Control Of Parallel Robot Research Articles

Real-time fuzzy position control and design of Star parallel robot

Real-time fuzzy position control and design of Star parallel robot

Fuzzy adaptive impedance control for the two-layered vertical cable-driven parallel robot

This study unveils a novel two-layered vertical octahedron cable-driven parallel robot (TLVO CDPR), distinctively engineered for effective force interactions with vertical surfaces while preventing collision with cables. It pioneers an innovative control strategy integrating a position-based fuzzy adaptive impedance controller with a fuzzy Proportional – Integral – Derivative (PID) controller, adeptly managing both the pose and contact force of the robot. While dual control application is often found in rigid-link robots, it remains a largely unexplored frontier in the realm of CDPRs, despite its critical importance in sectors like manufacturing and assembly. The fuzzy adaptive mechanism significantly boosts impedance control efficacy in the face of unpredictable, non-uniform working surfaces, ensuring algorithmic stability and convergence. Concurrently, fuzzy logic is harnessed to optimize PID controller performance. The forward kinematics challenge is efficiently tackled using a least squares method coupled with an Inertial Measurement Unit (IMU), ensuring swift and precise solutions. The robustness and adaptability of the robot and its control systems are thoroughly validated through extensive experimental trials, involving diverse trajectories and varying uncertainties on vertical working surfaces.

Research on trajectory tracking control of delta high-speed parallel robot based on PTNTSMC

Research on trajectory tracking control of delta high-speed parallel robot based on PTNTSMC

Soft Robots: Computational Design, Fabrication, and Position Control of a Novel 3-DOF Soft Robot

This paper presents the computational design, fabrication, and control of a novel 3-degrees-of-freedom (DOF) soft parallel robot. The design is inspired by a delta robot structure. It is engineered to overcome the limitations of traditional soft serial robot arms, which are typically low in structural stiffness and blocking force. Soft robotic systems are becoming increasingly popular due to their inherent compliance match to that of human body, making them an efficient solution for applications requiring direct contact with humans. The proposed soft robot consists of three soft closed-loop kinematic chains, each of which includes a soft actuator and a compliant four-bar arm. The complex nonlinear dynamics of the soft robot are numerically modeled, and the model is validated experimentally using a 6-DOF electromagnetic position sensor. This research contributes to the growing body of literature in the field of soft robotics, providing insights into the computational design, fabrication, and control of soft parallel robots for use in a variety of complex applications.

High-Precision Adaptive Control of Cable-Driven Parallel Robots With Convergence Guarantee

High-Precision Adaptive Control of Cable-Driven Parallel Robots With Convergence Guarantee

Control of planar parallel robots by applying distinct hybrid techniques in the task space

Control of planar parallel robots by applying distinct hybrid techniques in the task space

Dynamic modeling and adaptive finite-time control of cable-driven parallel robots for ship cleaning

ABSTRACT Ship cleaning, often manually performed by numerous workers, is crucial in the shipping industry. This study introduces a marine cable-driven parallel cleaning robot (MCDPCR) to enhance cleaning security and efficiency. To investigate trajectory tracking under the moving base, the dynamic model of MCDPCR is established, and the optimisation of cable forces is considered. Then, an adaptive terminal sliding mode controller is designed, integrating the discontinuous sign function term to obtain a continuous actual control law and thus a chattering-free response. Meanwhile, the uncertainty is compensated by an adaptive method. The stability of the control strategy is analyzed using the Lyapunov stability theory. Finally, the effectiveness of the proposed control strategy is verified by several simulation scenarios. The results show that the controller has a fast global convergence speed, high tracking accuracy, and strong robustness. The method can provide a valuable reference for developing automated marine cleaning equipment.

