Manipulator In Joint Space Research Articles

Neural Network Adaptive Inverse Control of Flexible Joint Space Manipulator Considering the Influence of Gravity.

With the aim of correcting the problem of trajectory tracking control of a flexible joint space manipulator in environments with different gravity, a neural network adaptive inverse control algorithm based on singular perturbation theory is proposed to resist the disturbance caused by system uncertainty. Firstly, the dynamic model of a flexible joint space manipulator with the influence of gravity is established, and then the system is divided into a fast subsystem and a slow subsystem using singular perturbation theory. The velocity feedback control rate is designed for the fast subsystem to suppress the elastic vibration caused by the joint flexibility. For the slow subsystem, the uncertain term and known term are separated by the inverse control algorithm, where the uncertain term is approximated online by the RBF neural network, and the robust control rate is designed to compensate for the approximation error. The simulation results show that the control method can not only effectively reduce the high-frequency vibration caused by the flexible joint but also resist the system disturbance so that a good track control effect is achieved.

Partial Potential Energy Shaping Control of Torque-Driven Robot Manipulators in Joint Space

Partial Potential Energy Shaping Control of Torque-Driven Robot Manipulators in Joint Space

Novel Potential Guided Bidirectional RRT* With Direct Connection Strategy for Path Planning of Redundant Robot Manipulators in Joint Space

Novel Potential Guided Bidirectional RRT^* With Direct Connection Strategy for Path Planning of Redundant Robot Manipulators in Joint Space

Observer-Based Output Feedback Tracking Control for Flexible Joint Space Manipulator with Output Constraints

Observer-Based Output Feedback Tracking Control for Flexible Joint Space Manipulator with Output Constraints

Decentralized Robust Control of Robotic Manipulator in Joint Space Based on Torque Feedbacks

Decentralized Robust Control of Robotic Manipulator in Joint Space Based on Torque Feedbacks

Modified power rate sliding mode control for robot manipulator based on particle swarm optimization

This work suggests an optimized improved power rate sliding mode control (PRSMC) to control a 4-degrees of freedom (DOF) manipulator in joint space as well as workspace. The proposed sliding mode control (SMC) aims to improve the reaching mode and to employ an optimization method to tune the control parameters that operate the robotic manipulator adaptively. Inverse kinematics is used to obtain the joint desired angles from the end effector desired position, while forward kinematics is used to obtain the real Cartesian position and orientation of the end effector from the real joint angles. The proposed enhancements to the SMC involve the use of the hyperbolic tangent function in the control law to improve the reaching mode. Added to that, particle swarm optimization (PSO) is used to tune the parameters of the improved SMC. Furthermore, the Lyapunov function is utilized to analyze the stability of the closed-loop system. The proposed enhanced sliding mode combined with the optimization method is applied experimentally on a 4-DOF manipulator to prove the feasibility and efficiency of the proposed controller. Finally, the performance of the suggested control scheme is compared with the conventional power rate SMC in order to demonstrate the enhanced performance of the suggested method.

Family of Bounded Regulators with Variable Gains and Lyapunov Asymptotic Stability for Robot Manipulators

In this paper, the regulation problem for robot manipulators in joint space through the proposal of a new family of bounded regulators with variable gains is presented. The proposed regulators have bounded functions that replace the classical position error and the velocity; moreover, the variable gains are formed by a family of Lipchitz functions with the position error and the velocity as their arguments. This structure avoids exceeding the physical limits of the servomotors. A strict Lyapunov function is proposed to demonstrate the global and asymptotic stability. Finally, the functionality and performance of the proposal are examined by experimental results on a direct-drive-robot of 3-degrees-of-freedom against the PD regulator

