Dynamics Of Robotic System Research Articles

Learning and Uncertainty Compensation in Robotic Motion Systems using Li-Slotine Adaptive Tracking and Intelligent Adaptive Control

This paper examines adaptive control strategies for stabilizing robots, focusing on Li-Slotine adaptive control and Iterative Learning Control (ILC). Both methods handle uncertainties through learning and compensation, ensuring stability and precision. Li-Slotine control, based on Lyapunov theory, dynamically adjusts parameters for asymptotic stability in uncertain systems. ILC improves performance in repetitive tasks by refining control inputs using tracking errors, making it suitable for robotics and manufacturing. While Li-Slotine excels in real-time adaptation and robustness to disturbances, its computational demands challenge high-degree-of-freedom systems. ILC enhances accuracy through iterative learning but is sensitive to noise and requires careful tuning. MATLAB simulations and experimental results demonstrate the effectiveness of both approaches. Future work will explore hybrid frameworks that combine the adaptability of Li-Slotine with the data-driven refinement of ILC to provide robust solutions for complex, dynamic robotic systems.

An innovative finite-time leader-follower consensus control for a nonlinear mechanical disturbed system with actuation saturation

ABSTRACT This paper presents a novel leader-follower consensus control method for a special class of nonlinear multi-agent mechanical systems in the presence of control input saturation and external disturbances, and this class includes robot system dynamics with a wide range of potential applications in industry. In the innovative technique, one of the agents is selected as the leader and the other agents are guided in such a way that the entire states of the system can reach a consensus within the transient limits of the determined performance. Due to the presence of disturbance in most practical systems, the effect of limited disturbance has been evaluated in the consensus control method and actuator saturation is included in the design process of the control technique. Finally, a terminal sliding mode control method is adapted to ensure the stability of the overall system and fast finite-time leader-follower consensus control. The simulation results of the multi-agent nonlinear robot system in the MATLAB environment, in different scenarios by simultaneously considering the actuator saturation and external disturbance, show the effectiveness of the proposed control method.

CONTACT PROBLEM FOR A SPHERICAL SHELL SUPPORTED ON TWO FLAT SURFACES

An effective numerical and analytical method is proposed for studying the force state inside the contact spots of elastic composite spherical shells resting on two flat surfaces inclined at an angle to each other. The method is based on constructing the influence function for the shell in an axisymmetric formulation, followed by its expansion into a Fourier series using the Legendre and Gegenbauer polynomials. A resolving system of equations for the corresponding coefficients is obtained. The developed methodology will allow for a more correct study of the dynamics of various robotic systems, the main controlled elements of which are elastic spherical shells that carry out complex kinematic movements on various surfaces. These systems, in particular, include a “butterfly robot”, the main controlled element of which is a thin spherical shell, rolling with simultaneous spinning and sliding along two parallel plates, as if on rails.

Simulation and time series analysis of responsive active Brownian particles (rABPs) with memory

To realise the goals of active matter at the micro- and nano-scale, the next generation of microrobots must be capable of autonomously sensing and responding to their environment to carry out pre-programmed tasks. Memory effects are proposed to have a significant effect on the dynamics of responsive robotic systems, drawing parallels to strategies used in nature across all length-scales. Inspired by the integral feedback control mechanism by which Escherichia coli (E. coli) are proposed to sense their environment, we develop a numerical model for responsive active Brownian particles (rABP) in which the rABPs continuously react to changes in the physical parameters dictated by their local environment. The resulting time series, extracted from their dynamic diffusion coefficients, velocity or from their fluctuating position with time, are then used to classify and characterise their response, leading to the identification of conditional heteroscedasticity in their physics. We then train recurrent neural networks (RNNs) capable of quantitatively describing the responsiveness of rABPs using their 2D trajectories. We believe that our proposed strategy to determine the parameters governing the dynamics of rABPs can be applied to guide the design of microrobots with physical intelligence encoded during their fabrication.

