Robot Manipulator Control System Research Articles

Adaptive super twisting observer‐based prescribed time integral sliding mode tracking control of uncertain robotic manipulators

SummaryA novel integral sliding mode control (ISMC) strategy combined with an adaptive super twisting observer (ASTO) for an uncertain robotic manipulator tracking control system is presented in this article. The comprehensive uncertainties including both parameter perturbations and external disturbances are considered during the controller design. Firstly, a new nominal control law with prescribed time convergent property based on time varying scaling function is presented for the system without uncertainties. Then this nominal control law constitutes the prescribed time convergent sliding surface for ISMC. As the reaching phase is eliminated in ISMC, leading to the prescribed time stability of the whole control system without uncertainties. Secondly, take the system uncertainties (both the matched and unmatched uncertainties) into consideration, two ASTOs are designed for handling them. So, the lumped uncertainties of the robotic manipulator control system can be well estimated and compensated in finite time with the help of backstepping method. Besides, the finite time convergent adaptive switching gains of the ASTO make the system stable without knowing the bounds of the uncertainties exactly and suppress the chattering phenomenon of control input. Finally, the proposed control algorithm is validated by simulation and experiment on a robotic manipulator. Also, from a quantitative analysis, we testify the proposed control scheme outperforms the compared one in all of the discussed cases of simulation part.

Adaptive Parallel Iterative Learning Control with A Time-Varying Sign Gain Approach Empowered by Expert System

This study explores the incorporation of time-varying sign gain into a parallel iterative learning control (ILC) architecture, augmented by an expert system, to enhance the performance and stability of a robotic arm system. The methodology involves iteratively tuning the learning control gains using time-varying sign gain guided by an expert system. Stability analysis, encompassing asymptotic and monotonic convergence, demonstrates promising results across multiple joints, affirming the effectiveness of the proposed control architecture. In comparison with traditional PID control, fixed gain ILC, and ILC with adaptive learning in the expert system, the analysis focuses on stability, precision, and adaptability, using root mean square error (RMSE) as a key metric. The results show that ILC with adaptive learning from the expert system consistently reduces RMSE, even in the presence of learning transients. This adaptability effectively controls the learning transients, ensuring improved performance in subsequent iterations. In conclusion, the integration of time-varying sign gain with expert system assistance in a parallel ILC architecture holds promise for advancing adaptive control in robotic systems. Positive outcomes in stability, precision, and adaptability suggest practical applications in real-world scenarios. This research provides valuable insights into the implementation of dynamic learning mechanisms for enhanced robotic system performance, laying the groundwork for future refinement in robotic manipulator control systems.

Adaptive Single-Input Recurrent WCMAC-Based Supervisory Control for De-icing Robot Manipulator

The control of any robotic system always faces many great challenges in theory and practice. Because between theory and reality, there is always a huge difference in the uncertainty components in the system. That leads to the accuracy and stability of the system not being guaranteed with the set requirements. This paper presents a novel adaptive single-input recurrent wavelet differentiable cerebellar model articulation controller (S-RWCMAC)-based supervisory control system for an m-link robot manipulator to achieve precision trajectory tracking. This adaptive S-RWCMAC-based supervisory control system consists of a main adaptive S-RWCMAC, a supervisory controller, and an adaptive robust controller. The S-RWCMAC incorporates the advantages of the wavelet decomposition property with a CMAC fast learning ability, dynamic response, and input space dimension of RWCMAC can be simplified; and it is used to control the plant. The supervisory controller is appended to the adaptive S-RWCMAC to force the system states within a predefined constraint set and the adaptive robust controller is developed to dispel the effect of the approximate error. In this scheme, if the adaptive S-RWCMAC can not maintain the system states within the constraint set. Then, the supervisory controller will work to pull the states back to the constraint set and otherwise is idle. The online tuning laws of S-RWCMAC and the robust controller parameters are derived from the gradient-descent learning method and Lyapunov function so that the stability of the system can be guaranteed. The simulation and experimental results of the novel three-link De-icing robot manipulator are provided to verify the effectiveness of the proposed control methodology. The results indicate that the proposed model has superior accuracy compared to that of the Standalone CMAC Controller. The parameters of the average squared error in the S-RWCMAC -based 3 robot joints are lower than those of the Standalone CMAC Controller by 0.023%, 0.029%, and 0.032%, respectively.

