser platform to further demonstrate the feasibility of the proposed fuzzy NN control.