Dynamic Model Of Manipulator Research Articles

A predefined-time radial basis function (RBF) neural network tracking control method considering actuator faults for a new type of spraying robot

Abstract. A small-range fine-spraying collaborative robot (SFSC) for vehicle surface repair has been designed, which has 4 degrees of freedom. Conventional control methods, such as sliding mode control (SMC) have difficulty meeting the accuracy requirements when the end of the attitude adjustment robotic arm control is spraying. Focusing on the problem of tracking control of a multi-joint robot with uncertain information, such as modeling uncertainty and random interference, a predefined-time radial basis function (RBF) neural network tracking control (PRC) method considering actuator fault is proposed for a new spraying robot. Firstly, the dynamics equations of the n-joint manipulator are derived using the Euler–Lagrange equation. Then, a new predefined-time sliding mode surface is designed based on the stability theory of PRC. Combined with the Euler–Lagrange dynamics model of the two-joint manipulator, a nonsingular PRC controller is designed according to the uncertainty in model parameters and external interference. Stability of the system is proven based on Lyapunov theory. The simulation results show that the designed controller can ensure that the state convergence of the system does not depend on the initial conditions and has a faster convergence rate, shorter convergence time and good robustness.

Neuroadaptive‐based fixed‐time control for robotic manipulators with uniform prescribed performance under unknown disturbance

AbstractAchieving faster convergence, smaller transient overshoots, and higher steady‐state tracking accuracy is essential to improve the efficiency, robustness, and applicability of robotic manipulators. This article introduces an innovative adaptive fixed‐time uniform prescribed performance controller for the manipulator facing model uncertainties and unknown disturbances. Initially, by designing a modified prescribed performance function inspired by variable superposition, this study redefines the unified prescribed performance control problem into a simplified parameter selection problem. This approach allows for the incorporation of varied performance metrics within a singular control scheme, addressing both transient and steady‐state performances concurrently without shifting control frameworks. Then, to alleviate computational demands, an adaptive neural network employing a single‐parameter weight update technique compensates for uncertainties of the manipulator dynamic model. Additionally, a disturbance observer is designed to mitigate the impact of non‐parametric disturbances. Moreover, integrating fixed‐time theory with the Lyapunov stability analysis method guarantees the convergence of all error signals to a near‐zero compact neighborhood at a fixed time. Finally, the advantages and comprehensive performance of the proposed method are confirmed by numerical simulations and real‐world experiments.

Efficiency assessment of SOA-based computed torque control: A comparative analysis with NE-based approach

This paper proposes the spatial operator algebra-based computed torque control scheme applied to an anthropomorphic 3-degree of freedom robotic manipulator, which aims to reduce the computational cost of the classical methods and integrate the advantages of low computational cost into advanced robotic control systems. The computational cost is increased due to the calculations of the inverse dynamic and the feedback control loop. The spatial operator algebra (SOA) algorithm provides a systematic derivation, evaluation, and subsequent conceptual interpretation of the manipulator dynamics model. This paper presents a powerful and efficient solution for controlling the dynamics and trajectory of the manipulator. In order to show the efficiency of the solution, the Newton Euler (NE) based control schemes are also applied to the manipulator. The SOA-based controller significantly reduced the computational cost and performed approximately 60%–70% faster than the NE-based controller. Furthermore, different initial states, disturbances, and uncertainty tests are implemented and the SOA-based controller demonstrated successful performance under various conditions while maintaining a lower computational cost. In this study, the advantages and limitations of each method (SOA-based, NE-based) are evaluated and the potential benefits of using SOA in the computed torque control scheme are highlighted. The SOA-based controller, which was verified by simulation, is then implemented in real-time and showed successful performance.

Research on Reliability of Positioning Accuracy of Electromechanical Coupling Ammunition Loading Manipulator During Rotation Process

The loading manipulator is an important actuator in modern artillery automatic loading systems, and it is crucial to evaluate its positioning accuracy accurately and efficiently. This paper presents a reliability analysis method based on the combination of feedforward neural network (FNN) and mixed importance sampling (MIS) to study the positioning accuracy of the loading manipulator. First, the manipulator dynamic model considering the control system is established. To improve the efficiency of reliability analysis, a surrogate model based on FNN is constructed. By searching the most probable point (MPP) of each limit state equation, an MIS density function is constructed, after which important samples can be extracted to evaluate the reliability of the positioning accuracy of the manipulator. Compared with the Monte Carlo simulation (MCS) method, the proposed method enjoys higher efficiency while ensuring accuracy. The results of the example show that the gear clearance and friction coefficient have an important effect on the positioning accuracy of the loading manipulator, which should be taken into account in the machining and installation process.