Calculation of Angular Coordinates for the Control System of a Two-Link Industrial Robot Manipulator

Introduction. One of the tasks of two-link manipulators of industrial robots that move the end-effector along complex trajectories (e.g., robot welder) is associated with the need for careful programming of their movement. For these purposes, manual programming methods or training methods are used. These methods are quite labor-intensive, and they require highly qualified service personnel. A possible solution to the problem of programming the manipulator movements is the simulation of motion with the calculation of angular coordinates. This can help simplify the geometric adaptation of the manipulator in the process of debugging the control program. Therefore, this work aimed at calculating coordinates for programming the control system of a two-link manipulator operating in an angular coordinate system and moving the end-effector along a complex trajectory (e. g., when welding car bodies). Materials and Methods. A two-link robot manipulator designed for cyclically repeating actions in an angular coordinate system was considered. The manipulator consisted of two rotating links: “arm” and “elbow”, which were fixed on the base. The base could rotate, which provided a third degree of freedom. This configuration increased the working area of the manipulator and minimized the area for its placement in production. The movement of the manipulator end-effector could be performed if the kinematics provided its positioning along three Cartesian and three angular coordinates. For software control of robots, including welding robots operating in an angular coordinate system and performing the movement of the end-effector along a complex trajectory, it was required to calculate the angular coordinates of the movement of the end-effector of a two-link articulated manipulator. The robot control system should determine the position of the tool in the angular coordinate system, converting it for user friendliness into x, y and z coordinates of the Cartesian coordinate system. Results. The relations of angular and Cartesian coordinates have been obtained. They can be used for calculating when programming the control system of a two-link manipulator of an industrial robot and organizing the exchange of information between the user and the control system, as well as for checking the accuracy and debugging the movement of the end-effector of an industrial robot through feedback. Discussion and Conclusion. The presented results can be used for software control of a welding robot operating in an angular coordinate system and performing a complex trajectory of the end-effector of a two-link articulated manipulator (gripper). A manipulator operating in an angular coordinate system can be used for contact spot welding when moving the end-effector along a complex trajectory using a positioning or contouring control system. These systems control the movement of the end-effector along a given trajectory with the help of technological commands.

Development of a novel parallel soft manipulator with sensing capabilities

It is challenging to equip a soft manipulator with sensors without compromising its mechanical properties. In this paper, we explain the design, analysis, and building of an innovative, low-cost soft parallel manipulator with sensing and actuation capabilities. Innovative bubble artificial muscle actuators serve as the connecting rod and contraction type actuator for the manipulator, as well as an opto-mechanical tactile sensor (TacTip) for objects detection. We propose a simulation method for the key structural design parameters of TacTip, and the influence of TacTip’s pin height, pin diameter, and surrounding pin angle on perceptual sensing has been examined via testing, which can provide a design guideline for the TacTip’s the structure. The impact of the counterweight on the soft parallel manipulator end-ascending effector’s and descending velocity, as well as its vertical and horizontal workspace, has been the topic of dynamic tests using a range of counterweights. The soft parallel manipulator’s workspace has been analyzed. In addition, an innovative, cost-effective, and simple soft-smart parallel robotic manipulator (PSM) control system has been built, and a demonstration of the detection and movement of the soft parallel manipulator is shown. The results demonstrate that the soft parallel manipulator described in this research is capable of detecting motion and collisions. It is anticipated that this soft-smart PSM would expand the employment of optical tactile sensors and artificial muscle actuators in material handling and size/shape sorting assembly lines.

Design of the MVT RBF neural network robotic manipulator control system based on model block approximation

Due to the uncertain dynamic characteristics, the requirements for robotic manipulator control are increasingly complex. The traditional radial basis function (RBF) neural network has a good generalization ability, but its redundant and tedious training process cannot meet the “Intelligent” control requirement of robotic manipulator. This study designs a new valve-regulated memristive RBF neural network, which adopts the model block approximation control strategy to estimate the three coefficient matrices of the robotic manipulator and uses the memristor with voltage threshold (MVT) as an electronic synapse to provide connections between neurons for the neural network and store information. This study adopts the design idea of software hardening and replaces the updated neural network weight with the change of the memristance value in the MVT network (crossed array), which can effectively improve the control performance of the traditional RBF neural network and can also provide analytical data for the fault detection of the subsequent control system. A simulation analysis is conducted with a single-joint robotic manipulator as the control object, and the results verify the rationality and feasibility of the proposed control algorithm.

