Multi-joint Manipulator Research Articles

Exponential fast terminal sliding mode based finite-time control of manipulator systems

A finite-time controller is designed based on an exponential fast terminal sliding mode for multi-joint manipulators, which solves the problem of slow convergence in trajectory tracking. By adding an exponential function on the basis of fast terminal sliding mode, the convergence speed is greatly improved. In addition, a new fast variable power reaching law is designed to reduce the chattering problem. The approaching speed is adjusted by changing the size of the power term adaptively in different stages of the approximation, so that the states of the system enter the sliding mode surface faster and more stablely. The controller realizes the finite-time trajectory tracking of the manipulator, shorens the convergence time and weakens the chattering frequency. Finally, the above views are verified by simulation and comparison experiments.

Calculation Method of Static Friction Forces for Multi-Joint Manipulators

Calculation Method of Static Friction Forces for Multi-Joint Manipulators

Adaptive fuzzy sliding mode control of the manipulator based on an improved super-twisting algorithm

The high-precision trajectory tracking and chatter-free performance of the manipulators are of great significance to the improvement of the automation level. Therefore, in view of the problems of chattering and tracking performance degradation of multi-joint manipulators caused by uncertain factors such as external disturbances and modeling errors, an adaptive fuzzy sliding mode control of the manipulator based on an improved super-twisting algorithm is proposed. First, fuzzy control is chosen to approximate the uncertainty of the manipulator model, which reduces the influence of unknown disturbances on the system, and then the adaptive law is utilized to automatically modify and improve the fuzzy rules to enhance the approximation capability; Second, an improved super-twisting algorithm is used on this basis to put the discontinuous term into the integral term to avoid chattering and eliminate the approximation error; Finally, a comparative simulation experiment is carried out with the 3-DOF manipulator as the research object. The experimental results show that the method in this paper improves the convergence speed by up to 69.74% and the tracking error accuracy by up to 85.11% over the fuzzy control based on adaptive reaching law with no chattering and little difference in control inputs, thus verifying the feasibility and superiority of the method.

Terminal sliding mode control based on recursive stochastic configuration network for ball mill lining replacement process

The precise control of multi-joint manipulators presents significant challenges in the context of replacing internal liners of large grinding machines. These challenges arise due to the uncertainty of system parameters and external disturbance. A terminal sliding mode control based on a recursive stochastic configuration network is proposed to ensure that the system is stable, and it reduces system oscillations in this article. In addition, a disturbance observer based on recursive stochastic configuration network is designed to estimate and compensate for the uncertainties in the system, thereby improving the robustness of the system. To enhance the realism of the simulations, the simulation modeling process incorporated uncertainty parameters and external disturbance information. The simulation results demonstrated the effectiveness of the proposed terminal sliding mode control approach in meeting the practical control requirements. This accomplishment presents a valuable new method for controlling mechanical arms. The method is applicable under conditions of system uncertainty and external disturbance.

Sliding mode control based on particle swarm optimization neural network and adaptive reaching law

This paper presents a sliding mode control based on particle swarm optimization neural network and adaptive reaching law, and the proposed control method solves the problem of chattering and tracking performance degradation of a multi-joint manipulator caused by uncertainties such as external disturbances and modeling error. First, to address the problem that the precise dynamic system of the manipulator is difficult to establish, the radial basis function neural network (RBFNN) is used to approximate the uncertainty of the manipulator model, and the parameters of the neural network are optimized through the adaptive natural selection particle swarm optimization algorithm (ASelPSO) to improve the approximation ability and reduce the approximation error. Second, to eliminate chattering, adaptive reaching law is selected to improve the dynamic quality of approaching motion. Finally, a comparative simulation experiment is carried out with a 3-DOF manipulator as the research object. The results show that the control method has obvious improvements in eliminating chattering, improving tracking accuracy, and increasing convergence speed, which verifies the feasibility and superiority of the control scheme.

Application of Multiscale Method in Nonlinear Vibration Problem of Multi-joint Manipulator

In this paper, the nonlinear two-joint manipulator system is taken as the research object. Firstly, the dynamic equation of the system is established by using the Lagrange method. In the case of ignoring some high-order nonlinear terms in the equations, the multi-scale method in the nonlinear vibration approximate analytical method is used to solve the nonlinear multi-joint manipulator system model, and the approximate analytical solution of the system is obtained. The theoretical analysis results are verified by numerical calculation. The results show that the analytical solution is in good agreement with the numerical solution, which verifies the effectiveness and accuracy of the multi-scale method.