Energy regulation of torque–driven robot manipulators in joint space

The energy regulation of fully actuated torque–driven robot manipulators in joint space is addressed in this paper. The proposed controller is designed via an energy shaping plus damping injection approach. The contribution is the proposal of an energy regulator with partial damping injection capable of inducing oscillations in the undamped joints of robot manipulators, with an user specified desired frequency and amplitude, by adding only damping in the rest of the joints, which may require less control effort than a trajectory tracking controller with full damping injection. Although viscous friction is considered in all joints of robot manipulator, it has been compensated via the proposed energy regulator. Moreover, the controlled periodic motion oscillates around a desired joint position as reference, and this provides a nice feature in the robot, mainly when there is not interest in the undamped joint to follow an specified time-varying sinusoidal function, but generating an oscillatory motion of constant amplitude and frequency. Instrumental in stability analysis is the Lyapunov’s theory and LaSalle’s theorem, which allows concluding that the closed-loop trajectories approach an invariant set that could include a unique equilibrium or periodic orbits. Numerical simulations on a manipulator arm model of two degrees of freedom illustrate the main results.

Hybrid force/position control in workspace of robotic manipulator in uncertain environments based on adaptive fuzzy control

In this study, a novel hybrid force/position controller in workspace of robotic manipulator based on adaptive fuzzy control is developed to improve the controlling performance of force and position in contact with uncertain environments. The dynamic model of the robotic manipulator in joint space is converted to a dynamic model in workspace, which is consisted of the model of position-controlled subsystem and model of force-controlled subsystem. Furthermore, in the position-controlled subsystem, an adaptive fuzzy computed torque control is proposed by considering adaptive fuzzy control and conventional computed torque control (AFCTC), for compensating deviations caused by the presence of structured uncertainty and unstructured uncertainty. In the force-controlled subsystem, a fuzzy proportional integral control method (FPI) is proposed by fuzzy logic and conventional proportional integral method to improve the control performance. The asymptotic stability of the developed controller is proved by Lyapunov theorem. The simulation results show the preferable performance of the proposed controller (AFCTC-FPI) by comparison with adaptive fuzzy sliding mode control (AFSMC), adaptive network-based fuzzy inference system with proportion differential and integral (ANFIS-PD+I), computed torque control-proportion integral (CTC-PI), fuzzy proportional integral derivative (FPID), and proportional integral derivative (PID) in uncertain environments. Furthermore, in the experimental study, average position error with AFCTC-FPI is decreased by 87.14 % than that with PID, meanwhile, range of interaction force with AFCTC-FPI is reduced by 70.31% than that with PID. In summary, the experimental results also show the superior control accuracy of the proposed controller in real environment.

Global Saturated Regulator with Variable Gains for Robot Manipulators

In this paper, we propose a set of saturated controllers with variable gains to solve the regulation problem for robot manipulators in joint space. These control schemes deliver torques inside the prescribed limits of servomotors. The gamma of variable gains is formed by continuous, smooth, and differentiable functions of the joint position error and velocity of the manipulator. A strict Lyapunov function is proposed to demonstrate globally asymptotic stability of the closed-loop equilibrium point. Finally, the functionality and performance of the proposal are illustrated via simulation results and comparative analysis against Proportional-Derivative (PD) control scheme on a two-degrees-freedom direct-drive robot manipulator.

Global Saturated Regulator with Variable Gains for Robot Manipulators

In this paper, we propose a set of saturated controllers with variable gains to solve the regulation problem for robot manipulators in joint space. These control schemes deliver torques inside the prescribed limits of servomotors. The gamma of variable gains is formed by continuous, smooth, and differentiable functions of the joint position error and velocity of the manipulator. A strict Lyapunov function is proposed to demonstrate globally asymptotic stability of the closed-loop equilibrium point. Finally, the functionality and performance of the proposal are illustrated via simulation results and comparative analysis against Proportional-Derivative (PD) control scheme on a two-degrees-freedom direct-drive robot manipulator.

Research on Space Motion Trajectory Optimization of the Industrial Robot

In this paper, an online iterative learning planning method is proposed for industrial robot joint space manipulator to capture moving objects. The dynamic mathematical model of the robot is established, and the trajectory of the robot manipulator is tracked and adjusted through the path planning algorithm to change the visual impedance of the robot and optimize the system parameters. It makes the velocity direction of the end effector of the manipulator consistent with the tangent direction of the weld, and realizes the tracking and recognition of the weld trajectory at the end of the manipulator, so as to improve the accuracy and reliability of the robot in capturing moving objects. The simulation results show that the algorithm has good convergence and robustness.

