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The purpose of this paper is to design a new compliant motion/force control strategy for robotic manipulators with environmental constraints in the sense of fixed-time stability.

This paper investigates a novel compliant motion/force control strategy for robotic manipulators with environmental constraints. By using the Lyapunov theory and fixed-time stability theory, a non-singular terminal sliding mode manifold is first established. Then, the compliant motion/force controller is designed, and relevant conditions are given for guaranteeing that the robotic manipulator can track the prescribed constrained trajectory while exerting a desired force to the environment in fixed-time. An illustrative example is presented to show the effectiveness of our proposed control strategy.

Based on fixed-time stability theory, the desired compliant motion/force controller for robotic manipulators with environmental constraints is developed.

Compared with most existing literature, the proposed fixed-time compliant motion/force control strategy can provide the upper bound of the settling time independent of the initial conditions in designing procedure and is more practical for the real-world applications.

The purpose of this paper is to propose an adaptive control for a redundant robot manipulator interacting physically with the environment, especially with the existence of humans, on its body.

The redundant properties of the robot manipulator are used and a reference velocity variable is introduced to unify the operation-space tracking control and the null-space impedance control under one common framework. Neural networks are constructed to deal with unstructured and unmodeled dynamic nonlinearities. Lyapunov function is used during the course of control design and simulation studies are carried out to further illustrate the effectiveness of the proposed strategies.

Satisfying tracking performance in the operation-space and compliance behavior in the null-space of the redundant robot manipulator are ensured simultaneously.

The design procedure of redundant robot manipulators control can be greatly simplified, and the framework of multi-priority control can be transformed into a joint-space velocity tracking problem via the introducing of a reference velocity variable.

This paper aims to carry out assembly variation analysis for mechanisms with compliant joints by considering deformations induced by manufactured deviations. Such an analysis procedure extends the application area of direct linearization method (DLM) to compliant mechanisms and also illustrates the dimensional interaction within multi-loop compliant structures.

By applying DLM to both geometrical equations and Lagrange’s equations of the second kind, an analytical deviation modeling method for mechanisms with compliant joints are proposed and further used for statistical assembly variation analysis. The precision of this method is verified by comparing it with finite element simulation and traditional DLM.

A new modeling method is proposed to represent kinematic relationships between joint deformations and parts/components deviations. Based on a case evaluation, the computational efficiency is improved greatly while the modeling accuracy is maintained at more than 94% rate comparing with the benchmark finite element simulation.

The Equilibrium Equations of Incremental Forces derived from Lagrange’s equations are proposed to quantitatively represent the relationships between manufactured deviations and assembly deformations. The present method extends the application area of DLM to compliant structures, such as automobile suspension systems and some Micro-Electro-Mechanical-Systems.

The aim of this paper is to present the non‐model‐based multiple impedance control (NMIC) law for object manipulation tasks, which can be implemented with reasonable limited on‐line computations.

The multiple impedance control (MIC) is a model‐based algorithm that enforces a designated impedance on all cooperating manipulators, and the manipulated object itself. In this paper, the MIC law is modified to be implemented without using system dynamics. Therefore, this modified MIC law is a quick and more realistic algorithm for implementation in cooperating robotic systems, and so is called NMIC. Developing the NMIC law, error analysis shows that under the NMIC law all participating manipulators, and the manipulated object exhibit the same designated impedance behavior. Next, the proposed NMIC law is applied on an object manipulation task with three cooperating PUMA 560 manipulators while two of them are equipped with a remote compliant centre.

Developing the NMIC law, error analysis shows that under the NMIC law all participating manipulators, and the manipulated object exhibit the same designated impedance behavior. The obtained results show good tracking performance even in the presence of impacts due to contact with an obstacle, and also system flexibility.

The obtained results show good tracking performance even in the presence of impacts due to contact with an obstacle, and also system flexibility. These results reveal the merits of NMIC law as a non‐model‐based algorithm for object manipulation tasks, which can be implemented with reasonable limited on‐line computations.

The proposed NMIC law is applied on an object manipulation task with three cooperating PUMA 560 manipulators while two of them are equipped with a remote compliant centre.

The purpose of this paper is to present a comparison study of cooperative object manipulation control algorithms. To this end, a full comprehensive survey of the existing control algorithms in this field is presented.

Cooperative manipulation occurs when manipulators are mechanically coupled to the object being manipulated, and the manipulators may not be treated as an isolated system. The most important and basic impedance control (IC) strategies for an assumed cooperative object manipulation task are the Augmented Object Model (AOM) control and the multiple impedance control (MIC) which are found based on the IC, where the former is designed based on the object movement, and the latter is designed based on the whole robot movement. Thus, the basis of these two algorithms are fully studied.

The results are fully analyzed, and it is practically verified that the MIC algorithm has the better performance. In fact, the results reveal that the MIC system could successfully perform the object manipulation task, as opposed to the AOM controller: for the same controller gains, the MIC strategy showed better performance than the AOM strategy. This means that because there is no control on the robot base with the AOM algorithm, the object manipulation task cannot be satisfactorily performed whenever the desired path is not within the robot work space. On the other hand, with the MIC algorithm, satisfactory object manipulation is achieved for a mobile robotic system in which the robot base, the manipulator endpoints and the manipulated object shall be moved.