Sensorless admittance control of 6-DoF parallel robot in human-robot collaborative assembly

This paper presents an adaptive admittance control to achieve human-robot collaborative (HRC) assembling of large, heavy components without using external sensors. This sensorless adaptive admittance control is constructed by the dynamic model of a six degree-of-freedom (DoF) parallel robot based on finite and instantaneous screw (FIS) theory. The dynamic model is intuitive and easy to be programmed. After getting stable actuation force by Kalman filter and identifying friction by LuGre model, accurate force estimator is built. Admittance controller is adopted to convert the change of estimated force to the change in position, velocity and acceleration. As a result, the 6-DoF parallel robot is capable of following the guidance of operator. Particularly, adaptive admittance control is design by using robot velocity as reference. A fast motion before contacting and a fine motion during contacting are allowed. Experiments are conducted to verify force estimator, admittance control and adaptive admittance control for HRC assembly. Results show that external force is accurately estimated. Robot complaint control in response to operator's force is achieved. The adaptive admittance control enhances execution time, accuracy of the HRC assembly.

Novel and Robust Forward Kinematic Algorithm for Real-Time Control of General Six-Degree-of-Freedom Parallel Robot for Tele-Manipulation and Tele-Navigation

Abstract The forward kinematics (FK) of a 6-6 universal-prismatic-spherical (UPS) structure of a parallel robot is highly nonlinear, coupled, and has a one-to-many nature of mapping. There exists no close form solution to a forward kinematic problem (FKP), and real-time kinematic control is extremely difficult. This paper presents the implementation of time efficient and robust solution for FKP using a trajectory modifier algorithm along with a Newton Raphson (NR) method. One micrometer in translation and 0.001 deg in orientation accuracy with an average pose computation time of 2.3 ms are achieved. The novel algorithm is elaborated and the detailed performance parameters are tabulated. The paper presents trajectory following experiments to show robust, real-time FK solution and efficient kinematic control on both standalone and master–slave modes to be used for robot-assisted neurosurgery. The neuro-registration using the FK solutions in real time in a tele-manipulation mode is demonstrated.

Noniterative Positive Constrained Control of Cable-Driven Parallel Robots

Noniterative Positive Constrained Control of Cable-Driven Parallel Robots

Fuzzy adaptive control of coal gangue sorting parallel robot with variable load

ABSTRACT Delta parallel robot is widely used in the high-speed sorting industry with a light load and constant load. Its motion control is based on kinematic planning, without considering the influence of load and disturbance. The gangue sorting robot has the characteristics of a large load and random mass change, so traditional kinematics planning can not meet the control requirements. A fuzzy adaptive robust control algorithm is proposed to solve the problems of unstable motion and jitter in the coal gangue sorting robot. By establishing the mathematical model of the fuzzy system, the uncertain terms of the dynamic parameters caused by the random changes in the load are approximated. At the same time, to reduce the number of fuzzy rules and the amount of fuzzy calculation, the uncertain terms are decomposed into velocity terms and acceleration terms and then approximated, respectively. The Lyapunov analysis of the system is carried out, and the convergence of the system is proved. The simulation results show that the fuzzy adaptive robust control algorithm has a smaller error and faster convergence compared with the computational torque control algorithm when the system is sorting gangue within 8 kg, which improves the stability of gangue grasping.

Adaptive Position Feedback Control of Parallel Robots in the Presence of Kinematics and Dynamics Uncertainties