An asinh-type regulator for robot manipulators with global asymptotic stability

In this paper, a new asinh-type control scheme with gravity compensation for the position control problem of robot manipulators in joint space is presented. The properties and characteristics of the asinh control structure make the position error and the motion velocity asymptotically converge to the equilibrium point. A strict Lyapunov function to formally prove global asymptotic stability is developed. The tuning of the control gains is obtained by PSO (Particle Swarm Optimization) technique without saturating the servomotors. To illustrate the effectiveness and performance of the proposed scheme, an experimental comparative analysis between the proportional-derivative (PD) and atanh controls against the proposed algorithm on a three degrees of freedom direct-drive robot manipulator is carried out.

Model Predictive Control With Integral Compensation for Motion Control of Robot Manipulator in Joint and Task Spaces

The motion control of robot manipulators is a crucial problem concerning automatically controlled robots. In this work, the model predictive control method with an integral compensation (MPC-I), which compensates for the matched uncertainties due to unmodeled dynamics, is proposed to solve the trajectory tracking problem of robot manipulators in joint and task spaces. First, this paper decouples the joint variables of the robot manipulator using a computed torque control method. The MPC-I method is, thereafter, derived to realize the motion control of the robot manipulators in joint space. To realize the motion control of the robot manipulator in task space, the task space is, thereafter, converted into the joint space, in which the MPC-I method is executed, afterward, to control the robot. Furthermore, an MPC-I variation, in which the inverse kinematics is calculated indirectly, is proposed to achieve the motion control in task space. The novelty of this paper is to propose the MPC-I method and the method of converting task space to joint space with indirect inverse kinematics calculation. The former is suitable for the dynamic control of the robot manipulators in the joint space, and the latter can extend the MPC-I method to dynamic control in task space. To evaluate the performance of the proposed control method, motion control simulations are performed in the task and joint spaces, respectively. Simulation results and comparisons verify the effectiveness of the proposed control approach for the dynamic control of the UR5 robot manipulator.

Low Delay Control Algorithm of Robot Arm for Minimally Invasive Medical Surgery

Minimally invasive surgical robots have received more and more attention from medical patients because of their higher surgical accuracy and higher safety than doctors. Minimally invasive surgery is rapidly revolutionizing the treatment of traditional surgery. In order to solve the problem that the surgical robot has a redundant degree of freedom, which makes the kinematics solution more complicated, this paper analyzes the kinematics of the coordinate system block. Aiming at the problem that the strategy search algorithm needs to re-learn when the target pose changes, a convolutional neural network control strategy is studied and constructed. By designing the structure of the convolutional neural network visual layer and motor control layer, the loss function and sampling of the training process are established. Aiming at the problem of long training time of convolutional neural network, an effective pre-training method is proposed to shorten the training time of the neural network. At the same time, the effectiveness of the above method and the end-to-end control of the convolutional neural network strategy are verified through simulation experiments. The physical structure of the manipulator body is analyzed, and the forward and inverse kinematic equations of the manipulator are established by the D-H method. Monte Carlo method was used to analyze the working space of the manipulator, and low-latency control and simulation experiments were carried out on the movement trajectory of the manipulator in joint space and Cartesian space. The results show that the low-latency control algorithm in this paper is effective to control the mechanical arm of the minimally invasive medical surgery robot.

Dynamic performance analysis of parallel manipulators based on two-inertia-system

Load inertia time-varying is one of the main dynamic characteristics of parallel manipulators in joint space, which would heavily affect the performance of the mechatronic system, especially during the acceleration and deceleration phases. In this paper, a complete dynamic performance analysis method is presented by mainly considering the load inertia time-varying characteristic and using two-inertia-system. First, the control system of a parallel manipulator in joint space is established based on the two-inertia-system; next, the control parameters are designed by selecting appropriate damping ratio, and the variations of damping ratio and natural angular frequency are used to analyze the dynamic performance changing of parallel manipulators, moreover, the analysis step flow is also given; finally, by taking a 3-PRS parallel manipulator as an object of study, a series of simulations is given, including step response and trajectory tracking of angular velocity and displacement, and the results show that compared with the traditional method, the proposed method could be used to reflect the change of the dynamic performance caused by the load inertia time-varying characteristic of parallel manipulators more efficiently.