MPMC-frame: Multiplatform migration control framework for manipulator control

Existing robot control models suffer from poor generalization performance due to varied tasks between manipulators, configuration differences, and physical limits of motion. To address this problem, a multiplatform migration control framework (MPMC-frame) based on a constrained dynamics model is proposed in this paper. First, a model of the robotic manipulator dynamics with configuration adjustment (CA) is constructed based on a neural network to unify the physical joint data of different manipulators. Second, the model-based controller that can be integrated into the framework is designed, and the constraint on the upper bound on the error of uncertain parameters in the control law to guarantee the control robustness and accuracy of the controller for different manipulator platforms. Final, the generic potential function is designed based on multiplatform task requirements, and a task parameter table is constructed to improve the joint motion control performance of MPMC-frame. The feasibility of the method proposed in this paper are verified through simulations and experiments based on the ABB_irb120 and AUBO_i5 robotic manipulators.

Analysis and formulation of the manipulator dynamic model

The movement of a robot manipulator with four joints is analyzed and mathematically modeled. The kinematic and dynamic models of the manipulator for each hinge are considered. A PID controller is designed using MatLab/Simulink systems. The results of the proposed controller operation are presented and compared with the data of the reference signal and the output of the system without control. Keywords kinematic model, dynamic model, manipulator, PID controller, MatLab, robot manipulator, hinge

Adaptive radial basis function neural network sliding mode control of robot manipulator based on improved genetic algorithm

ABSTRACT Since the trajectory-tracking control performance of multi-joint robot manipulator may be degraded due to modeling errors and external disturbances, this paper designs a new adaptive robot manipulator trajectory tracking control method through improved genetic algorithm and radial basis function neural network sliding mode control (IGA-RBFNNSMC). Firstly, the genetic algorithm (GA) is improved by establishing superior populations centered on individuals with high fitness values and selecting individuals in the superior populations for crossover and variation. Secondly, the improved genetic algorithm (IGA) is used for the optimization of the center vector and width vector of the Gaussian basis function in radial basis function (RBF) neural network. Then, based on the dynamics model of the robot manipulator, the modeling errors are approximated by RBF neural network and eliminated by sliding mode control (SMC), and the Lyapunov theorem is used to prove the stability and convergence of the control system. Finally, a two-joint robot manipulator is taken as the research objective and the simulation results show that IGA can significantly reduce the solution time on the basis of guaranteed accuracy and IGA-RBFNNSMC can make the trajectory tracking control accurate and more efficient, which proves the effectiveness of the proposed control method.

Emergency ejection characteristics of space manipulator multi-body system

AbstractSpace manipulators are typically installed on spacecraft using an emergency separation device (ESD). In the event of a malfunction, the ESD ejects the manipulator from the spacecraft. However, due to the relative rotation of the manipulator’s joints during the ejection, the equivalent ejection mass varies depending on different attitudes. This paper focuses on studying manipulators equipped with separation slide rails and analyzes their ejection characteristics under different attitudes to determine the optimal manipulator attitude for ejection. Initially, the ejection dynamics model of the space manipulator is established using the Lagrangian method, based on the kinetic energy equation, kinematics equation, and the boundary condition between the manipulator and ESD. Afterward, the space dynamics model is transformed into the dynamic model of plane ejection state by recursion formula. From this model, the equivalent ejection mass and ejection velocity are obtained, and the joint angular variation during ejection is acquired by considering joint friction torque. Using the law of conservation of angular momentum, the ejection angular velocity is then calculated. Finally, this study selected a 7-DOF space manipulator as an example and adjusted the damping parameter B of the joint for more precise calculations by choosing the attitude with a relatively larger joint angular variation. The modified model was then tested for its applicability to other attitudes. After determining the value of B, the correctness of the algorithm was validated by MATLAB calculation, ADAMS simulation, and real object ejection test.

