Search

Motion-controlled robotic arms allow a user to interact with a remote real world without physically reaching it. By connecting cyberspace to the physical world, such interactive teleoperations are promising to improve remote education, virtual social interactions and online participatory activities. In this work, we build up a motion-controlled robotic arm framework comprising a robotic arm end and a user end, which are connected via a network and responsible for manipulator control and motion capture respectively. To protect the system access, we propose to verify who is controlling the robotic arm by examining the robotic arm’s behavior, which adds a second security layer in addition to the system login credentials. We show that a robotic arm’s motion inherits its human controller’s behavioral biometric in interactive control scenarios. By extracting the angle readings of the robotic arm’s all joints, the proposed user authentication approach reconstructs the robotic arm’s end-effector movement trajectory that follows the user’s hand. Furthermore, we derive the unique robotic motion features to capture the user’s behavioral biometric embedded in the robot motions and develop learning-based algorithms to verify the robotic arm user to be one of the enrolled users or a nonuser. Extensive experiments show that our system achieves 94% accuracy to distinguish users while preventing user identity spoofing attacks with 95% accuracy.

It is a main direction of industrial application that manipulator performs grasping task in task space and combines obstacle avoidance planning.It is a main direction of industrial application that manipulator performs grasping task in task space and combines obstacle avoidance planning.I This paper presents an improved RRTConnect path planning algorithm IRRTC based on dynamic constrained space and admittance control. The sliding constraints on the Cartesian position space is used to search for the reachable path of the traction robot by the algorithm. For orientation control, a priori path is formed by artificially given constraint interval, and then the dynamic admittance control is modified by using the end grasping load moment estimated by the joint torque sensor of the manipulator as the constraint. At the same time, the algorithm designs a local escape algorithm based on the current state and target state of the robotic arm, which overcomes the problem that the traditional RRTConnect and RRT cannot escape the local minimum. The simulation and experimental results of the Xmate3 redundant manipulator show that a feasible path can be quickly searched based on constraints and local escape strategies, and a relatively optimal trajectory can be planned based on the original path by the optimized path and also meet the constraints. Within 3S, the average planning success rate is increased to over 90%, and without time constraint, the success rate is 100%, the efficiency is higher than other algorithms. Because the admittance control is used in conjunction with the joint torque feedback to maintain the dynamic correction of the end orientation, which improves the stability of the manipulator. The algorithm can be artificially designed to solve the obstacle avoidance and task constraints in disorderly grasping.

In this paper, a noisy output-based direct learning tracking control is proposed for stochastic linear systems with nonuniform trial lengths. The iteration-varying trial length is modeled using a Markov chain for demonstration of the iteration-dependence. The effect of the noisy output is asymptotically eliminated using a prior given decreasing gain sequence in the learning algorithm. Two alternative adaptive gains are presented for improving the tracking performance and the convergence speed. Both the mean square and almost sure convergence are provided. Numerical simulations on a 4 DoF robot arm are presented to illustrate the effectiveness of the proposed scheme.

This paper aims to tackle the controller design issue of highly nonlinear and stochastic inflatable robotic arms (IRAs). A novel control scheme, i.e., hybrid adaptive disturbance rejection control (HADRC), is devised to handle the challenging tracking control of hard-to-model IRAs. The model-free adaptive control (MFAC) that linearizes the dynamics by leveraging solely the online input and output (I/O) data of plants is analytically enhanced for superlinear convergence. Both internal and external disturbances are rejected via the active disturbance rejection control (ADRC) that requires little prior model information. The fuzzy logic control (FLC) is subsequently implemented to correlate the two sub-controllers and contribute to attaining smooth motions. The superiority of the proposed scheme is demonstrated by the comparative simulations and experiments on a 2-degree-of-freedom (DOF) IRA.

This paper discusses the exponential stability in mean square of Takagi-Sugeno fuzzy systems (T-SFSs) under the stochastic case. Moreover, stochastic factors are taken into account to make the model more general. Different from traditional time-triggered control, we introduce event-triggered control strategy into intermittent control and then intermittent dynamic event-triggered control (IDE-TC) is developed, which can reduce updates of the controller and save resources. Besides, we eliminate Zeno phenomena, which is independent of mathematical expectation. Furthermore, the minimum interexecution time by the IDE-TC can be obtained directly for T-SFSs under the stochastic case. In addition, in order to illustrate the theoretical results, an application about double-link robot arm model is given. Meanwhile, we exhibit some numerical simulations.

This article deals with the almost surely exponential synchronization issue for complex dynamical networks (CDNs) under noise control. Different from most of the existing literature, aperiodically intermittent discrete observations noise control is proposed. It is worth noting that the state in noise work time is discretely observed rather than continuously. Meanwhile, some sufficient conditions are presented based on stochastic analytical techniques and the Lyapunov method. Besides, the upper bounds of noise rest rate and the time lag between two consecutive observations are estimated. Moreover, it is clear that CDNs are easier to achieve the almost surely exponential synchronization when noise control gain becomes larger. To demonstrate the effectiveness and feasibility of analytical results, two applications about single-link robot arm systems as well as second-order oscillator systems are given. At the same time, some numerical simulations are exhibited.

This article presents an adaptive control method for dual-arm robot systems to perform bimanual tasks under modeling uncertainties. Different from the traditional symmetric bimanual robot control, we study the dual-arm robot control with relative motions between robotic arms and a grasped object. The robot system is first divided into two subsystems: a settled manipulator system and a tool-used manipulator system. Then, a command filtered control technique is developed for trajectory tracking and contact force control. In addition, to deal with the inevitable dynamic uncertainties, a radial basis function neural network (RBFNN) is employed for the robot, with a novel composite learning law to update the NN weights. The composite learning is mainly based on an integration of the historic data of NN regression such that information of the estimate error can be utilized to improve the convergence. Moreover, a partial persistent excitation condition is employed to ensure estimation convergence. The stability analysis is performed by using the Lyapunov theorem. Numerical simulation results demonstrate the validity of the proposed control and learning algorithm.