A simple conceptual model for cooperative object manipulation is considered, and a suitable setup is designed for practical implementation of the two ICs.

The basis of these two aspects or these two algorithms is fully studied and compared which is the foundation of this paper. For this purpose, a case study is considered, in which a space free-flying robotic system, which contains two 2-degrees of freedom planar cooperative manipulators, is simulated to manipulate an object using the above control strategies. The system also includes a rotating antenna and camera as its third and fourth arm. Finally, a simple conceptual model for cooperative object manipulation is considered, and a suitable setup is designed for practical implementation of the two ICs.

The purpose of this paper is to achieve stable grasping and dexterous in-hand manipulation, the control of the multi-fingered robotic hand is a difficult problem as the hand has many degrees of freedom with various grasp configurations.

To achieve this goal, a novel object-level impedance control framework with optimized grasp force and grasp quality is proposed for multi-fingered robotic hand grasping and in-hand manipulation. The minimal grasp force optimization aims to achieve stable grasping satisfying friction cone constraint while keeping appropriate contact forces without damage to the object. With the optimized grasp quality function, optimal grasp quality can be obtained by dynamically sliding on the object from initial grasp configuration to final grasp configuration. By the proposed controller, the in-hand manipulation of the grasped object can be achieved with compliance to the environment force. The control performance of the closed-loop robotic system is guaranteed by appropriately choosing the design parameters as proved by a Lyapunove function.

Simulations are conducted to validate the efficiency and performance of the proposed controller with a three-fingered robotic hand.

This paper presents a method for robotic optimal grasping and in-hand manipulation with a compliant controller. It may inspire other related researchers and has great potential for practical usage in a widespread of robot applications.

The purpose of this paper is to focus on the changing dynamics of the ultrasonic consolidation (UC) process due to changes in substrate geometry. Past research points to a limiting height to width ranging from 0.7 to 1.2 on build features.

Resonances of a build feature due to a change in geometry are examined and then a simple non‐linear dynamic model of the UC process is constructed that examines how the geometry change may influence the overall dynamics of the process. This simple model is used to provide estimates of how substrate geometry affects the differential motion at the bonding interface and the amount of energy emitted by friction change due to build height. The trends of changes in natural frequency, differential motion, and frictional energy are compared to experimental limits on build height.

The paper shows that, at the nominal build, dimensions of the feature the excitation caused by the UC approach two resonances in the feature. In addition trends in regions of changes of differential motion, force of friction, and frictional energy follow the experimental limit on build height.

This paper explores several aspects of the UC process not currently found in the current literature: examining the modal properties of build features, and a lumped parameter dynamic model to account for the changes in of the substrate geometry.

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.

First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.

Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.

This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

This study aims to propose a force control algorithm based on neural networks, which enables a robot to follow a changing reference force trajectory when in contact with human skin while maintaining a stable tracking force.

Aiming at the challenge of robots having difficulty tracking changing force trajectories in skin contact scenarios, a single neuron algorithm adaptive proportional – integral – derivative online compensation is used based on traditional impedance control. At the same time, to better adapt to changes in the skin contact environment, a gated recurrent unit (GRU) network is used to model and predict skin elasticity coefficients, thus adjusting to the uncertainty of skin environments.

In two robot–skin interaction experiments, compared with the traditional impedance control and robot force control algorithm based on the radial basis function model and iterative algorithm, the maximum absolute force error, the average absolute force error and the standard deviation of the force error are all decreased.

As the training process of the GRU network is currently conducted offline, the focus in the subsequent phase is to refine the network to facilitate real-time computation of the algorithm.

This algorithm can be applied to robot massage, robot B-ultrasound and other robot-assisted treatment scenarios.

As the proposed approach obtains effective force tracking during robot–skin contact and is verified by the experiment, this approach can be used in robot–skin contact scenarios to enhance the accuracy of force application by a robot.

The purpose of this paper is to present the radial basis function (RBF) networks‐based adaptive robust control for an omni‐directional wheeled mobile manipulator in the presence of uncertainties and disturbances.

First, a dynamic model is obtained based on the practical omni‐directional wheeled mobile manipulator system. Second, the RBF neural network is used to identify the unstructured system dynamics directly due to its ability to approximate a nonlinear continuous function to arbitrary accuracy. Using the learning ability of neural networks, RBFNARC can co‐ordinately control the omni‐directional mobile platform and the mounted manipulator with different dynamics efficiently. The implementation of the control algorithm is dependent on the sliding mode control.

Based on the Lyapunov stability theory, the stability of the whole control system, the boundedness of the neural networks weight estimation errors, and the uniformly ultimate boundedness of the tracking error are all strictly guaranteed.

In this paper, an adaptive robust control scheme using neural networks combined with sliding mode control is proposed for crawler‐type mobile manipulators in the presence of uncertainties and disturbances. RBF neural networks approximate the system dynamics directly and overcome the structured uncertainty by learning. Based on the Lyapunov stability theory, the stability of the whole control system, the boundedness of the neural networks weight estimation errors, and the uniformly ultimate boundedness of the tracking error are all strictly guaranteed.