Spherical Joint Actuator with Backstepping Sliding Mode Control for Robotic Manipulator Rigid-Flexible Coupling System

At present, the output shaft of a permanent magnet spherical actuator (PMSA) is a rigid structure, which has the problems of small load weight ratio and high energy consumption. It is difficult to meet the application requirements of lightweight, high speed, and low energy consumption. In this paper, a PMSA is proposed as the driving motor for the rigid-flexible coupling system (R-FCS) of the robotic manipulator, and a flexible mechanical arm is used to replace the rigid output shaft of the PMSA to connect the hand claw to form a spherical joint. Firstly, the partial differential dynamics model of the robotic manipulator in the coordinate system is established based on the Hamilton method, and the R-FCS is constructed according to the momentum conservation equation combined with the rigid axis dynamics model of the PMSA. Then, a backstepping sliding mode controller (BSMC) is designed to form a closed-loop control system by selecting an appropriate nonlinear gain function to estimate the unknown disturbances of the system, and the stability of the controller is guaranteed by the Lyapunov theory. Finally, the simulation and experimental results verify that the R-FCS can achieve better trajectory tracking and provide an effective driving method for robotic manipulator spherical joint.

Vibration Suppression Trajectory Planning of Underwater Flexible Manipulators Based on Incremental Kriging-Assisted Optimization Algorithm

It is of great significance to expand the functions of submarines by carrying underwater manipulators with a large working space. To suppress the flexible vibration of underwater manipulators, an improved sparrow search algorithm (ISSA) combining an elite strategy and a sine algorithm is proposed for the trajectory planning of underwater flexible manipulators. In this method, the vibration evaluation function is established based on the precise dynamic model of the underwater flexible manipulator and considering complex motion and vibration constraints. Simulation results show that the ISSA algorithm requires only 1/3.68 of the time of PSO. Compared to PSO, SSA and the opposition-based learning sparrow search algorithm (OBLSSA), the optimization performance is improved by 17.3%, 13.1% and 9.7%, respectively. However, because the complex dynamics model of the underwater flexible manipulator leads to large computational effort and a long optimization time, ISSA is difficult to apply directly in practice. To obtain a large number of optimization results in a shorter time, an incremental Kriging-assisted ISSA (IKA-ISSA) is proposed in this paper. Simulation results show that IKA-ISSA has good nonlinear approximation ability and the optimization time is only 3% of that of the ISSA.

A digital twin framework for robust control of robotic-biological systems

Medical device regulatory standards are increasingly incorporating computational modelling and simulation to accommodate advanced manufacturing and device personalization. We present a method for robust testing of engineered soft tissue products involving a digital twin paradigm in combination with robotic systems. We developed and validated a digital twin framework for calibrating and controlling robotic-biological systems. A forward dynamics model of the robotic manipulator was developed, calibrated, and validated. After calibration, the accuracy of the digital twin in reproducing the experimental data improved in the time domain for all fourteen tested configurations and improved in frequency domain for nine configurations. We then demonstrated displacement control of a spring in lieu of a soft tissue element in a biological specimen. The simulated experiment matched the physical experiment with 0.09 mm (0.001%) root-mean-square error for a 2.9 mm (5.1%) length change. Finally, we demonstrated kinematic control of a digital twin of the knee through 70-degree passive flexion kinematics. The root-mean-square error was 2.00°, 0.57°, and 1.75° degrees for flexion, adduction, and internal rotations, respectively. The system well controlled novel mechanical elements and generated accurate kinematics in silico for a complex knee model. This calibration method could be applied to other situations where the specimen is poorly represented in the model environment (e.g., human or animal tissues), and the control system could be extended to track internal parameters such as tissue strain (e.g., control knee ligament strain). Further development of this framework can facilitate medical device testing and innovative biomechanics research.

Hierarchical Sliding Mode Control for the Trajectory Tracking of a Tendon-Driven Manipulator

Abstract The tracking control of tendon-driven manipulators has recently become a hot topic. However, the flexible elastic tendon introduces greater residual vibration, making it more difficult to control the trajectory tracking of the manipulator. In this paper, a dynamics model of the elastic tendon-driven manipulator (ETDM) that considers motion coupling is established. A hierarchical sliding mode control (HSMC) method is proposed to realize the trajectory tracking control of the ETDM. On the basis of the Lyapunov design method, the actuator subsliding manifold is defined as the first sliding manifold. The first sliding manifold is then used to construct the joint side subsliding manifold. Furthermore, the total sliding manifold is established based on the joint side sliding manifold and the actuator's sliding manifold. The stability of the proposed HSMC is proved using the Lyapunov stability theory. Finally, simulations and experiments are performed on a two-degree-of-freedom ETDM tracking desired trajectories to demonstrate the effectiveness of the proposed HSMC method. The proposed HSMC exhibits higher tracking accuracy compared with proportional–integral–derivative, and adaptive second-order fast nonsingular terminal sliding mode (SOFNTSM) controls in the simulations. The introduction of different disturbances reveals that HSMC has better robustness than proportional–integral–derivative control. Experimental results show that the maximum error of trajectory tracking is less than 0.025 rad.