In this paper, we investigate the problems of robust exponential stabilization in mean square and ${\mathcal{H}_{\infty}}$ -based integral sliding mode controller design for uncertain stochastic Takagi-Sugeno (T-S) fuzzy switched time delay systems with both matched and unmatched uncertainties under synchronous switching and asynchronous switching, respectively. In the systems, the control input is dependent on switching signal. Under synchronous switching and asynchronous switching, respectively, sufficient conditions are developed to guarantee that the resulting closed-loop systems are robustly exponentially stable in mean square with a prescribed ${\mathcal{H}_{\infty}}$ -performance. The integral sliding mode surfaces and the sliding mode controllers are designed for the underlying systems under synchronous switching and asynchronous switching, respectively. By two examples stemming from the mass-spring-damper model and the single-link robot arm model under stochastic perturbation, the effectiveness of the proposed new design techniques are verified.

Despite some research attempts, a general and comprehensive modeling means for inflatable robotic arms (IRAs) is yet to be established. To bridge this gap, this paper presents a novel approach to capturing the high nonlinearity of a large-scale (approximately 1.8 m long) and ultralight (only 191 g in weight) IRA, which is a trade-off between the computational accuracy and efficiency. The rigid-body kinematics and the piecewise constant curvature (PCC) assumption are compared to formulate the kinematic model that incorporates link compression and base errors. A dynamic model is derived leveraging the Cosserat rod theory recursively to predict the behavior of the IRA's thin-walled and incomplete continuum structure, and its inverse model is explicitly extended for error compensation. Thereafter, some prior-unknown parameters are primarily identified, and the real-time performance is also evaluated to assure the closed-loop tracking control. A comparative study amongst the modeling approach and its existing counterparts is quantitatively performed to verify the accuracy. Three model-based control schemes are contrasted to further demonstrate the viability of the proposed model, where an adaptive sliding mode control (ASMC) scheme is designed to handle the model uncertainties. Experimental results reveal the effectiveness of the modeling method and the superior performance of the ASMC.

A quadcopter manipulator system is an aerial robot consisting of a quadcopter with a robotic arm attached to it. The system has coupled nonlinear dynamics with uncertain time-varying parameters. The work in this paper focuses on designing an adaptive nonlinear controller to facilitate the uncertain system’s trajectory tracking and stability. The novelty of the proposed work is the design and implementation of an adaptive feedback linearization controller, called adaptive augmented torque (AAT) control, for the aerial robot. The control law is based on a feedback linearization controller with model reference adaptive controller and a tracking error-based augmented term. Using the input-to-state stability concept, a bound on the parameter estimation error is also developed. In the presented methodology, the controller uses estimated values of system parameters obtained from the adaptive mechanism and the tracking error to compute the control input using the AAT control law. An adaptive law for estimating unknown parameters is obtained using the strictly positive real-Lyapunov method. The asymptotic stability of the closed-loop system is analyzed via the Lyapunov theory. Simulations implemented in MATLAB and ROS/Gazebo and preliminary hardware experiments are presented to validate the theoretical results and to corroborate the performance of the AAT control law.

This paper proposes a saturated smooth adaptive controller for regulating a certain type of underactuated Euler–Lagrange systems (UELSs) with modeling uncertainties and control saturations based on a singular perturbation approach. Compared with relevent literature, the advantages of the proposed controller include: (1) it renders the UELS semiglobally asymptotically track the desired position without the violation of control input constraints; (2) high-order derivatives of positions are not required in its implementation. The Hoppensteadt’s Theorem is employed to show that the proposed saturated controller renders the UELS semiglobally asymptotically stable about the desired set point with the satisfaction of control input constraints. The control effectiveness is validated by simulations on a two-link compliant robot arm.

A significant number of the planned on-orbit servicing and active debris removal missions will be performed by a spacecraft equipped with a robotic arm. In this paper we propose a new method for the path planning of the robotic arm mounted on the free-floating unmanned spacecraft. In the proposed approach the path of the arm is described in the joint space by a spline function. The coordinates of spline knots form the vector of decision variables. The constrained nonlinear optimization problem is formulated and solved with an active-set algorithm. The proposed method generates a collision-free path of the arm that results in the desired position and orientation of the gripper and the desired attitude of the spacecraft. The possibility of achieving all these goals at the same time is the major advantage of the proposed approach. The practical applicability of the presented method is demonstrated in experiments performed on the planar air-bearing microgravity simulator (for a planar case) and using numerical simulations performed with the Monte Carlo method (for a spatial case). In the planar case the optimal path is compared with the path obtained using the bi-directional Rapidly-exploring Random Trees (RRT) algorithm. It is shown that the path obtained using the proposed method is shorter and smoother than the RRT path.

This paper investigates the sliding mode control (SMC) for semi-Markov jump cyber-physical systems (S-MJCPSs) under randomly occurring deception attacks (RODA). It is assumed that the adversaries inject false data into the control signal in probability. First, a mode-dependent sliding surface is designed under the framework of integral form. Second, the criteria to ensure stochastic stability (SS) of the corresponding system are proposed, which establishes the theoretical framework of semi-Markov Lyapunov function. Third, an appropriate SMC law under attacks and uncertainties is constructed to drive the states of S-MJCPSs onto the switching manifold in finite time. Finally, the simulation study on a single-link robot arm model (SLRAM) is provided to demonstrate the obtained results.