Modeling and Self-Learning Soft-Grasp Control for Free-Floating Space Manipulator During Target Capturing Using Variable Stiffness Method

During target capturing operation, the changes in the dynamics parameters of a free-floating space manipulator degrade the performance of the base attitude stabilization. This paper presents a new self-learning soft-grasp control algorithm based on the variable stiffness technology. First, the dynamic model of variable stiffness joint space manipulator system is established. Simultaneously, the detailed dynamic analysis of pre-impact and post-impact stages is carried out. Second, a new soft-grasp control strategy utilizing cellular differential evolution algorithm combined opposition-based learning with orthogonal crossover is employed to minimize the base angular momentum. Its principle is to solve the optimal stiffness value of the variable stiffness joint to realize desired buffering. Thereafter, we put forward an adaptive backstepping sliding mode control method to track the actual joint stiffness. Finally, the proposed method is applied to a two-degree of freedom planar free-floating space manipulator and the simulation results verify the effectiveness.

On the dynamics and control of flexible joint space manipulators

Space manipulator systems are designed to have lightweight structure and long arms in order to achieve reduction of fuel consumption and large reachable workspaces, respectively. Such systems are subject to link flexibilities. Moreover, space manipulator actuators are usually driven by harmonic gear mechanisms which lead to joint flexibility. These types of flexibility may cause vibrations both in the manipulator and the spacecraft making the positioning of the end-effector very difficult. Here, both types of flexibilities are lumped at the joints and the dynamic equations of a general flexible joint space manipulator are derived. Their internal structure is highlighted and similarities and differences with fixed-base robots are discussed. It is shown that one can exploit the derived dynamic structure in order to design a static feedback linearization control law and obtain an exact linearization and decoupling result. The application of such controllers is desired in space applications due to their small computational effort. In case of fixed-base manipulators, the effective use of a static feedback controller is feasible only if a simplified model is considered. Then, the proposed static feedback linearization control law is applied to achieve end-effector precise trajectory tracking in Cartesian space maintaining a desirable non-oscillatory motion of the spacecraft. The application of the proposed controller is illustrated by a planar seven degrees of freedom (dof) system.

Dynamic Modeling and Analysis of a Lightweight Robotic Manipulator in Joint Space

Abstract The primary importance of the paper is the application of the efficient formulation for the simulation of open-loop lightweight robotic manipulator. The framework employed in the paper makes use of the spatial operator algebra and the associated equations are expressed in joint space. This compact representation of the manipulator dynamics makes it possible to solve the robot forward and inverse dynamics problems in a recursive and fast manner. In the current form, the presented algorithm can be applied for the dynamics simulation of an open-loop chain system possessing any number of joints. Specifically, the formulation has been successfully applied for the analysis of the 7DOF KUKA LWR robot. Results from a number of test cases for the robot demonstrate the verification of the calculations

Robust fuzzy sliding mode control for tracking the robot manipulator in joint space and in presence of uncertainties

SUMMARYThis paper proposes a simple fuzzy sliding mode control to achieve the best trajectory tracking for the robot manipulator. In the core of the proposed method, by applying the feedback linearization technique, the known dynamics of the robot's manipulator is removed; then, in order to overcome the remaining uncertainties, a classic sliding mode control is designed. Afterward, by applying the TS fuzzy model, the classic sliding mode controller is converted to fuzzy sliding mode controller with very simple rule base. The mathematical analysis shows that the robot manipulator with the new proposed control in tracking the robot manipulator in presence of uncertainties has the globally asymptotic stability. Finally, to show the performance of the proposed method, the controller is simulated on a robot manipulator with two degrees of freedom as case study of the research. Simulation results demonstrate the superiority of the proposed control scheme in presence of the structured and unstructured uncertainties.