Deep Deterministic Policy Gradient and Active Disturbance Rejection Controller based coordinated control for gearshift manipulator of driving robot

In order to improve the shift precision of the gearshift manipulator, a coordinated control method for the gearshift manipulator based on the Deep Deterministic Policy Gradient (DDPG) and Active Disturbance Rejection Controller (ADRC) is proposed. Firstly, the kinematic model and dynamics model of the gearshift manipulator are established. Secondly, a coordinated control strategy is proposed to solve the target rotation angle of the servo motor, so as to deal with the nonlinear trajectory problem caused by the mechanical decoupling strategy. Then, an ADRC-based controller and a DDPG-based parameter adjuster are proposed. A DDPG model is established to adaptively adjust the control parameters of ADRC through the target rotation angle, actual rotation angle and rotation angle error. Finally, the theoretical stability proof and experimental verification of the proposed method are conducted. The test results show that the mean absolute error of the proposed method is 34% and 18% lower than that of ADRC and fuzzy PID methods. It shows the effectiveness of the method.

Adaptive sliding mode robust control of manipulator driven by tendon-sheath based on HJI theory

The load side and the motor connected by flexible joints in the manipulators’ joint servo system. During the motion of manipulators driven by tendon-sheath, the status change of the end-effector will result in the change of the load side rotational inertia. The perturbation of the inertia of the load side will result in the modeling mismatch of the servo system. So the modeling uncertainty and the system robustness will decrease. An adaptive sliding mode robust control based on HJI (Hamilton-Jacobi-Issacs) theory is proposed in this paper to improve the robustness of the system. Firstly, according to D-H coordinate method, kinematics and dynamics models of the manipulator are established. Then, the basic strategy of adaptive sliding mode robust control is proposed. The variation of control parameters of a single joint of the manipulator is adjusted to reduce the control cost. Next, the sliding mode control law was established through the design of the Lyapunov function based on the HJI theory. The manipulator dynamics model was taken as the research object. The simulation analysis was conducted in uncertain parameters. Finally, a series of manipulator prototype experiments were carried out to proof our control theory. The experiment results show that our method can better solve the model uncertainty caused by the servo system. The adaptive sliding mode robust control strategy based on HJI theory has lower dependence on accurately modeling and stronger robustness.

Dynamic Analysis and Trajectory Tracking Control for a Parallel Manipulator with Joint Friction

To overcome the bearing capacity deficiencies of traditional serial hip joint simulators, complex trajectory simulation, among others, as well as a parallel manipulator with two pairs of artificial hip joints and two moving platforms are proposed. The movements and driving forces of the parallel manipulator under the required motion and loading are studied to provide a basis for further research. In this study, the modeling and analysis of inverse kinematics and dynamics for a parallel manipulator with joint friction are derived. In the inverse kinematic model, kinematic relationships between the linear module slider and the moving platform are established, and expressions for the slider are deduced. Subsequently, by analyzing the frictional forces of the artificial hip joint and thrust ball bearing, a rigid body dynamics model of the parallel manipulator with joint friction is established, which is subsequently decomposed into four driving torques associated with the moving platform, joint lever, slider, and screw. Finally, the difference in the kinematic performance between the two moving platforms is analyzed using numerical simulations and experiments, and the accuracy of the established model is verified.

Adaptive reinforcement learning in control design for cooperating manipulator systems

AbstractIn this paper, an optimal motion/force hybrid control strategy based on adaptive reinforcement learning (ARL) is proposed for cooperating manipulator systems. The optimal trajectory control and constraint force factor control, by using the Moore–Penrose pseudoinverse, are addressed to design the controller corresponding to the manipulator dynamic model. In addition, a frame of a different auxiliary term and an appropriate state‐variable vector are presented to address the non‐autonomous closed system with a time‐varying desired trajectory. The simultaneous actor/critic algorithm is implemented by optimizing the squared Bellman difference to be computed from the error of control policy and optimal control input. Moreover, the constraint force factor controller is discussed by a nonlinear technique after achieving the result of the constraint force factor. The tracking and convergence of ARL‐based optimal motion/force hybrid control strategy are validated in closed‐loop systems by a proposed Lyapunov function candidate. Finally, simulation results are implemented on a constrained system using three manipulators to verify the physical implementation of the presented optimal motion/force hybrid‐tracking control strategy.