Task-space tracking for networked heterogeneous robotic systems via adaptive neural fixed-time control

The task-space distributed adaptive neural network (NN) fixed-time tracking problem is studied for networked heterogeneous robotic systems (NHRSs). In order to address this complex problem, we propose a NN-based fixed-time hierarchical control approach that transforms the problem into two sub-problems: a distributed fixed-time estimation problem and a local fixed-time tracking problem, respectively. Specifically, distributed estimators are constructed so that each follower can acquire the dynamic leader’s state in a fixed time. Then, the neural networks (NNs) are employed to approximate the compounded uncertainty consisting of the unknown dynamics of robotic systems and the boundary of the compounded disturbance. More importantly, to guarantee that the tracking errors can converge into a small neighborhood of equilibrium in a fixed time independent of the initial state, the adaptive neural fixed-time local tracking controller is proposed. Another merit of the proposed controller is that the approximation errors are addressed in a novel way, eliminating the need for prior precise knowledge of uncertainties and improving the robustness and convergence speed of unknown robotic systems. Finally, the experimental results demonstrate the effectiveness and advantages of the proposed control method.

A novel finite-time leader-follower consensus control for a disturbed mechanical nonlinear system in presence of actuator saturation

This paper presents a novel leader-follower consensus control for a particular class of nonlinear multi-agent mechanical systems in the presence of control input constraints and external disturbances which includes robot system dynamics with a wide range of potential applications in industry. In this case, one of the agents is selected as the leader to direct the other agents in such a way that the whole system can reach consensus within certain prescribed performance transient bounds. Due to the presence of disturbances in most practical systems, the effect of limited disturbance in the consensus control method has been investigated, and actuator saturation is included in the design process. A terminal sliding mode control method has been adapted to ensure the stability of the overall system and fast finite-time leader-follower consensus control. The simulation results of the multi-agent nonlinear robot system in MATLAB environment, in different scenarios with simultaneous consideration of actuator saturation and external disturbance, will show the efficiency of the proposed control method.

Marine algae inspired dispersion of swarm robots with binary sensory information

The dynamics of swarm robotic systems are complex and often nonlinear. One key issue is to design the controllers of a large number of simple, low-cost robots so that emergence can be observed. This paper presents a sensor and computation-friendly controller for swarm robotic systems inspired by the mechanisms observed in algae. The aim is to achieve uniform dispersion of robots by mimicking the circular movement observed in marine algae systems. The proposed controller utilizes binary sensory information (i.e., see or not see) to guide the robots’ motion. By moving circularly and switching the radii based on the perception of other robots in their line of sight, the robots imitate the repulsion behavior observed in algae. The controller relies solely on binary-state sensory input, eliminating the need for additional memory or communication. Up to 1024 simulated robots are used to validate the effectiveness of the dispersion controller, while experiments with 30 physical robots demonstrate the feasibility of the proposed approach.

Flatness-based control in successive loops for electropneumatic actuators and robots

The control problem for the nonlinear dynamics of robotic and mechatronic systems with electropneumatic actuation is solved with the use of a flatness-based control approach which is implemented in successive loops. The state-space model of these systems is separated into a series of subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems. In this chain of i=1,2,…,N subsystems, the state variables of the subsequent (i+1th) subsystem become virtual control inputs for the preceding (ith) subsystem, and so on. In turn, exogenous control inputs are applied to the last subsystem and are computed by tracing backwards the virtual control inputs of the preceding N−1 subsystems. The whole control method is implemented in successive loops and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is confirmed in two case studies: (a) control of an electropneumatic actuator, (ii) control of a multi-DOF robotic manipulator with electropneumatic actuators.

