Control Of Robot Manipulators Research Articles

Nonfragile Prescribed Performance Control of Robot Manipulators With Actuator Faults

Nonfragile Prescribed Performance Control of Robot Manipulators With Actuator Faults

Muscle‐Inspired Robust Anisotropic Cellulose Conductive Hydrogel for Multidirectional Strain Sensors and Implantable Bioelectronics

AbstractIntegrating superior mechanical performance, anisotropic conductivity, and biocompatibility into conductive hydrogels as all‐in‐one human‐machine interaction device remains challenging. Herein, by mimicking the anisotropic structures of human muscles, a robust anisotropic conductive hydrogel is developed by initially aligning polyvinyl alcohol with polypyrrole decorated cellulose nanofibrils to form an anisotropically oriented polymer networks, followed by post‐crosslinking with tannic acid (TA). Introducing TA into hydrogel network permanently secures its hierarchically anisotropic structure through multiple hydrogen bonds, thus endowing the hydrogel with exceptional mechanical properties (tensile strength of 11.41 MPa, toughness of 12.44 MJ m−3), anisotropic adhesive property, and direction‐dependent conductivity. With these attributes, a hydrogel strain sensor with excellent multidirectional sensitivity is developed, enabling stable monitoring of multi‐degrees of freedom joint movements in the human body and facilitating the control of a multiaxial virtual robot manipulator. Moreover, the in vitro/vivo tests demonstrate exceptional biocompatibility and anti‐biofouling properties of the as‐prepared hydrogel sensor, maintaining stable electronic response signals for over 14 days after successful implantation into the Achilles tendon of mice. Overall, this study presents a promising approach for designing conductive hydrogels with superior mechanical properties and anisotropic functionality for emerging applications in both in vitro and in vivo human‐machine interface materials.

Adaptive Fault-Tolerant Tracking Control for Multi-Joint Robot Manipulators via Neural Network-Based Synchronization.

In this paper, adaptive fault-tolerant control for multi-joint robot manipulators is proposed through the combination of synchronous techniques and neural networks. By using a synchronization technique, the position error at each joint simultaneously approaches zero during convergence due to the constraints imposed by the synchronization controller. This aspect is particularly important in fault-tolerant control, as it enables the robot to rapidly and effectively reduce the impact of faults, ensuring the performance of the robot when faults occur. Additionally, the neural network technique is used to compensate for uncertainty, disturbances, and faults in the system via online updating. Firstly, novel robust synchronous control for a robot manipulator based on terminal sliding mode control is presented. Subsequently, a combination of the novel synchronous control and neural network is proposed to enhance the fault tolerance of the robot manipulator. Finally, simulation results for a 3-DOF robot manipulator are presented to demonstrate the effectiveness of the proposed controller in comparison to traditional control techniques.

Model predictive control for space robot manipulator modeled by switched systems: A data‐driven approach

AbstractThis article investigates the data‐driven based model predictive control (MPC) problem for a class of space robot manipulator (SRM). First, the SRM system is modeled as a class of discrete‐time switched system to reflect more system characteristics. Second, only the input‐state data are used for end‐to‐end controller design, and the system identification process is avoided. In order to meet the real‐time requirements of the manipulator arm angles, a kind of constrained predictive control method is designed to prevent the overheating or damage of SRM motor, optimizing the cost function for each decision cycle under the safe control input constraints. Then, based on the average dwell time method, the asymptotic stability is assured for the presented SRM system. The design conditions for data‐driven based MPC are provided in the form of linear matrix inequalities. Finally, the effectiveness and advantage for the developed data‐driven based MPC strategy are validated via a case study of SRM system.

Nonzero-sum game-based decentralized approximate optimal control of modular robot manipulators with coordinate operation tasks using value iteration

Nonzero-sum game-based decentralized approximate optimal control of modular robot manipulators with coordinate operation tasks using value iteration

Disturbance observer based adaptive predefined‐time sliding mode control for robot manipulators with uncertainties and disturbances

AbstractThis article develops a predefined‐time sliding mode control approach for systems with external disturbances and uncertainties through a nonlinear disturbance observer (DO). For addressing predefined‐time stabilization problem of robotic manipulator system, a predefined‐time sliding mode surface is proposed, ensuring system states converge to origin within a predefined‐time once sliding mode surface is attained. Compared to conventional fixed‐time and finite‐time control strategies, a distinctive advantage of this scheme is that system settling time can be explicitly chosen in advance and independent of system states. To achieve predefined‐time performance, a disturbance observer is introduced to generate the disturbance estimate, which can be incorporated into controller to counteract disturbance. To address the systems uncertainty, an adaptive law is employed to estimate the unknown upper boundary of system uncertainties. Finally, the effectiveness and performance of the proposed scheme are illustrated by simulation and experiment.