Back-stepping design method is widely used in high-performance tracking control tasks As is known to all, the controller based on back-stepping design will become complex as the model order increases, which is the so called “explosion of terms” problem. In this paper, a tracking differentiator (TD) based back-stepping controller is proposed to handle the “explosion of terms” problem. Instead of calculating the derivatives of intermediate control variables through tedious analytical expressions, for the proposed method, the tracking differentiator is embedded into each recursive procedure to generate the substitute derivative signal for every intermediate control variable. As a result, the complexity of implementation procedure of back-stepping controller is significantly reduced. The discrepancies between the derivative substitutes and the real derivatives are considered. And the effects on control performances caused by the discrepancies are analyzed. In addition to giving the theoretical results and the stability proofs with Lyapunov methods, the developed controller design method is evaluated through a series of experiments with a hydraulic robot arm position serve system. The control performance of the proposed controller is verified by the experiments results.

This paper explores a scheme of tuning a basic input-output (I/O) gain (BIOG) depending on usage conditions for improving the work performance of human-operated machines. I/O gain tuning is effective for improving operability and workability, but continual and/or huge changes makes operators confuse the machine dynamics, and then degrade the operability. Thus, the proposed tuning system tunes the BIOG at long terms according to comprehensive characteristics of the work content and operator, collected from a control lever input histogram. The target value is specified by a Gaussian distribution, meaning that all ranges of the spring-type control lever are equally used. This approach was chosen because leveling the control lever histogram independently of the operator and work content provides a consistent operational experience, leading to comfortable operations. In this study, BIOG curves are specified as a polygonal line involving a break and saturation point. These points are adjusted by reciprocal conversion of differential areas between the Gaussian distribution and obtained histogram curves. Experimental results obtained with a robot arm indicated that the developed BIOG tuning system could improve time efficiency by decreasing the differential area and increase the subjective usability as compared to a conventional fixed BIOG system. Moreover, the relationship between the histogram and BIOG curve could reveal features of both the work content and the operators skill level.

We propose a micro-data ( 10 trials) sensorimotor learning and adaptation (SEED) model for human-like arm inverse dynamics control. The SEED model consists of a feedforward Gaussian motor primitive (GATE) neural network and an adaptive feedback impedance (AIM) mechanism. Sensorimotor weights over trials are learned in the GATE network, while the AIM mechanism is used to online tune impedance gains in a trial. The model was validated by periodic and non-periodic tracking tasks on a two-joint robot arm. As a result, the proposed model enables the arm to stably learn the tasks within 10 trials, compared to thousands of trials required by state-of-art deep learning. This model facilitates the exploration of unknown arm dynamics, in which the elbow joint requires much less active control compared to the shoulder. This control goes below 3% of the overall effort. This finding complies with a proximal–distal control gradient in human arm control. Taken together, the proposed SEED model paves a way for implementing data-efficient sensorimotor learning and adaptation of human-like arm movement.

This work investigates the finite-time filtering problem of discrete-time switched nonlinear systems with unreliable links, where the persistent dwell-time switching mechanism is introduced to describe the changes of system mode and the fuzzy set theory is adopted to construct the model of nonlinear systems . Moreover, unreliable links including random nonlinearity and disturbance are employed to transmit data between the plant and the filter. The main intention of this study is to develop a desired full-order filter such that the resulting filtering error system is finite-time bounded with a fixed energy-to-peak performance index. Afterwards, based on the finite-time bounded theory, and through constructing appropriate Lyapunov–Krasovskii functionals, sufficient conditions for the stability with corresponding performance of the filtering error system are established. Then, by solving a set of convex optimization problems , the specific gains of the desired filter are computed. Finally, a numerical example and a single link robot arm model are provided to expound the correctness and practicability of the proposed approach.

Due to structural complexity of robot arms, constraints of experiments, especially uncertainty of design parameters, numerical models for dynamics analysis of robot arms can produce erroneous resul...

This article presents a multi-degree-of-freedom variable damping controller to manage the trade-off between stability and agility and to reduce user effort in physical human-robot interaction. The controller accounts for the human body's inherent impedance properties and applies a range of robotic damping from negative (energy injection) to positive (energy dissipation) values based on the user's intent of motion. To evaluate the effectiveness of the proposed controller in balancing the trade-off between stability/agility and reducing user effort, two studies are performed on both the human upper-extremity and lower-extremity to represent both industrial and rehabilitation applications of the proposed controller. These studies required subjects to perform a series of multidimensional target reaching tasks while the human user interacted with either the end-effector of a robotic arm for the upper-extremity study or a wearable ankle robot for the lower-extremity study. Stability, agility, and user effort are quantified by a variety of performance metrics. Stability is quantified by both overshoot and stabilization time. Mean and maximum speed are used to quantify agility. To quantify the user effort, both overall and maximum muscle activation, and mean and maximum root-mean-squared interaction force are calculated. The results of both the upper- and lower-extremity studies demonstrate that the controller is able to reduce user effort while increasing agility at a negligible cost to stability.

Multi-axis stepper motors offer a compact solution to actuate multiple degrees of freedom in a mechanical system by a single device. This research presents an electric stepper motor with three coaxial shafts driven by a single stator. The motor dimensions are 42 × 42 × 44 mm $^3$ (excluding output shafts), its mass is 0.326 kg and it includes three rotors of height 7 mm and different number of teeth. The maximum torque for one rotor at low speed and 1 A current is 0.10 N m, which is comparable to that of a conventional stepper motor with similar rotor size. When multiple rotors are driven simultaneously, then the total power has to be distributed over the three rotors, resulting in 42% lower torque on average. The maximum no-load speed is 4000 revolutions per minute. The coaxial output shaft configuration poses significant challenges but also unique opportunities in actuating multiple joints of a robotic system. In many applications, the mechanical design can be redesigned to make effective use of the multi-motor design. An example of a three-DOF robot arm is demonstrated that can be efficiently driven by the proposed motor.