Introducing convex BiLSTM-based controller applied to 3-PSP spatial parallel robot manipulator

The idea of modeling first and then finding a control approach might have made sense when systems were changing on a vast scale, but not in today’s world where we are in danger of being run over on a highway called the information highway. The feedback phenomenon, which involves a system’s future being influenced by its past, presents a rational conflict in this context. However, we can move beyond these challenges by leveraging the system’s inherent ability to randomly repeat and re-create parts, allowing new features to emerge and the best repetition to become the standard. To address the mentioned challenges, this paper proposes a new structure based on bilateral long short-term memory (BiLSTM) to provide supervising feedback on the nonlinear components comprising friction, Coriolis, and Centrifugal forces in addition to gravity for adapting uncertain variations in the robotic system dynamics. A key characteristic of the proposed BiLSTM-based controller is the incorporation of memory. A new formulation is developed for determining when to recover old memories and to what extent, ensuring the controller remains both adaptive and efficient. Furthermore, given that for system identification, speed is vital; the proposed approach has dealt with the speed category by introducing a convex combination of BiLSTM layers. The stability of the whole structure has also been analyzed, and the updated learning rules have been derived. The simulation results show improved tracking performance and learning time compared to alternative approaches.

Robust Adaptive Fixed-Time Sliding-Mode Control for Uncertain Robotic Systems With Input Saturation.

In this article, a robust adaptive fixed-time sliding-mode control method is proposed for robotic systems with parameter uncertainties and input saturation. First, a model-based fixed-time controller is designed under the premise that the system parameters are known. Moreover, the unknown dynamics of robotic systems and the boundary of compounded disturbance are synthesized into a compounded uncertainty. Then, the Gaussian radial basis function neural networks (NNs) are selected to approximate the compounded uncertainty. In addition, the nonsingular fast terminal sliding-mode (NFTSM) control is incorporated into the proposed fixed-time control framework to enhance the robustness and convergence speed of unknown robotic systems. Finally, a comparative simulation based on a rigid manipulator shows the superiority and efficacy of the designed methods.

Comparison the accuracy of a novel implant robot surgery and dynamic navigation system in dental implant surgery: an in vitro pilot study

BackgroundTo compare the accuracy of dental implant placement using a novel dental implant robotic system (THETA) and a dynamic navigation system (Yizhimei) by a vitro model experiment.Methods10 partially edentulous jaws models were included in this study, and 20 sites were randomly assigned into two groups: the dental implant robotic system (THETA) group and a dynamic navigation system (Yizhimei) group. 20 implants were placed in the defects according to each manufacturer’s protocol respectively. The implant platform, apex and angle deviations were measured by fusion of the preoperative design and the actual postoperative cone-beam computed tomography (CBCT) using 3D Slicer software. Data were analyzed by t - test and Mann-Whitney U test, p < 0.05 was considered statistically significant.ResultsA total of 20 implants were placed in 10 phantoms. The comparison deviation of implant platform, apex and angulation in THETA group were 0.58 ± 0.31 mm, 0.69 ± 0.28 mm, and 1.08 ± 0.66° respectively, while in Yizhimei group, the comparison deviation of implant platform, apex and angulation were 0.73 ± 0.20 mm, 0.86 ± 0.33 mm, and 2.32 ± 0.71° respectively. The angulation deviation in THETA group was significantly smaller than the Yizhimei group, and there was no significant difference in the deviation of the platform and apex of the implants placed using THETA and Yizhimei, respectively.ConclusionThe implant positioning accuracy of the robotic system, especially the angular deviation was superior to that of the dynamic navigation system, suggesting that the THETA robotic system could be a promising tool in dental implant surgery in the future. Further clinical studies are needed to evaluate the current results.

Bioinspired Multifunctional E‐skin for Robot Dynamic Tactile Real‐Time Feedback Systems Using Triboelectric Sensors and Electrochromic Devices (Adv. Sensor Res. 1/2022)

Back Cover Bioinspired Multifunctional E-skin In article number 2200013, Feng-xia Wang, Hui-cong Liu, Tao Chen, and co-workers, inspired by biological color change, report a bioinspired multifunctional e-skin for robot dynamic tactile real-time feedback systems using triboelectric sensors and electrochromic devices. The arrayed design system not only displays the pressure intuitively through color switching, but also provides more information for detecting the distribution of pressure, showing the positive prospects of electronic skins in robot tactile feedback.