Uncertainties in the kinematic and dynamic parameters of a parallel robot are unavoidable. The problem is more crucial in the cases where the manipulator interacts with the environment and when it is large–scale or deployable. Furthermore, precise measurement of the velocity of the end-effector is almost inaccessible in practice. This paper addresses the above shortcomings by designing of an adaptive trajectory tracking controller with merely position feedback of joint and task space variables. Simplicity of implementation, separation of adaptation laws of dynamic and kinematic parameters, and reduction of the number of adaptation laws such that in some cases, e.g., cable-driven robots, it is identically equivalent to the number of unknown parameters are some advantages of the proposed controller. The method’s efficiency is shown via implementation on a cable-driven parallel manipulator and an intraocular surgery robot. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In order to achieve a suitable response in parallel robots with traditional controllers, a precise knowledge of kinematic and dynamic parameters, together with accurate measurement of velocity, is required. In practice, these values are usually derived by robot calibration and identification. Since the system’s parameters may alter or depend on external factors such as temperature, these time-consuming methods should be implemented repeatedly while the robot is out of duty. Additionally, precise velocity measurement is a prohibitive task and requires expensive instruments. This article presents a controller to address the above shortcomings. By this means, a simple adaptive controller based on position feedback is designed such that via suitable estimation of kinematic and dynamic parameters, trajectory tracking is obtained without the need for accurate initial estimates of the parameters. The proposed method has a number of advantages, including separation of the adaptation laws of kinematic parameters and dynamic parameters, simple representation of the Jacobian matrix in regressor form, and there is no requirement for velocity feedback. Furthermore, a force distribution method is introduced for redundant robots. Hence, the proposed controller is an appropriate alternative to traditional controllers widely used in industries.

Trajectory Tracking Control of Fast Parallel SCARA Robots with Fuzzy Adaptive Iterative Learning Control for Repetitive Pick-and-Place Operations

Aiming at enhanced suppression of external disturbances and high-precision trajectory tracking of parallel SCARA robot dedicating to fast pick-and-place operations, this work presents the integrated control design of iterative learning algorithm, adaptive control and fuzzy rules, namely, fuzzy adaptive iterative learning control, for such type of robots. A step-design approach is adopted to ensure the adaptability of the designed control law, which is reflected in two aspects: ① the feedback gain of the controller is adjusted by the fuzzy rules; ② the adaptive unknown parameters are obtained by means of iterative learning estimation to suppress the uncertainties and external disturbances. The stability of the designed controller is analyzed and proved by the Lyapunov theory, and the effectiveness is verified by observing the tracking errors in joint space along with the testing pick path, in comparison with different iterative learning based algorithms. After the first-iteration learning, the motion errors of the four actuated joints can be reduced by 56.5%, 45.8%, 46.4% and 39.8%, respectively, and after 15 iterations of learning control, the final angular errors by the designed control law converge to 0.7×10−4 degree maximally. The varying maximum, root-mean-squared and mean angular displacement errors of the actuation joints can converge to zero values with the increasing iterations rapidly, which shows the robustness, effectiveness and advantages of the designed control law. The designed control law can be generalized to high-speed parallel pick-and-place robot to ensure high-precision trajectory tracking for high-quality material handling tasks.

Position-based visual servoing of a 6-RSS parallel robot using adaptive sliding mode control

The trajectory tracking control of parallel robots is challenging due to their complicated dynamics and kinematics. This paper proposes a position-based visual servoing (PBVS) approach for a 6-Revolute-Spherical-Spherical (6-RSS) parallel robot using adaptive sliding mode control in Cartesian space. A photogrammetry sensor C-Track 780 in the eye-to-hand configuration is adopted to measure the real-time pose of the robot end-effector, which can avoid the calculation of robot forward kinematics and provide more flexibility for controller design. An adaptive Kalman filter is utilized to deal with uncertain noises in visual measurements to increase the pose estimation accuracy. A sliding mode controller with strong robustness is designed to cope with system uncertainties, and a radial basis function (RBF) neural network is incorporated to realize the auto-tuning of the control gains, which make the robot effectively track different trajectories with time-varying conditions in real applications. Based on Lyapunov theorem, the stability analysis of the controller has been done. Experiments have been conducted to validate the effectiveness of the proposed strategy and illustrate the superiority of the designed controller.