Dynamic Movement Primitives Based Robot Skills Learning

In this article, a robot skills learning framework is developed, which considers both motion modeling and execution. In order to enable the robot to learn skills from demonstrations, a learning method called dynamic movement primitives (DMPs) is introduced to model motion. A staged teaching strategy is integrated into DMPs frameworks to enhance the generality such that the complicated tasks can be also performed for multi-joint manipulators. The DMP connection method is used to make an accurate and smooth transition in position and velocity space to connect complex motion sequences. In addition, motions are categorized into different goals and durations. It is worth mentioning that an adaptive neural networks (NNs) control method is proposed to achieve highly accurate trajectory tracking and to ensure the performance of action execution, which is beneficial to the improvement of reliability of the skills learning system. The experiment test on the Baxter robot verifies the effectiveness of the proposed method.

A novel multi-point trajectory generator for robotic manipulators based on piecewise motion profile and series-parallel analytical strategy

A novel time-optimal multi-point trajectory generator (TMTG) based on piecewise motion profile and series-parallel analytical strategy is proposed for robotic manipulators to improve the motion accuracy and efficiency. The multi-point trajectory is composed of various subtrajectories to eliminate overshoot and undershoot according to the constrained velocity direction at the desired points. The piecewise motion profile is acted as a fundamental subtrajectory model for a jerk-continuous subtrajectory. A series-parallel analytical strategy provides a closed-form solution for the time-optimal multi-point trajectories. The series analytical strategy for a single-joint trajectory generates the time-optimal trajectory and the upper velocities of the desired points. The parallel analytical strategy for multi-joint subtrajectories synchronizes the joint movements to enhance the multi-joint motion accuracy. The simulations and experiments conducted on a manipulator validate that TMTG outperforms state-of-the-art approaches in terms of accuracy and efficiency for a multi-joint manipulator. The absolute position errors of all joint by TMTG at the end of motion are within 0.007°. Therefore, TMTG is a functional solution for manipulators to construct time-optimal multi-point trajectories.

Parameter-Tunable RBF Neural Network Control Facing Dual-Joint Manipulators

In order to improve the parameter control effect of the double-joint manipulator, this paper combines the RBF neural network to control the parameters of the double-joint manipulator and the command filtering backstep impedance control method based on the RBF neural network is effectively applied to the multijoint manipulator. Moreover, this paper introduces the filter error compensation mechanism into the controller design to eliminate the influence caused by the filter error. Finally, the effectiveness and superiority of the command filtering backstep impedance control scheme of the multijoint manipulator adaptive neural network designed in this paper is verified by simulation experiments. The experimental research results verify that the parameter-tunable RBF neural network control method facing the dual-joint manipulator has a certain effect on the parameter control process of the dual-joint manipulator and can effectively improve the motion accuracy of the dual-joint manipulator.

An Adaptive Fuzzy Control Model for Multi-Joint Manipulators

Multi-joint manipulator systems are subject to nonlinear influences such as frictional characteristics, random disturbances and load variations. To account for uncertain disturbances in the operation of manipulators, we propose an adaptive manipulator control method based on a multi-joint fuzzy system, in which the upper bound information of the fuzzy system is constant and the state variables of the manipulator control system are measurable. The control algorithm of the system is a MIMO (multi-input-multi-output) fuzzy system that can approximate system error by using a robust adaptive control law to eliminate the shadow caused by approximation error. It can ensure the stability of complex manipulator control systems and reduce the number of fuzzy rules required. Comparison of experimental and simulation data shows that the controller designed using this algorithm has highly-precise trajectory-tracking control and can control robotic systems with complex characteristics of non-linearity, coupling and uncertainty. Therefore, the proposed algorithm has good practical application prospects and promotes the development of complex control systems.

Inverse dynamics of underactuated planar manipulators without inertial coupling singularities

In this paper, we present a model for the inverse dynamics of underactuated manipulators that is free from inertial coupling singularities. The framework’s main idea is to include a small-amplitude wave on the trajectory of the rotating active joints. First, we derive the modified nonlinear dynamics for the multijoint manipulators with multiple degrees-of-freedom (DoF). Next, a 4-DoF mass-rotating underactuated manipulator with two passive and two active joints is chosen. Then, a condition assuming the positive definiteness of the inertia matrix is developed to have the singularity-free inverse dynamics. Finally, we analytically study how singularities can be avoided and show an example simulation with a feed-forward control at the singular configuration.