Bioinspired Multifunctional E‐skin for Robot Dynamic Tactile Real‐Time Feedback Systems Using Triboelectric Sensors and Electrochromic Devices

AbstractDesigning efficient robot dynamic tactile feedback systems remains a great challenge, especially in disaster relief and human‐computer interaction, because they need to provide timely tactile and visual data. Herein, a novel bioinspired electronic skin (e‐skin) is proposed based on self‐powered triboelectric sensor (TES) arrays and electrochromic device (ECD) arrays. Single‐electrode TES arrays (TESA) are used to detect pressure distribution at the different positions of the robot. The ECD arrays (ECDs) are used as the real‐time visualization window to provide feedback on the pressure distribution by the change in the color of the e‐skin. The bioinspired e‐skin arrayed system possesses a similar capability to Chameleon, displaying the pressure information on the contact surface between the robot and object in real‐time via interactive color‐changing capabilities, such as the location, degree, and distribution. Moreover, the proposed e‐skin can be used as a feedback system for large‐scale curved robot surfaces due to its flexibility, large‐scale process, and excellent compatibility. Thus, the novel design, large‐scale process, and self‐powered advantages make it a good candidate to pave the way for developing human‐robot interactions as robot dynamic tactile real‐time feedback systems.

Dynamic modeling and vibration analysis of milling robots

To deal with the situations where the complex dynamics modeling methods and the high computational cost, a method of rapid dynamic modeling and vibration response analysis of milling robot is proposed. Taking the influence of joint flexibility into account, the transfer matrix method for multibody system is applied to establish the rigid-flexible coupling multibody system dynamic model of milling robot in this paper, and its vibration characteristics and dynamic response are dissected. First, the robot system and milling dynamics model are established to form a dynamics numerical simulation system. Then, the vibration characteristics and dynamic response of the robot system are solved, and the relationship between the dynamic parameters of the robot body and the processing process parameters and the dynamic response is revealed. Finally, a dynamic test and a robot milling test were designed to verify the accuracy of the dynamic model.

Neural Network-Based Tracking Control of Uncertain Robotic Systems: Predefined-Time Nonsingular Terminal Sliding-Mode Approach

This article investigates the predefined time trajectory tracking control of uncertain nonlinear robotic systems. A radial basis function neural network (RBFNN) is used to estimate uncertainties in the robotic system dynamics. To avoid the singularity of terminal sliding-mode control (TSMC), a modified sliding variable is adopted. In order to realize that the tracking errors can converge to a small neighborhood of the origin in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">predefined time</i> , within which the maximum convergence time can be adjusted by explicit parameters in advance, a nonsingular TSMC based on the RBFNN is proposed. Experiments on a ROKAE platform demonstrate the effectiveness and advantage of the proposed control method.

Design of motion-assisted training control system based on nonlinear mechanics

Abstract In order to improve the level of intelligent rehabilitation training equipment and assist patients to effectively restore the walking function, a motion-assisted training control system based on nonlinear mechanics is proposed. According to the robot direction of lower limb rehabilitation equipment studied in this article, a stable, safe, and reliable mechanical structure system is designed to optimize the structure, and the anti-overturning stability of the robot static system and dynamic system is analyzed. Then, through the human-computer interaction system, the robot automatically realizes the automatic forward and backward, left and right turn, anti-overspeed, anti-fall, and other functions. A joint simulation platform of rehabilitation robot speed control was built using MATLAB and ADAMS to verify the effectiveness of the designed control algorithm. The results showed that users slowly increased their training speed from 1 to 5, walking at approximately 0.3 m/s. The user adapts slowly during the use process, and through training, and the walking speed is improved slowly. Therefore, the lower limb rehabilitation equipment training robot is highly applicable, convenient for operation, with good robot control performance, high stability, and good mobility ability.