Automation technology has been extensively recognized as an emerging field in various industrial applications. Recent breakthrough in flexible automation is primarily due to deployment of robotic arms or manipulators. Autonomy in these manipulators is essentially linked with the advancements in non-linear control systems. The objective of this research is to propose a robust control algorithm for a five degree of freedom (DOF) robotic arm to achieve superior performance and reliability in the presence of friction. A friction compensation-based non-linear control has been proposed and realized for the robotic manipulator. The dynamic model of the robot has been derived by considering the dynamic friction model. The proposed three-state model is validated for all the joints of the manipulator. The integral sliding mode control (ISMC) methodology has been designed; the trajectories of system every time begin from the sliding surface and it eliminates the reaching phase with assistance of integral term in the sliding surface manifold. The designed control law has been first simulated in Matlab/Simulink environment to characterize the control performance in terms of tracking of various trajectories. The results confirm the effectiveness of the proposed control law with model-based friction compensation. The transient parameters like settling and peak time have improvement as well have better results with friction than without considering the friction. The proposed control law is then realized on an in-house developed autonomous articulated robotic rducational platform (AUTAREP) and NI myRIO hardware interfaced with LabVIEW. Experimental results also witnessed the trajectory tracking by the robotic platform.

Automation of the industrial sector is increasing rapidly. Many industrial applications involve robotic manipulators in a networked environment with sensor and an actual plant via a communication channel. Control of the robot manipulator is a challenging task due to its nonlinearity and parameter uncertainty. However, due to the necessity of lesser bandwidth allocation and to eliminate network congestion, this paper focuses on the development of an event-triggered constrained next-generation robustness, tracking, disturbance rejection and, overall aggressiveness (RTDA) controller for a single link robot arm. The constraints on torque input and torque rate are considered and incorporated in the optimization problem using the Lagrange multiplier method. The proposed event-trigger based next-generation controller has simplicity, flexibility in tuning, and closed form solution similar to PID controllers and superior performance like predictive controllers. In addition, the incorporation of event-triggered mechanism has improved the bandwidth utilization. The performance of the proposed RTDA controller with and without event-triggered mechanism is compared with constrained DMC controller.

Industrial demand for high precision manipulators has lead to heavy, stiff, and therefore, expensive, inefficient, and potentially dangerous serial arm manipulators. The move towards lighter, and therefore, flexible manipulators has not matured, although there have been advancements in data processing capabilities, sensor technology, and control theory in the past couple of decades that could potentially reduce the problems with flexible serial manipulators. Most research in the past has focused on single link manipulators and planar robot arms and less research has been done on spatial multi-link robots. This work presents the derivation for a low-order model for spatial multi-link serial arms. Due to its low number of degrees of freedom this model can be used in real-time systems for control and estimation. The model is then verified by comparing its performance to results from commercial finite element software. Additional tests are performed against a flexible robot arm testbed.

This article presents a controller for global joint position tracking control of torque-driven robot manipulators involving dynamic Dahl friction. The control law has been designed using a novel theoretical Hamiltonian inspired framework, which allows global uniform asymptotic stability (GUAS) of the nonautonomous nonlinear closed-loop system. An observer is provided to estimate the nonmeasurable state of internal friction of the Dahl model, preserving the GUAS attribute in the closed-loop system. The performance of the proposed control scheme is illustrated through experiments on a two-degree-of-freedom direct-drive robot arm.

This article addresses the issue of input–output finite-time stability (IO-FTS) for nonlinear systems by employing sliding-mode control (SMC) methodology. Many practical systems subject to complex factors, such as the single-link robot arm model (SLRAM), can be characterized as nonlinear systems. Our attention is focused on designing a fuzzy-model-based finite-time SMC law to attenuate the influences of uncertainty, nonlinear term, and external disturbance during the finite-time region. First, a novel integral sliding-mode surface is proposed based on the Takagi–Sugeno fuzzy rule. Then, by using the key point of Lyapunov function theory, an appropriate fuzzy SMC law is designed to make sure that the signal variables can arrive at a domain within the assigned fixed-time level. Moreover, some new IO-FTS criteria are constructed for the resulting sliding dynamics over the whole finite-time level, including reaching phase and sliding motion phase. Via the SLRAM, we demonstrate the effectiveness of the proposed SMC approach.

This paper studies the composite refined anti-disturbance control problem of a 2-degree-of-freedom robot arm system modeled by a semi-Markov jump system with multiple disturbances, which includes harmonic disturbances with unknown frequency and amplitude as well as energy bounded disturbances. Firstly, the semi-Markov jump system model is proposed to construct a novel linear model of the 2-degree-of-freedom robot arm subject to two types of disturbances. Next, in order to estimate the uncertain harmonic disturbance, a novel higher order disturbance observer is introduced to convert the uncertain harmonic disturbance into some parameter uncertainty and then estimate the parameter uncertainty. In addition, a corresponding composite anti-disturbance control scheme is formulated to reject and attenuate the above two types of disturbances, respectively. Furthermore, sufficient conditions that can guarantee that the system is stochastically stable are given. Finally, a simulation study of the obtained model is carried out to illustrate the validity of the composite control design method proposed in this paper.