DESIGN OF A TWO-LINK ROBOT MANIPULATOR ADAPTIVE CONTROL SYSTEM BY THE GAIN SCHEDULING METHOD

The application areas of robot manipulators are increasingly growing year by year. Rehabilitation exoskeletons and upper limb prostheses are special cases of robot manipulators. Such systems, being nonlinear, multidimensional, and dynamic systems, have parametric uncertainties. All this becomes a challenge in the design process of the control system. One of the advanced technologies in this field is adaptive control, which is an important and interesting area of scientific and industrial research. The nonlinear control system of the two-linked robot manipulator was considered in the work. In this case, classical control methods become inapplicable and there is a need to develop and apply other control methods. Using the gain scheduling method, a group of PID controllers are designed to ensure the stability of the system at various operating points. The non-linearity of the system is compensated by the gain scheduling method and the only problem that remained is the uncertainties of the system depending on the weight of the operator. A model reference adaptive controller (MRAC) was designed and augmented with the gain scheduling method for eliminating uncertainties. Simulation is done in the MATLAB Simulink environment. A comparative performance has been quantified with different adaptation gains for the upper limb prosthesis system. The obtained results show the viability of the proposed control method.

Hybrid Decentralized Nonlinear Control System for Manipulation Robot with Input Saturations

The paper is devoted to consideration of the problem of constructing a discrete-continuous decentralized nonlinear combined control system for two-link robotic manipulator with restrictions in control signals. The mathematical model of the manipulator is presented as a two-channel dynamic plant with nonlinear cross-links. As methods for solving this problem V. M. Popov’s hyperstability criterion and the conditions of L-dissipativity are used. With the help of simulation, the quality of the proposed control system is shown. The results obtained in article may be useful for construction the control systems for manipulators with many degrees of freedom and also for control various mechanical systems.

AI 기반의 용접로봇 3D 비전모듈 및 시스템

This paper proposes an AI-based welding robot 3D vision module and manipulator control system for welding automation. With conventional welding robots, welding is performed only at the designated welding points through robot teaching; however, the difficulty with this method is that the position of the welding point changes due to the tolerance (gap) of the welding object. To solve this problem, we automatically recognized the welding points using a deep learning-based object detection model called YOLOv4. The time required for labeling was thus reduced through a deep learning labeling tool that uses an existing learned weights file. To control the robot with the recognized welding point, a hand/eye calibration method calculating the homogeneous transformation matrix between the robot and the camera was adopted. Additionally, to correct the final robot control error caused due to calibration and camera depth errors, the control precision at the welding point was improved through interpolation method. The system has been verified by conducting sufficient number of experiments in a non-destructive formwork welding environment.

Design of Picking Robot Manipulator Control System Based on Fuzzy Compensation RBF Neural Network

Industrial manipulator occupies a very important position in industrial production. The tracking control of its control system and joint trajectory has always been a research hotspot. But the manipulator is a multi input multi output system, which has the characteristics of nonlinearity and strong coupling. Radial basis function (RBF) neural network has high nonlinear mapping ability. In this paper, the structure characteristics, learning algorithm and application of RBF neural network in manipulator control are analyzed. In this paper, the nonlinear approximation property of RBF neural network is theoretically verified. This paper analyzes the basic structure of picking manipulator system in detail. At the same time, the Lagrange Euler method is used to deduce the dynamic equation of the two degree of freedom series manipulator, and the inertia characteristics, Coriolis force and centripetal force characteristics, heavy torque characteristics are analyzed. The nonlinear system model of manipulator based on S-function is established in MATLAB, and the dynamic model is transformed into the form of second-order differential equation to facilitate the introduction of the designed algorithm.

Repetitive Control Scheme of Robotic Manipulators Based on Improved B-Spline Function

In this paper, a repetitive control scheme of a 2-DOF robotic manipulator based on the improved cubic B-spline curve is proposed. Firstly, a repetitive controller for robotic manipulator is designed, which is composed of an iterative controller and disturbance observer. Then, an improved B-spline optimization scheme is introduced to divide the task of the robotic manipulator into three intervals. A correction function is added to each interval of cubic spline interpolation. Finally, a variety of cases are designed and simulated by MATLAB. The experimental results show that, compared with the conventional B-spline, the improved B-spline has better performance in tracking accuracy and smoothness of motion trajectory. By changing the mechanism of the manipulator, the cases of different weights and lengths are designed. The experimental results in these cases show that the proposed scheme can be applied to most of the 2-DOF robotic manipulator control systems.