Adaptive visual servoing control for uncalibrated robot manipulator with uncertain dead-zone constraint

This paper addresses the adaptive visual tracking control problem of an uncalibrated camera robot system with unknown dead-zone inputs. The uncertainties include camera extrinsic and intrinsic parameters, robot dynamic parameters, and feature depth parameters. The control achieves a better performance by establishing a smooth inverse model of the dead-zone input, which eliminates the nonlinear effect of the actuator constraint. A novel adaptive control scheme is presented which does not require a priori knowledge of the parameter intervals of dead-zone model, to update the parameter values online. Furthermore, adaptive laws are provided to estimate the uncertainties that exist both in robot and in camera models. Meanwhile, in order to avoid the singularity of the image Jacobian matrix, a projection algorithm is also introduced. Subsequently, a novel robust adaptive controller together with dead-zone compensation is established so that the tracking error image space converges to the origin. Simulations and experimental studies are conducted to verify the effectiveness of the proposed method.

Adaptive trajectory tracking control of robotic manipulators based on integral sliding mode

AbstractThis paper proposes an adaptive control scheme with finite‐time convergent property based on integral sliding mode to achieve trajectory tracking control of rigid robotic manipulators. The novel adaptive gain can be adjusted automatically with the system disturbance, as long as the disturbance and its derivative are bounded without requiring more information. Therefore, the control chattering effect can be mitigated obviously. The global robustness of system is guaranteed by Lyapunov stability analysis. Finally, the simulation of a two‐link manipulator is given to show that the desired trajectory tracking performance is obtained with the proposed adaptive sliding mode approach.

A Bi-Criteria Kinematic Strategy for Motion/Force Control of Robotic Manipulator

A Bi-Criteria Kinematic Strategy for Motion/Force Control of Robotic Manipulator

Adaptive Tracking Control of Robotic Manipulators With Unknown Kinematics and Uncertain Dynamics

Adaptive Tracking Control of Robotic Manipulators With Unknown Kinematics and Uncertain Dynamics

Fixed-time self-triggered fuzzy adaptive control of N-link robotic manipulators

This paper studies the fixed-time fuzzy adaptive self-triggered control of n-link robotic manipulators. To achieve fixed-time stability, a fuzzy adaptive fixed-time control algorithm is proposed by reconstructing the self-triggered controller and using the singularity-free function, which avoids the singularity problem and obtains fixed-time stability. Meanwhile, the self-triggered mechanism reduces the communication resources between the actuator and the controller, improving the system's transmission efficiency. Through the above thoughts, a kind of fixed-time fuzzy adaptive controller based on the self-triggered mechanism is designed, which guarantees that the tracking error of each joint converges to the prescribed range within a fixed time. Finally, the effectiveness of the proposed algorithm is verified by a simulation example of the two-link robotic manipulator.

Vision-based reinforcement learning control of soft robot manipulators

PurposeThis study aims to tackle control challenges in soft robots by proposing a visually-guided reinforcement learning approach. Precise tip trajectory tracking is achieved for a soft arm manipulator.Design/methodology/approachA closed-loop control strategy uses deep learning-powered perception and model-free reinforcement learning. Visual feedback detects the arm’s tip while efficient policy search is conducted via interactive sample collection.FindingsPhysical experiments demonstrate a soft arm successfully transporting objects by learning coordinated actuation policies guided by visual observations, without analytical models.Research limitations/implicationsConstraints potentially include simulator gaps and dynamical variations. Future work will focus on enhancing adaptation capabilities.Practical implicationsBy eliminating assumptions on precise analytical models or instrumentation requirements, the proposed data-driven framework offers a practical solution for real-world control challenges in soft systems.Originality/valueThis research provides an effective methodology integrating robust machine perception and learning for intelligent autonomous control of soft robots with complex morphologies.