This paper presents a unique optimal torso design selection technique for the dual seven-degree-of-freedom robot arm. This optimal design enhances the robot end-effector’s workspace with less operational stress. The selection procedure incorporates the von Mises stress and robot workspace analysis in different robot-torso mounting configurations. The torso design selection strategy includes the von Mises stress calculation numerically and by using SolidWorks static analysis for robot arm considered as bolted-fixed end cantilever beam. Followed by robot end-effector workspace analysis using inverse kinematics, which determines the maximum area covered by manipulated robot end-effector in 2-dimensional (2D) and 3-dimensional (3D) space. Subsequently, introducing a selection function for torso design. This selection function is calculated based on weighting coefficients (priority given to stress and workspace), von Mises stress, and workspace area. The selection technique concludes that the torso design with upper-arm mounting configuration is optimal for current robot application, i.e., assembly.

The interference of the elasticity of a single robotic arm and the unavoidable time delay of its position control is analysed from nonlinear vibrations viewpoint. The simplified mechanical model of two blocks and a connecting spring considers the first vibration mode of the arm, while the collocated proportional-derivative (PD) control uses the state of the first block only and actuates also there. It is assumed that the relevant nonlinearity is the saturation of the delayed control force. The linear stability analysis proves that stabilizable and non-stabilizable parameter regions follow each other periodically even for large spring stiffnesses and for tiny time delays. Hopf bifurcation calculation is carried out after an infinite-dimensional centre manifold reduction, and closed-form algebraic expressions are given for the amplitudes of the emerging oscillations. These results support the experimental tuning of the control gains since the parameters of the arising and often unexpected self-excited vibrations can serve as a guide for this practical procedure.

With the rapid development of science and technology, the current ordinary industrial manipulator cannot complete the task well, because it is difficult to control. This research mainly discusses the planning and control of the mobile manipulator for the operation of restricted motion objects in an intelligent environment. In this study, the rapid search random tree (RRT) method is used to study the obstacle avoidance motion planning of the manipulator. The 6-axis robotic arm system uses CAN (controller area network) bus to connect each joint in series and then connect to the multi-axis controller Maestro, and the host computer and Maestro send and receive control commands based on the TCP/IP protocol. A 1-DOF grip is installed at the end of the robotic arm, and the grip is connected to the end of the robotic arm (i.e., at the wrist joint of the robotic arm) through a 6-dimensional force 1 torque sensor. The operating system of the host computer is Ubuntu 14.04. The position of the object is recognized through Kinect, and the position of the object is sent to the robotic arm control software. The PTP (precision time protocol) of the robotic arm moves to a certain distance in front of the object, and then the movement of the robotic arm is controlled by visual servoing to complete the object capture. Each joint of the manipulator uses a speed control mode. In a single control cycle, the joint angular velocity of the manipulator remains unchanged. The controller used by the robotic arm is a 51-core STC12C5410AD single-chip microcomputer, which ensures the response speed of the joint motor linkage. In the environment of multiple obstacles, the RRT algorithm has 10 simulation experiments, the average sampling time is 95.651 s, and the average sampling point is 1308. The results show that the RRT algorithm proposed in this study enables the manipulator to move from the starting point to the endpoint in the environment of multiple obstacles, while avoiding the obstacles, and the mobile manipulator can better operate the motion-limited objects.

In this paper, forward/inverse dynamics of a continuum robotic arm is developed using a data-driven approach, which could tackle uncertainties and extreme nonlinearities to obtain reliable solutions. By establishing a direct mapping between the actuator and task spaces, the unnecessary mappings of actuator-to-configuration then configuration-to-task are eliminated, to reduce extra computational cost. The proposed approach is validated through simulation (based on Cosserat rod theory) and experimental tests on RoboArm. Next, path tracking in the presence/absence of obstacles as well as load carrying maneuver are investigated. Finally, the obtained results concerning repeatability, scalability, and disturbance rejection performance of the approach are discussed.

Purpose This study aims to propose a novel method based on model learning with sparsity inducing norms for estimating dynamic gravity terms of the serial manipulators. This method is realized by operating the robot, acquiring data and filtering the features in signal acquisition to adapt to the dynamic gravity parameters. Design/methodology/approach The core principle of the method is to analyze the dictionary composition of the basis function of the model based on the dynamic equation and the Jacobian matrix of an arm. According to the structure of the basis function and the sparsity of the features, combined with joint-angle and driving-torque data acquisition, the effective features of dynamic gravity parameters are screened out using L1-norm optimization and learning algorithms. Findings The theoretical analysis revealed that training data obtained based on joint angles and driving torques could rapidly update dynamic gravity parameters. The simulation experiment was carried out by using the publicly available robot model and compared with the previous disassembly method to evaluate the feasibility and performance. The real 7-degree of freedom (DOF) industrial manipulator was used to further discuss the effects of the feature selection. The results show that this estimation method can be fully operational and efficient in industrial applications. Research limitations/implications This approach is applicable to most serial robots with multi-DOF and the dynamic gravity parameters of the robot are estimated through learning and optimization. The method does not require prior knowledge of the robot arm structure and only requires joint-angle and driving-torque data acquisition under low-speed motion. Furthermore, as it is a data-driven-based method, it can be applied to gravity parameters updating. Originality/value Different from previous general robot dynamic modelling methods, the sparsity of the analytical form of dynamic equations was exploited and model learning was formulated as a convex optimization problem to achieve effective gravity parameters screening. The novelty of this estimation approach is that the method does not only require any prior knowledge but also does not require a specifically designed trajectory. Thus, this method can avoid the laborious work of parameter calibration and the induced modelling errors. By using a data-driven learning approach, the new parameter updating process can be completed conveniently when the robot carries additional mass or the end-effector changes for different tasks.