Jerk-Limited Time-Optimal Model Predictive Path Following Control of Cable-Driven Parallel Robots

Jerk-Limited Time-Optimal Model Predictive Path Following Control of Cable-Driven Parallel Robots

Evaluating cable tension distributions of CDPR for virtual reality motion simulator

This paper presents a study on modeling, analysis, and control of an over-constrained Cable-Driven Parallel Robot taking into account the deformation of the cable transmission system due to the elastic model of the transmission mechanism and the affection of tension distribution for cables. The Cable-Driven Parallel Robot is used for a virtual reality motion simulation system with a simulation cabin mounted on a moving platform. A nonlinear cable length controller with tension feedback is designed to control the cabin to move along a trajectory extracted from the virtual simulated environment. The tension distribution algorithm is integrated into the controller to compensate for the dynamic error caused by the two redundant cables and the elastic characteristic of the transmission mechanism. The tensions are calculated based on the constraints of the workspace, the structure of the system, and the force limits of actuators. The cable tension feedback control algorithm was tested on the concept Cable-Driven Parallel Robot, the experimental results show that the joint trajectories meet the desired moving trajectory with high accuracy, and the cable tensions are controlled in the optimal region to ensure safety and save energy, but still suitable for the control requirements of the virtual reality motion simulator system.

Research on workspace visual-based continuous switching sliding mode control for cable-driven parallel robots

AbstractAchieving the high-precision control of cable-driven parallel robots (CDPRs) is complex because of their structural properties. In this paper, a quintessential redundant CDPR is designed as the research subject, and a continuous switching sliding mode controller based on workspace vision is implemented to enhance the accuracy and stability of trajectory tracking. In addition, a virtual prototype of the CDPR with uncertainties is created in the simulation analysis software ADAMS, and co-simulation is performed with the control system designed in Simulink to validate the effectiveness of the proposed control strategy. Furthermore, a CDPR platform is established for trajectory tracking experiments using the visual-based position feedback method. The trajectory tracking performance with the three control schemes is then evaluated. The experimental results show that the continuous switching sliding mode control algorithm can significantly decrease trajectory tracking errors and exhibit superior trajectory tracking performance compared to the other control strategies.

Research on Active Disturbance Rejection Algorithm for Loading Control of 3-DOF Parallel Robot

A three-dimensional (3D) loading device based on a pneumatic 3-universal-prismatic-universal (UPU) parallel manipulator (three chains with UPU joint each) is designed to apply time-varying multi-dimensional loads to a target. First, according to the principle of vector superposition and Newton–Raphson method, the inverse and forward kinematics of the loading device are analyzed. Second, based on the screw theory, the static mapping between the dynamic platform and actuations is derived. Third, a second-order mathematical model is established for pneumatic 3-UPU parallel mechanism based on proportional flow valve and a metal seal pneumatic cylinder. A referential inaccurate model is provided for the control algorithm during modeling. Fourth, based on active disturbance rejection control (ADRC) technique, a 3D loading control algorithm is proposed and dynamic loading control is realized. Experimental results show that the ADRC algorithm has good robustness against disturbances, the steady-state control accuracy is less than 2 N, and the mean square error in dynamic tracking (sinusoidal tracking at 0.2 Hz) is less than 10.5 N.

Robust optimal constrained control of fully-constrained cable-driven parallel robots based on GSDRE

Robust optimal constrained control of fully-constrained cable-driven parallel robots based on GSDRE

Deep Reinforcement Learning Control of Fully-Constrained Cable-Driven Parallel Robots

Cable-driven parallel robots (CDPRs) have complex cable dynamics and working environment uncertainties, which bring challenges to the precise control of CDPRs. This paper introduces the reinforcement learning to offset the negative effect on control performance of CDPRs resulting from the uncertainties. The problem of controller design for CDPRs in the framework of deep reinforcement learning is investigated. A learning based control algorithm is proposed to compensate for uncertainties due to cable elasticity, mechanical friction, etc. A basic control law is given for the nominal model, and a Lyapunov-based deep reinforcement learning control law is designed. Moreover, the stability of the closed-loop tracking system under the reinforcement learning algorithm is proved. Both simulation and experiments validate the effectiveness and advantages of the proposed control algorithm.