A Graph Theory-Based Method for Dynamic Modeling and Parameter Identification of 6-DOF Industrial Robots

At present, the absolute positioning accuracy and control accuracy of industrial serial robots need to be improved to meet the accuracy requirements of precision manufacturing and precise control. An accurate dynamic model is an important theoretical basis for solving this problem, and precise dynamic parameters are the prerequisite for precise control. The research of dynamics and parameter identification can greatly promote the application of robots in the field of precision manufacturing and automation. In this paper, we study the dynamical modeling and dynamic parameter identification of an industrial robot system with six rotational DOF (6R robot system) and propose a new method for identifying dynamic parameters. Our aim is to provide an accurate mathematical description of the dynamics of the 6R robot and to accurately identify its dynamic parameters. First, we establish an unconstrained dynamic model for the 6R robot system and rewrite it to obtain the dynamic parameter identification model. Second, we establish the constraint equations of the 6R robot system. Finally, we establish the dynamic model of the constrained 6R robot system. Through the ADAMS simulation experiment, we verify the correctness and accuracy of the dynamic model. The experiments prove that the result of parameter identification has extremely high accuracy and the dynamic model can accurately describe the 6R robot system mathematically. The dynamic modeling method proposed in this paper can be used as the theoretical basis for the study of 6R robot system dynamics and the study of dynamics-based control theory.

Tracking control of an uncertain heavy load robot based on super twisting sliding mode control and fuzzy compensator

AbstractThis paper presents a new combined strategy to control an uncertain heavy load robot with position disturbance caused by flexible foot, and the control strategy is realized only by position feedback. Firstly, deformation during motion is analyzed, and the position transformation generated by this deformation is treated as periodic position disturbance and non‐periodic position disturbance. Then, a control method combined by computed torque controller (CTC), super twisting sliding mode (STSM) controller, and fuzzy compensator is designed for the robot dynamic system to track ideal trajectory. Meanwhile, a proportional–integral–derivative (PID) controller based on CTC is designed as a comparison to verify the trajectory tracking performance and robustness of the combined strategy under external disturbances. Finally, simulation results show that CTC‐STSM is clearly found to converge faster and to show stronger ability to suppress interference than CTC‐PID. The addition of fuzzy compensator (CTC‐STSM‐Fuzzy) can further reduce the sliding mode chattering effect and improve the performance.

이륜 로봇의 동역학 유도를 위한 Denavit-Hartenberg 변수 접근법에 대한 연구

This paper presents a study on the derivation of the dynamics equations of two-wheel robot systems by using Denavit-Hartenberg(D-H) parameters defined for robot manipulators. A wheeled-mobile robot and a two-wheel robot of combining an inverted pendulum system and a wheeled-mobile robot share a similar kinematics and dynamics. Since the wheeled-mobile robots behave like a simple inertial system under some assumptions, linear control schemes are applied accordingly by ignoring kinematics constraints in practice. The simplified dynamics of two-wheel robot systems without nonholonomic constraints by ignoring wheel dynamics is analyzed. The kinematics and dynamics of two-wheel robots are derived by defining D-H parameters. Experimental studies of controlling a circular trajectory of a two-wheel robot are conducted to show the feasibility of the proposal.

Inspection and control system for experiments in space robotics

The communication subsystem is one among the various subsystems of a telerobotic space system. It is responsible for coordinating the commands received from the teleoperator control subsystem to the robotic arm, for reading signals from the sensors, and for stating the communication of the telerobot with the ground station. The telerobotic experiment under development by the ITA space robotics research group was developed with the purpose of investigating a robotic space system dynamics and control, including the study of the working and integration of all subsystems involved in the teleoperation control. The lab experiment consists of two identical units of robot manipulators, each of them mounted on its own floating air-supported platform. The objective is to simulate computationally the operations of rendezvous and capture in the microgravity' orbital environment, emulated by the floating manipulators' dynamics. The closed circuit for this system involves the in time position detection, transmission and data processing by using a position-tracking (X, Y, and Z) computer system combined with a Kinect sensor (RGB-D). The computer system comprises two computers capable of processing the positional images with greater accuracy. One of them receive and send the sensor data to a second computer which performs the data processing by proper algorithms in Matlab® and Simulink and sends commands to the robotic arm via WIFI (UDP protocol) network. The robot receives and executes the control signals moving the robotic arms whose position is again detected by the kinect sensor and informed back to the computer system, closing the control mesh and allowing the safe capture of the target. This work deals with the communication subsystem of the space robot experiment and its ability to set an integrated and efficient communication satisfying the telerobot control requirements