Pick and Place Control of a 3-DOF Robot Manipulator Based on Image and Pattern Recognition

Board games like chess serve as an excellent testbed for human–robot interactions, where advancements can lead to broader human–robot cooperation systems. This paper presents a chess-playing robotic system to demonstrate controlled pick and place operations using a 3-DoF manipulator with image and speech recognition. The system identifies chessboard square coordinates through image processing and centroid detection before mapping them onto the physical board. User voice input is processed and transcribed into a string from which the system extracts the current and destination locations of a chess piece with a word error rate of 8.64%. Using an inverse-kinematics algorithm, the system calculates the joint angles needed to position the end effector at the desired coordinates actuating the robot. The developed system was evaluated experimentally on the 3-DoF manipulator with a voice command used to direct the robot movement in grasping a chess piece. Consideration was made involving both the own pieces as well as capturing the opponent’s pieces and moving the captured piece outside the board workspace.

A New Method for Displacement Modelling of Serial Robots Using Finite Screw

Kinematics is a hot topic in robotic research, serving as a foundational step in the synthesis and analysis of robots. Forward kinematics and inverse kinematics are the prerequisite and foundation for motion control, trajectory planning, dynamic simulation, and precision guarantee of robotic manipulators. Both of them depend on the displacement models. Compared with the previous work, finite screw is proven to be the simplest and nonredundant mathematical tool for displacement description. Thus, it is used for displacement modelling of serial robots in this paper. Firstly, a finite-screw-based method for formulating displacement model is proposed, which is applicable for any serial robot. Secondly, the procedures for forward and inverse kinematics by solving the formulated displacement equation are discussed. Then, two typical serial robots with three translations and two rotations are taken as examples to illustrate the proposed method. Finally, through Matlab simulation, the obtained analytical expressions of kinematics are verified. The main contribution of the proposed method is that finite-screw-based displacement model is highly related with instantaneous-screw-based kinematic and dynamic models, providing an integrated modelling and analysis methodology for robotic mechanisms.

Enhanced tracking control for n-DOF robotic manipulators: A fixed-time terminal sliding mode approach with time delay estimation

This work proposes a fixed-time terminal sliding mode control scheme with time delay estimation (FxTSM-TDE) for unknown nonlinear robotic systems under uncertainties and unknown bounded disturbances. The idea of the fixed-time terminal sliding mode control method (FxTSM) is thoroughly explained and analyzed. The FxTSM approach has been used to achieve nonsingular fixed-time control, non-chatter control inputs, and superior trajectory tracking performance. Then, the proposed approach for estimating the unknown robot dynamics utilizes the TDE approach. The Lyapunov analysis is used to establish the fixed-time stability of the system. The effectiveness of the suggested approach is evaluated, demonstrated, and contrasted with the existing control schemes by illustrating the results of the proposed method's enhanced performance in tracking accuracy, robustness, and convergence speed when implemented in robotic manipulator dynamics. The results indicate that the existing scheme has corresponding root mean square (RMS) error values of 0.0235 and 0.0283, while the proposed scheme has values of 0.0178 and 0.0188, respectively.

Fractional‐order fixed‐time sliding mode control of robotic manipulators with robust exact differentiator

AbstractThis paper introduces a robust position tracking control structure of exoskeleton robots (ERs). An open environment and body flexibility are the main challenges in ER control system design. First, a fractional‐order nonsingular terminal sliding mode (FONTSM) controller is proposed for a robotic manipulator with uncertainty, where lumped unknown dynamics are computed by time‐delay estimation (TDE). Secondly, a robust exact differentiator (RED) is employed as the output feedback observer for the velocity sensorless robotic system. Thirdly, the stability of the FONTSM control system is demonstrated. It is proved that the system error can move to the specified value along the sliding mode surface at a fixed time and can reach the sliding mode surface after a finite time. Finally, simulations on a 2‐DOF robotic manipulator show the effectiveness of the fixed‐time control strategies. When a combined trigonometric function disturbance is applied, the joint angle error is reduced to 1% by the model‐free controller based on output feedback within approximately 1 s.