This paper presents an efficient covid-19 optimization algorithm to find the optimal values of the PD/PID cascaded controller. The purpose of the control algorithm is to force the link shaft to follow the desired reference position with good accuracy all time. This objective should be achieved for different position/time tracks regardless of load disturbance and parameter variations. This work, simulating how the covid-19 spreads and infects to optimize the parameters of the PD/ PID control. The initial values of PD/PID controller parameters consider the zero patient, which infects new patients (other values of PD/PID controller parameters). The optimization model simulates as accurately as possible the covid-19 activity. The covid-19 has two major advantages compared to other similar strategies. First, the covid-19 parameters are already adjusted according to disease statistics to prevent designers from initializing them with arbitrary values. Second, the approach has the ability to finish after several iterations where the infected population initially grows at an exponential rate. However, after some iterations. The proposed covid-19 was investigated with well-known optimization techniques such as the genetic algorithm (GA) and Harmony Search (HS) optimization. A multi-objective function is used to allow the designer to select the desired rise time, the desired settling time, the desired overshoot, and the desired steady-state error. Several tests have been performed to investigate the obtained proper values of PD/PID controller parameters. In the first test, a step position reference had been applied. In the second test, the continuous change in position reference had been subjected to the robot arm. The results provide that the covid-19 based PD/PID controller has the best performance among other techniques. In addition, the covid-19 based PID controller can track accurately the position command compared to other techniques.

In this paper, an online adaptive dynamic programming (OADP) combining with a disturbance observer is proposed to solve the problem of robust optimization for nonlinear systems. The scheme has only one neural network so it is more effective and simpler than others works which use two or three neural networks (two networks for action decision and one for disturbance estimation). Moreover, the stability of the overall system which includes the Actor, Critic, and disturbance observer components is mathematically proven through Lyapunov theory. Finally, the simulation and comparison are done to evaluate the correction and the advantages of the proposed algorithm. The simulation results show that the observer-based OADP technique has the ability to give the good response for the Planar robot even under conditions of system uncertainties and external disturbances. Moreover, they also demonstrate that proposed method is not only simpler but also more effective than other works, i.e., faster convergence, smaller error, and smaller storage requirement.

Although proportional-integral-derivative control remains one of the most common control schemes used in industry, its tuning still remains inadequately understood in many applications. This task becomes much more challenging when applied to multi-input multi-output (MIMO) systems. This article presents the design of a discrete-time robust multivariable (nonadaptive) tracking controller that comes with a simple structure, requires very limited information on the plant model, and is relatively easy to tune. In addition to being easy to tune and implement, an objective of this controller is to deal with a class of large-scale systems with complex dynamics. We analytically demonstrate the robustness and convergence of the closed-loop system for a class of MIMO linear time-varying systems. The overall superiority of the proposed controller is experimentally validated on a Barrett robot arm in a laboratory environment. The article also provides a stochastic framework of the general setting of the controller. Within this framework, two minimum mean square error optimal solutions of the controller are provided; one is designed for the case where the number of inputs is not greater than the number of outputs, and the other is for the antithesis.

This paper presents a feasible analysis of a foldable robot arm for drones (FRAD) for payload lifting without drone landing. The arm design is aimed to be light, foldable and energy-efficient. FRAD is designed with a scissor mechanism in a parallel arrangement actuated by a twisted string actuator (TSA) with a guide. The robot arm can fold, unfold, and easily deploy on drones by having benefit from inherent TSA advantages. We propose a linkage design approach to provide a solution to mechanical singularity and to improve a space-saving capability. The nonlinear pulling force of twisted strings’ transmission ratio (decreasing with twisting) closely matched the typical force profile requirement for folding the arm. This fact was used in the foldable robot arm for its optimal actuation design. The required string torque and twist angle were calculated to generate a pulling force to fold the arm with a 0.3 kg payload from the unfolded to the fully folded situation. Preliminary experimental trials demonstrated that the proposed FRAD could generate the required lifting force and fold the arm without a high torque motor.

© 2021 The Authors. This paper presents the novel design of an ambidextrous robot arm that offers double range of motion as compared to dexterous arms. The arm is unique in terms of design (ambidextrous feature), actuation (use of two different actuators simultaneously: Pneumatic Artificial Muscle (PAM) and Electric Motors)) and control (combined use of Proportional Integral Derivative (PID) with Neural Network (NN) and Multiple Adaptive Neuro-fuzzy Inference System (MANFIS) controller with selector block). In terms of ambidextrous robot arm control, a solution based on forward kinematic and inverse kinematic approach is presented, and results are verified using the derived equation in MATLAB. Since solving inverse kinematics analytically is difficult, Adaptive Neuro Fuzzy Inference system (ANFIS) is developed using ANFIS MATLAB toolbox. When generic ANFIS failed to produce satisfactory results due to ambidextrous feature of the arm, MANFIS with a selector block is proposed. The efficiency of the ambidextrous arm has been tested by comparing its performance with a conventional robot arm. The results obtained from experiments proved the efficiency of the ambidextrous arm when compared with conventional arm in terms of power consumption and stability.

The robotic arm is an automatic control device that imitates the function of a human arm and accurately recognizes specific points in the three-dimensional space according to received instructions. This article aims to study the wireless control system of six-degree-of-freedom manipulator under the Internet of Things. In this paper, the Arduino material platform is used as the main controller, combined with Arduino IDE to complete the six-degree-of-freedom manipulator and main load control program, and use APPinventor to develop Android applications, and realize the wireless control of the manipulator through Bluetooth mode. In addition, the robot arm Jacobi control algorithm is used to describe the differential mapping relationship between the robot arm motion and the camera image space, and the image is updated online through the calculation method to improve the accuracy of the robot arm light control server. Mark the control of each joint, and then collect these control signals and the position information of the six joints. In order to ensure the richness of the entry signs and the safety of the equipment, the control signal given here is a random signal, and the output signal is six connected position signals.A total of 400 input and output sample sets were used. The first 200 units were used for training and the last 200 were used for testing. The experimental results in this paper show that the performance of the six-degree-of-freedom manipulator based on the Internet of Things is 25% higher than that of the traditional machine, and the stability of the wireless control system is also improved by 18% compared with before.