Development and application of a moxibustion instrument with multi-jointed manipulator

In view of the limitations of the existing moxibustion instruments, i.e. possible accidental injury when using moxibustion instruments, the negative effects of products from moxibustion instruments on treatment efficacy and health of medical staff and patients, a moxibustion instrument with multi-jointed manipulator is designed. This moxibustion instrument could accurately control the temperature, maintain a safe moxibustion distance, automatically process the burning ashes of moxa and selectively handle moxa smoke. The experimental results shows that this instrument could maintain the constant temperature of target acupoint, reduce the risk of empyrosis, and reasonably deal with the products of moxibustion. The purification rate of moxa smoke is 44.9%, which not only ensures the therapeutic effect of moxa smoke, but also reduces the negative effects of high-concentration moxa smoke on the health of medical staff and patients.

A Novel Posture Positioning Method for Multi-Joint Manipulators

Safety and automatic control are extremely important when operating manipulators. For large engineering manipulators, the main challenge is to accurately recognize the posture of all arm segments. In classical sensing methods, the accuracy of an inclinometer is easily affected by the elastic deformation in the manipulator's arms. This results in big error accumulations when sensing the angle of joints between arms one by one. In addition, the sensing method based on machine vision is not suitable for such kind of outdoor working situation yet. In this paper, we propose a novel posture positioning method for multi-joint manipulators based on wireless sensor network localization. The posture sensing problem is formulated as a Nearest-Euclidean-Distance-Matrix (NEDM) model. The resulting approach is referred to as EDM-based posture positioning approach (EPP) and it satisfies the following guiding principles: (i) The posture of each arm segment on a multi-joint manipulator must be estimated as accurately as possible; (ii) The approach must be computationally fast; (iii) The designed approach should not be susceptible to obstructions. To further improve accuracy, we explore the inherent structure of manipulators, i.e., fixed-arm length. This is naturally presented as linear constraints in the NEDM model. For concrete pumps, a typical multi-joint manipulator, the mechanical property that all arm segments always lie in a 2D plane is used for dimension-reduction operation. Simulation and experimental results show that the proposed method provides efficient solutions for posture sensing problem and can obtain preferable localization performance with faster speed than applying the existing localization methods.

Capturing the dynamic target by the robot manipulator in high-dimensional configuration space map

In robotics, the tough problem about the dynamic target capturing consists of tracking the target by the robot manipulator and grasping the target by the robot finger. For the sake of space, this article deals with only the first problem, tracking the dynamic target by the robot manipulator. The traditional approaches of capturing the dynamic target may work well when they are employed in low-dimensional space by reinforcement learning or physical modeling. However, they fail to work well in high-dimensional space. The traditional approaches have four limitations with respect to Cartesian space, configuration space, reinforcement learning, and physical modeling. To overcome these limitations, this article implements improved dynamic A* algorithm in high-dimensional configuration space map to capture the target. First, a space injection model injects the collision detection and target position from the Cartesian space into the configuration space to construct a high-dimensional map. Then, the target capturing method including the improved dynamic A* algorithm is applied on the map to track and capture the target. Finally, the experiment performed in time-varying environment and the dynamic target achieves a reliable result. This article has proposed an approach that makes the robot manipulator motion planning more accurate in high-dimensional dynamic configuration space. This approach enables the multi-joint manipulator to avoid the obstacle while tracking the target in high-dimensional configuration space. It takes the advantages of heuristic algorithms in the process of target capturing method designing. It adds precision and speed to target tracking. The success of the approach may apply to any industrial robot tracking target, surgical operation, and space probes. And, it may lay a solid foundation for dynamics control with a scope for future investigations.

Development of a Rigidity Tunable Flexible Joint Using Magneto-Rheological Compounds-Toward a Multijoint Manipulator for Laparoscopic Surgery.

Laparoscopic surgery is a representative operative method of minimally invasive surgery. However, most laparoscopic hand instruments consist of rigid and straight structures, which have serious limitations such as interference by the instruments and limited field of view of the endoscope. To improve the flexibility and dexterity of these instruments, we propose a new concept of a multijoint manipulator using a variable stiffness mechanism. The manipulator uses a magneto-rheological compound (MRC) whose rheological properties can be tuned by an external magnetic field. In this study, we changed the shape of the electromagnet and MRC to improve the performance of the variable stiffness joint we previously fabricated; further, we fabricated a prototype and performed basic evaluation of the joint using this prototype. The MRC was fabricated by mixing carbonyl iron particles and glycerol. The prototype single joint was assembled by combining MRC and electromagnets. The configuration of the joint indicates that it has a closed magnetic circuit. To examine the basic properties of the joint, we conducted preliminary experiments such as elastic modulus measurement and rigidity evaluation. We confirmed that the elastic modulus increased when a magnetic field was applied. The rigidity of the joint was also verified under bending conditions. Our results confirmed that the stiffness of the new joint changed significantly compared with the old joint depending on the presence or absence of a magnetic field, and the performance of the new joint also improved.