Dynamic event‐triggered approach for networked control systems under denial of service attacks

AbstractThis article studies the problems of exponential stabilization and ‐gain performance for networked control systems (NCSs) with transmission delays and periodic denial‐of‐service (DoS) attacks by exploring a resilient event‐triggered communication mechanism. First, a new resilient event‐triggered mechanism is developed to eliminate the adverse effects of network congestion caused by DoS attacks and decrease redundant communication so as to ensure efficient utilization of the limited network resources. The threshold parameter in the predefined event‐triggered condition can be adjusted according to the dynamic characteristics of the system. Then, an event‐driven control protocol is proposed, and a new switched NCS model is constructed. Sufficient conditions are then presented to ensure the exponential stability and ‐gain performance of the resulting closed‐loop system. Moreover, a co‐design scheme of the parameters in the event‐triggered condition and the controller gain is provided. Finally, the effectiveness and advantages of the new design techniques are verified through a comparative study for a robot manipulator control system.

Improved neural network-based adaptive tracking control for manipulators with uncertain dynamics

In this article, a robust adaptive tracking controller is developed for robot manipulators with uncertain dynamics using radial basis function neural network. The design of tracking control systems for robot manipulators is a highly challenging task due to external disturbance and the uncertainties in their dynamics. The improved radial basis function neural network is chosen to approximate the uncertain dynamics of robot manipulators and learn the upper bound of the uncertainty. The adaptive law based on the Lyapunov stability theory is used to solve the uniform final bounded problem of the radial basis function neural network weights, which guarantees the stability and the consistent bounded tracking error of the closed-loop system. Finally, the simulation results are provided to demonstrate the practicability and effectiveness of the proposed method.

ANALYSIS OF THE SPEED OF THE DEFUZZIFIER IN THE TASK OF CONTROLLING THE ROBOT-MANIPULATOR

Linear, nonlinear, modified, high-speed defuzzifiers based on the area ratio method are presented in this paper. The proposed defuzzifiers are used in a fuzzy digital filter device and make it possible to ensure the additivity of the robotic manipulator control system, since traditional models do not have this property. The essence of this development is to find a crisp value of the output fuzzy variable, which in this case are the regulation coefficients of the fuzzy digital filter. Reducing the number of computational operations provides an increase in the performance of the defuzzifier. The reduction in number of computational operations is carried out by eliminating the output variable’s truncated term’s height calculation, thereby reducing the computation time. A simulation model which was implemented in the MatLab Simulink system, for a neuro-fuzzy device of the robotic arm using linear and non-linear defuzzifiers is presented. The dependence of time graphs on the angle of rotation of the joints of the robotic manipulator are compared, based on the traditional center of gravity method and the method shown in this paper. It was found that the traditional center of gravity method does not ensure the fulfillment of the specified rotation angles of the links of the robotic arm, while the proposed models of defuzzifiers have this property, which can be seen from the presented dependency graphs. The simulation model of the device was also designed as a parallel-conveyor device for implementation in the field-programmable gate array of the Xilinx Spartan 3Е family. The analysis showed that the calculation time for a crisp value with high-speed defuzzification is 130 ns, which is two orders of magnitude higher than existing models. The experiment was conducted at a frequency of 100 MHz.

Intuitive gesture-based control system with collision avoidance for robotic manipulators

PurposeThis paper aims to present an intuitive control system for robotic manipulators that pairs a Leap Motion, a low-cost optical tracking and gesture recognition device, with the ability to record and replay trajectories and operation to create an intuitive method of controlling and programming a robotic manipulator. This system was designed to be extensible and includes modules and methods for obstacle detection and dynamic trajectory modification for obstacle avoidance.Design/methodology/approachThe presented control architecture, while portable to any robotic platform, was designed to actuate a six degree-of-freedom robotic manipulator of our own design. From the data collected by the Leap Motion, the manipulator was controlled by mapping the position and orientation of the human hand to values in the joint space of the robot. Additional recording and playback functionality was implemented to allow for the robot to repeat the desired tasks once the task had been demonstrated and recorded.FindingsExperiments were conducted on our custom-built robotic manipulator by first using a simulation model to characterize and quantify the robot’s tracking of the Leap Motion generated trajectory. Tests were conducted in the Gazebo simulation software in conjunction with Robot Operating System, where results were collected by recording both the real-time input from the Leap Motion sensor, and the corresponding pose data. The results of these experiments show that the goal of accurate and real-time control of the robot was achieved and validated our methods of transcribing, recording and repeating six degree-of-freedom trajectories from the Leap Motion camera.Originality/valueAs robots evolve in complexity, the methods of programming them need to evolve to become more intuitive. Humans instinctively teach by demonstrating the task to a given subject, who then observes the various poses and tries to replicate the motions. This work aims to integrate the natural human teaching methods into robotics programming through an intuitive, demonstration-based programming method.