This study presents a novel four-degrees-of-freedom parallel gripper with potential application to industrial automation. The gripper adopts a parallel grasping mode on objects and can independently complete in-plane horizontal and vertical motions and in-hand twisting motion. Kinematic and dynamic models of the gripper–object system are developed. The controllable internal force acting on the object is calculated to obtain the minimum driving force/torque. An energy-based manipulability index is developed on the basis of the derived solutions. The numerical simulation includes a comparison between the MATLAB model and the ADAMS model to verify the motion forms of the parallel gripper and the rationality of analytical modeling studies. Manipulability performance is evaluated along the transportation path of the object. Results indicate that the gripper can achieve horizontal transmission to supplement the workspace of a robotic arm, and it exhibits relatively better performance in in-hand manipulation and in-plane vertical transmission.

In this paper, a remote-control system for a six-degree-of-freedom robotic arm that uses an indirect teaching method is proposed. In the indirect teaching method, an essential time delay occurs, which degrades the system performance. To overcome this time delay, which can be modeled using a Smith predictor, a model neural network (MNN) has been adopted. The Smith predictor is a model-based algorithm that is uncertain and prone to interference. In this study, the MNN has been utilized in an effective manner to model the system to support the Smith predictor algorithm. Using this time delay compensation, the outer loop proportional, integral, and derivative (PID) control gains are adjusted in an optimal manner through a PID neural network (PIDNN) to ensure that the robotic arm follows human commands precisely. By using the PIDNN proposed in this paper, the time required for indirect teaching application of the robot arm can be reduced.

The paper compares the most commonly used numerical methods of solving a set of nonlinear equations, especially in terms of computational speed. The methods are applied to a set of nonlinear equations that describe the forward kinematics of a non-standard robotic arm. This arm is an open-loop kinematics chain, composed of special rotary modules. A non-standard feature of the modules is the unlimited rotation around their own axis. This robotic arm consists of six such modules and, thus, has six degrees of freedom. Computations of this nonlinear set of equations are also called inverse kinematics. All computations were performed in Matlab. The same initial conditions, the computation input parameters, and the same structure of the program was used with each method. By applying the below mentioned known methods to the same set, we sought to choose a suitable computation method for the given type of mechanism.

The key motions of land and water are analyzed for a designed amphibious carrier robot. On land, based on the characteristics of the multi-joint crawler, the robot has planned two gaits for climbing steps on land. The kinematic models of climbing steps and climbing steps are established for the two planned gaits, and the maximum height that the robot can climb steps is obtained based, Based on the laws of dynamics, the stability equation of the robot under strong transient impact is established to obtain the conditions for the robot to maintain its own stability when the robot is under strong transient impact during traveling, which provides a theoretical basis for the attitude control of the robot arm. In the water, the fluid numerical method is used to simulate the underwater motion of the robot's swing arm in the extended and retracted state, obtain the traveling resistance and surface pressure of the robot's swing arm crawler in the two states, analyze the relationship between the robot's traveling resistance and speed, obtain the relationship expression between the robot's traveling resistance and the traveling speed, and provide data support of the state selection and speed setting of the robot's underwater crawler.

This paper mainly discusses H ∞ finite-time realization for a class of uncertain Markovian jump systems (MJSs) with unmeasurable state via sliding mode control (SMC) method. A key question is how to design an appropriate SMC approach in a finite-time to reduce the effects of model switching and external interference on the overall performance of the system under consideration. First, by designing an appropriate finite-time SMC law based on a non-fragile observer, the state trajectory can reach a specified sliding surface (SSS) within given finite-time interval. Then, by means of time-partitioning strategy, finite-time boundedness (FTBs) criteria including the reaching phase and the sliding motion phase are proposed for uncertain MJSs with H ∞ performance. Second, by solving the corresponding linear matrix inequalities (LMIs), the controller gain and the observer gain are obtained. Finally, a single-link robot arm model (S-LRAM) is given to prove the effectiveness of the proposed method.

Controlling soft robots with precision is a challenge due to the difficulty of constructing models that are amenable to model-based control design techniques. Koopman operator theory offers a way to construct explicit dynamical models of soft robots and to control them using established model-based control methods. This approach is data driven, yet yields an explicit control-oriented model rather than just a “black-box” input–output mapping. This work describes a Koopman-based system identification method and its application to model predictive control (MPC) design for soft robots. Three MPC controllers are developed for a pneumatic soft robot arm via the Koopman-based approach, and their performances are evaluated with respect to several real-world trajectory following tasks. In terms of average tracking error, these Koopman-based controllers are more than three times more accurate than a benchmark MPC controller based on a linear state-space model of the same system, demonstrating the utility of the Koopman approach in controlling real soft robots.

Using a space robot to capture a non-cooperative spacecraft with high-speed rotation is significantly challenging since any collision generated during capturing will have great impact on both. To reduce the risk in capturing operations, it is crucial to slow down the rotation velocity of the target before capturing. Hence, this paper studies a collision control strategy for using a robotic arm to detumble a non-cooperative spacecraft. The goal of this strategy is to maintain contact between the robot and the target and to apply continuous detumbling force on the target to slow down its rotational motion. To achieve that, first the mechanism analysis of the two balls for a central collision scenario is performed. Then an overdamping control method is proposed to avoid the separation of the balls after the central collision based on the overdamping property of the mass-spring-damper system, the effectiveness of which is validated theoretically. Finally, to use this overdamping control method in the detumbling missions of space robots, a position-based overdamping control strategy is introduced. In this strategy, the relative dynamic behavior between the robotic arm tip and the contact surface of the target during detumbling is approximated to that of a mass-spring-damper system. Owing to the proposed overdamping control method, the contact between the robot and the target can be maintained, and the space robot can apply continuous control torque to slow down the rotational velocity of the target. Numerical simulations are performed to demonstrate the validity of the proposed control strategy.