Super-Twisting Control for trajectory tracking of a four-degree of freedom anthropomorphic robot manipulator

This work solves the regulation and tracking trajectories tasks for four degrees of freedom anthropomorphic robot manipulators. Two controllers are considered: a Super-Twisting controller (ST) and a Proportional Derivative with dynamics compensation (PD+) control. This comparison is carried out through numeric simulation of the dynamic model in the presence of disturbances using Matlab-Simulink software. Also, the tuning procedure of each controller is shown, as well as the stability criteria used for each case. The tunning of the ST controller is done considering the effects produced by an unknown Lipschitz disturbance; this guarantees robustness against this kind of disturbance. The results of the ST controller show the rejection of the disturbance, allowing the correct trajectory tracking. An algorithm based on the inverse kinematics solution is used to generate trajectories and their interpretation in generalized coordinates corresponding to the manipulator’s joint positions obtained through the geometric approach. In addition, we show the workspace, the manipulator parameterization, and the manipulator dynamic model through the Euler-Lagrange motion equations.

Backstepping Adaptive Trajectory Tracking Control of Manipulator with Uncertainties of Model and State

In this paper, a backstepping adaptive control method based on Kalman data fusion (KDF-BAC) is proposed to solve parametric and non-parametric as well as state variables in manipulator dynamics model. Firstly, the non-parametric uncertainties are regarded as internal disturbances to simplify the dynamics model of the manipulator. Secondly the backstepping control law is derived using the idea of recursive design, and the adaptive method is used to identify the uncertain parameters to provide the estimated value of unknown parameters for the backstepping control law. Finally, the Kalman data fusion method is used to reduce the uncertainty of the velocity information, and the most reliable estimate value is obtained, which is fed back to the controller. The simulation results show that the proposed KDF-BAC can reduce the influence of manipulator uncertainties on trajectory tracking performance, and has better control effect than adaptive control and backstepping control.

A review of dynamic parameters identification for manipulator control

Due to the important role of the manipulator dynamic model in manipulation control, the identification of the dynamic parameters of manipulators has become a research hotspot once again. In this paper, we present an overview of the modeling of manipulator dynamics, the optimization methods of excitation trajectory, the identification methods for dynamic parameters, and the identification of friction model parameters. First, the process and basic methods of identification of manipulation dynamic parameters are summarized, and the optimization methods for excitation trajectory are analyzed in detail. Further, friction model parameter identification and the physical feasibility of dynamic parameters are discussed. These are research hotspots associated with the identification of dynamic parameters of manipulators. The backgrounds and solutions of the problems of physical feasibility and identification of friction parameters are reviewed in this paper. Finally, neural networks and deep learning methods are discussed. The neural networks and deep learning methods have been used to improve the accuracy of identification. However, deep learning methods and neural networks need more in-depth analysis and experiments. At present, the instrumental variable method with complete physical feasibility constraints is an optimal choice for dynamic parameter identification. Moreover, this review aims to present the important theoretical foundations and research hotspots for the identification of manipulation dynamic parameters and help researchers determine future research areas.

Research on multi-task cooperative control and management method of industrial robot multi-manipulator

In order to overcome the problems of large cooperative control errors and trajectory tracking errors due to the lack of kinematics uncertainties in the traditional manipulator task control method, a new collaborative multi-task control method for industrial robots was designed. The Lagrange method was used to construct the single manipulator dynamics model, the Newton-Euler recursion method was used to construct the target object dynamics model, and then the multi-manipulator model was constructed. Based on the kinematics uncertainty, the multi-task cooperative control model of multi-manipulator was constructed, and the multi-task cooperative robust and adaptive controller was designed with the state feedback control, so as to realise the multi-task cooperative control. Experimental results show that the maximum error of multi-manipulator cooperative control is less than 20 mm, and the minimum error of object trajectory tracking is 10 mm, which proves that the proposed method can effectively achieve the design expectation.