Dynamic Visual Tracking for Robot Manipulator Using Adaptive Fading Kalman Filter

This paper focuses on the problem of visual tracking of a moving target with the temporary occlusion of image feature, a dynamic visual tracking control system for robot manipulator is developed by using adaptive fading Kalman filter (AFKF). The estimation of the residual covariance is used to compute the forgetting factor to automatically adjust the weight of the image observation data for improving the visual state estimation accuracy. When the target features are occluded, the prediction of missing observation sequence are generated by using the predicted compensation noise and preorder observation sequence to determine the forgetting factor for estimating the missing visual states. Then, a parameter adaptive law with projection error compensation is designed to realize the visual tracking with uncertain camera parameters. Finally, the trajectory tracking experiments based on a real robot platform is carried out to verify the performance of the proposed state estimator and tracking controller. The results show that the proposed method can accurately realize the visual tracking with the occluded trajectory and inaccurate camera parameters, which improves the flexibility of dynamic visual tracking of robot manipulator.

Hybrid tuning of a robotic manipulator controller with a database

In this paper a method of adaptation with hybrid tuning of a robotic manipulator control system with PD controller is considered. The adaptations of the controller parameters and compensating signal are carried out online and independent using modified simplex search algorithms. To increase the performance of the adaptive control system, the values obtained during adaptation process are stored in a database. The database stores these values and the system state, at which they were obtained during the training phase, and in the control phase the database returns the values according to the current state of the system. Efficiency of the proposed methods is verified by simulations. The results show advantage of the adaptive control system with the database.

Development of system of automatic control of robotic arm drive

Currently, the scope of application of robotic systems and complexes is expanding significantly. The introduction of industrial robots is one of the most promising areas of complex automation of production processes, which will significantly increase labor productivity and save people from monotonous, difficult and harmful work. Many tasks are solved by facilitating robotic manipulators to facilitate human performance and to achieve higher quality and accuracy of production. Thus, thanks to the development of automatic control systems, information technologies and their application in the creation of modern robotic systems, the opportunity has emerged to release a person from work that poses a threat to his life and health. When using manipulator robots, an important task is to develop a control system that provides positioned movement of the links of the manipulator. The main task of the automatic drive control system is to form and implement a set of control effects, in accordance with technological requirements. In this work we propose a system of automatic control of the actuator of the manipulator, which provides positioned movement of the manipulator link along a given trajectory. Using the tables of originals and images of typical functions for which continuous Laplace transforms and Z-transforms are calculated, a discrete linear steady-state mathematical model of the robot-manipulator automatic control system is constructed. The parameters of the corrective devices of the system of automatic control of the electric actuator of the robot manipulator were made, which allowed to improve the characteristics of the transient process, namely: to reduce the time of over-regulation and the duration of the transient. The obtained results of the simulation of transients allow to confirm that the synthesized parameters of the corrective devices for the analog automatic control system can be used in the digital automatic control system of the electric actuator of the robot manipulator.

Robot Manipulator Control System with Dynamic Moment Compensation

The paper offers the variants of manipulator control system design with PID and polynomial controllers. A manipulator dynamics model is used for compensating the moments affecting its sections during the motion.

PID Control for a Manipulator Robot Using Internet Networking and Matlab-based

This paper presents the design to the implementation of the proportional, integral and derivative (PID) remote control system of robot manipulator using Matlab-based Internet network. These studies seek the solution of distance operations, time constraints, and monitoring the operation response of robot manipulator motion as robot that possible to carry dangerous objects which is should be monitored remotely during the operation. PID control system that is controlled remotely by the client and server systems. The monitoring process of robot manipulator is Matlab-based. Testing is done by doing observation to the speed response of the DC motor as the robot’s axis driver on open loop system condition, and then doing observation using PID control system. The test of axis robot driver has also been carried out with a variety of no-load and load conditions. The test results showed the motor driver of robot will work optimally when using PID control system. The data acquisition and the stability of data communication between the client and server are affected by the condition of the existing data transfer on the network and also the distance from the server to the plant.