Hamiltonian method based on action micro-control is widely used in the control of mechanical arm synchronous motor. In order to realize the combination of robot dynamics and drive motor control, Hamiltonian control method is used in this paper to exploit a novel controller for robot, which can be used for better steady-state characteristics in the system. However, dynamic response of port-controlled Hamiltonian (PCH) of control system is slower, so the related control method is exploited and coordinated with the proportional-derivative (PD) plus gravity compensation. At this time, the system has both the fast dynamic response of the PD and the steady state of the PCH. The reverse motor method is used and the two controllers are combined by current conversion to realize the overall control of the robot and the drive motor. The robot drive motor is controlled, and the robot joint position control is combined with the drive motor current control by current conversion. It can be seen from the simulation results that the coordinately controlling the end position of robot can reach the desired position quickly and accurately. Moreover, compared with the separate control of PD plus gravity compensation and PCH control method, it is proved that this scheme has both a fast dynamic process and better performance and ability to resist load torque disturbance. So control method proposed in this paper has a good application prospect

High tumbling rates of uncooperative target pose strong technical challenges and collision risks that can prevent removal of the debris using contact, such as with robotic arms or capture nets. Ele...

Autonomous on-orbit servicing, such as capture, refuel, repair and refurbishment of on-orbit satellites using a robotic arm mounted on servicing satellite is one of the important components of future’s space missions. Space robots increase reliability, safety, and ease of execution of space operations, but pose a novel challenge due to micro-gravity and space environments. While capturing high speed orbiting objects, robotic arms undergo impact and require appropriate modeling of the system. In this paper, a unified framework is provided for modeling impact dynamics, post-capture stabilization and target maneuvering of a multi-arm robotic system mounted on a servicing satellite while capturing orbiting objects. The dynamic model of multi-arm space robot is obtained using the Decoupled Natural Orthogonal Complement (DeNOC) based formulation and closed-loop constraint equations. All three phases of the capturing operation, namely, approach, impact, and post-impact are modeled using Impulse-momentum approach and conservation of momentum. In the approach phase, robot arms are planned to move from its initial configuration to the desired capture configuration. In the impact phase, a framework is developed to estimate the impulse forces and changes in the generalized velocities caused by the impact. In post-impact phase, these velocities are used as initial conditions for the post-impact dynamics simulations. The uncontrolled dynamics during post-impact will result in an undesirable motion, thus post-impact reactionless control (minimum base disturbance) strategy is used to maneuver the space robot’s arms and target object. As such, the robotic arms can be used to maneuver an astronaut for repair of satellite. Most of the times the parameters of target object are not known. Hence, an adaptive reactionless control strategy has been devised for capturing object with unknown parameters. The effectiveness of the framework is shown using a dual-arm robot mounted on a servicing satellite performing capturing operation for multiple objects. The effects of relative velocity and angle of approach on the impact forces are also investigated.

Aerial manipulators (AMs) are flying robotic systems composed of a multicopter equipped with one or more robotic arms. The dynamic modeling of AMs is complex due to the highly coupled interactions between the multicopter and the robotic arms. In this paper, the dynamics and kinematics of a quadcopter are combined with the Newton–Euler recursive method to obtain the forces applied by the robotic arm on the multicopter. The complete dynamic model is then linearized and expressed in terms of the articulation angles of the robotic arm for controller synthesis purposes. A novel gain-scheduled controller based on structured $${\mathcal {H}}_\infty $$ synthesis is proposed using the joint angles of the robotic arm as scheduling variables. The controller is tuned according to multiple performance objectives on a family of parameterized linearized models. Simulations on the non-linear model with the gain-scheduled controller demonstrate its ability to compensate for robotic arm movements, while keeping the desired dynamic characteristics regardless of arm position. The advantage of gain scheduling is demonstrated by a comparison with a non-scheduled version of the controller. Finally, the controller is shown to be able to compensate for a $$10\%$$ uncertainty on the nominal tuning values of the physical parameters.

This paper investigates the lementation of optimal controllers to dynamic control systems and addresses their causality. First, a one-link robot arm system is introduced as the model for which the optimal controller will be designed. This model is used in a discrete linearized for so as to ease the mathematics involved, however no generality is lost due to this simplification as dictated by the obtained results. Then a proposed time-optimal controller is first adopted in which the control requirements are reduced to merely reaching a specified set-point in the shortest possible time. This, of course, gives rise to large control signals that may not be physically attained resulting in a fictitious controller. Then a relaxation is made to overcome this problem by applying a performance index to be minimized through the use of the minimum principle of Pontryagen. Finally, a comparative analysis is briefly made supported by the response curves.

This letter describes a novel pneumatically controlled hydraulic booster: Air-Hydraulic Servo Booster (AHSB) applied to water-hydraulic robots. This pump-less hydraulic circuit can control middle range water pressure easily by pneumatic proportional valves, where one can use tap water as the hydraulic medium. In this letter, we explain the principle of operation and the control system integration applied to a simple submersible robot arm. Then we analyze the full dynamics of the system and derive control algorithms by which a robust pressure controller is used with sliding mode control. The proposed system is evaluated using a joint torque and position control experiment on the 0.35-m-long robot arm with a 10 kg payload, with connections via 20-m-long hydraulic hoses. The experimental results demonstrate the high potential of the proposed system for remotely operated robots that work compliantly and robustly in various fields without contamination.