Multi-receptor skin with highly sensitive tele-perception somatosensory.

The limitations and complexity of traditional noncontact sensors in terms of sensitivity and threshold settings pose great challenges to extend the traditional five human senses. Here, we propose tele-perception to enhance human perception and cognition beyond these conventional noncontact sensors. Our bionic multi-receptor skin employs structured doping of inorganic nanoparticles to enhance the local electric field, coupled with advanced deep learning algorithms, achieving a ΔV/Δd sensitivity of 14.2, surpassing benchmarks. This enables precise remote control of surveillance systems and robotic manipulators. Our long short-term memory-based adaptive pulse identification achieves 99.56% accuracy in material identification with accelerated processing speeds. In addition, we demonstrate the feasibility of using a two-dimensional (2D) sensor matrix to integrate real object scan data into a convolutional neural network to accurately discriminate the shape and material of 3D objects. This promises transformative advances in human-computer interaction and neuromorphic computing.

Adaptive fast terminal sliding mode control of robotic manipulators based on joint torque estimation and friction compensation

AbstractIn this work, an adaptive fast terminal sliding mode control (AFSMC) approach based on joint torque estimation and friction compensation is proposed to enhance the trajectory tracking accuracy of robotic manipulators under variable load conditions. The joint torque estimation utilizes an improved harmonic drive compliance model and adaptive low‐pass filtering, and friction compensation employs a hybrid model accounting for velocity and load torque effects. These compensations reduce the upper bound of the uncertainty, while AFSMC further reduces dependency on upper uncertainty bounds and minimizes the chattering. The stability analysis using the Lyapunov method confirms the effectiveness of this approach. Experimental results demonstrate that the proposed controller achieves smaller root mean square and maximum error of trajectory tracking, thus significantly improving trajectory tracking accuracy under variable load conditions.

Observer-Based Finite-Time Prescribed Performance Sliding Mode Control of Dual-Motor Joints-Driven Robotic Manipulators with Uncertainties and Disturbances

Considering system uncertainties (e.g., gear backlash, unmodeled dynamics, nonlinear friction and parameters perturbation) coupling disturbances weaken the motion performance of robotic systems, an observer-based finite-time prescribed performance sliding mode control with faster reaching law is proposed for robotic manipulators equipped with dual-motor joints (DMJs). In the case where the backlash information is completely unknown, the backlash is maximally eliminated using a simple but efficient dual-motor adaptive anti-backlash strategy. Thus, the design of position tracking controllers for DMJs can be simplified. Then, to deal with the influence of disturbances and residual uncertainties (excluding backlash), a novel finite-time adaptive sliding mode disturbance observer (ASMDO) is proposed to practically estimate the lumped uncertainties where their upper bounds are assumed to be unknown. Finally, a finite-time composite fast non-singular terminal sliding mode (TSM) controller, integrated with the prescribed performance principle, is proposed in this paper. To enhance the convergence rate, a novel TSM-type reaching law has been developed. The controller ensures that the tracking error is not only stabilized within a finite-time convergence rate but also adheres to a predefined maximum transient-steady-state error. The proposed scheme is implemented through simulation and experimental results, demonstrating its superior performance.

Time-optimal constrained kinematic control of robotic manipulators by recurrent neural network

Time-optimal kinematic control is a vital concern for industrial manipulators to save allocated motion task time as much as possible. This requires maximizing the end-effector velocity to minimize the time required for path tracking. Nonetheless, it remains a challenge to ensure that joint motion constraints are not violated during this process, even with the aim of maximizing end-effector velocity simultaneously. This paper introduces a novel approach, which for the first time leverages dynamic recurrent neural networks (RNNs) within a constrained optimization framework to attain time-optimal kinematic control for manipulators. The theoretical analysis of the RNN-based kinematic control solver is addressed, ensuring both its optimality and convergence for achieving time-optimal kinematic control. The proposed method enables the maximization of end-effector velocity to achieve time-optimal kinematic control without violating all joint velocity limits simultaneously. In contrast to previous kinematic control schemes, the proposed method can enhance the end-effector path tracking speed of completion by 100% around, we substantiate the effectiveness and superiority of the proposed approach via simulation and V-Rep experiment on the manipulators.