Robust fault tolerant tracking control for the multi-joint manipulator based on operator theory

Uncertainties and fault signals usually exist in the real systems and it is difficult to maintain stability and tracking performance for the real applications. Therefore, it is important to study the robust fault tolerant tracking control (RFTTC) for uncertain nonlinear feedback systems (NFS). Generally, the RFTTC schemes are designed for the uncertain NFS by using operator theory based robust right coprime factorization (ORRCF) method. In details, firstly, one novel RFTTC scheme is proposed for the uncertain NFS, wherein the fault tolerant controllers as well as the tracking one are designed based on the internal model. Secondly, the other new RFTTC scheme is proposed where the robust fault tolerant controllers are combined with the compensation operator and the tracking controller. By the proposed two design schemes, the impacts induced by the faults on the system is eliminated, and the entire nonlinear feedback system holds strong robustness and perfect tracking performance. Finally, simulation results of the multi-joint manipulator are used to indicate the validity of proposed two design schemes.

Obstacle Avoidance and Path Planning for Multi-Joint Manipulator in a Space Robot

An obstacle avoidance and path planning algorithm for a multi-joint manipulator in a space robot is presented in this paper. In this paper, the end-effector of the manipulator is used to capture some special target in a space environment with obstacles. To ensure the safety of the operation, a collision-free path from the initial position to the target position is essential. Therefore, an obstacle avoidance and path planning algorithm based on the Rapidly-Exploring Random Tree (RRT) algorithm and the Forward and Backward Reaching Inverse Kinematics (FABRIK) algorithm is presented in this paper. First, a path planning algorithm based on the Rapidly-Exploring Random Tree (RRT) algorithm is designed for the multi-joint manipulator. Further, a method to generate a random point by artificial guidance is introduced for a higher searching speed. The RRT algorithm can effectively explore the entire workspace and find a feasible path without collision for the end-effector. To calculate the positions of each joint, the Forward and Backward Reaching Inverse Kinematics (FABRIK) algorithm is introduced and improved for the problem of inverse kinematics. The FABRIK algorithm avoids the use of rotational angles or matrices, and instead finds each joint position by locating a point on a line, and thus, it has a low computational cost. Therefore, the improved obstacle avoidance and path planning algorithm can quickly plan a feasible path for the multi-joint manipulator in a space environment with obstacles. A numerical simulation is carried out to analyze the proposed obstacle avoidance and path planning method. It is observed that the method finds a feasible path without collision for the multi-joint manipulator with a low computational cost. These results validated the effectiveness of the proposed method for path planning to avoid the obstacles.

Design and structural analysis of an inflatable coated fabric manipulation arm

This paper presents the structural behaviour of a novel inflatable manipulation arm, which consists of inflatable coated fabric tubes and bellows-type joints. In order to minimise the actuation moment, a rotational joint with creases, analogous to a spacesuit elbow, has been introduced. The biaxial tensile and shear tests have been carried out to evaluate the elastic constants of the PVC-coated polyester fabric membranes of the inflatable arm. Finite element analysis has been carried out to predict the mechanical behaviour of the inflated arm structure. Effects of the internal pressure on the arm deflection, and influence of crease geometric parameters, e.g. number, width and depth on the rotation performance are evaluated. A prototype of the inflatable arm has been developed to perform an experimental investigation on its rotational behaviour. Good correlations have been found between the predictions and the experimental results. The current work on a representative single-joint inflatable arm unit provides the rationale and knowledge to develop long range multi-joint manipulators.

A Passive Snake-like Arm Based on Wire-driven Mechanism for Laparoscopic Surgery

In this paper, a passive snake-like arm based on wire-driven mechanism is proposed, which can be connected with remote center of motion (RCM) mechanism to form a laparoscope-holder robot and be used in laparoscopic surgery. The purpose of the snake-like arm is to replace traditional multi-joint manipulator, making it possible for the robot to assist the surgeon in the best position in a surgery. The snake-like arm consists of a main body composed of four arm units and a wire locking device with 12 wire locking units. The arm units are connected by hook hinges. Four classes force amplificatory structure is adopted in the wire locking unit. The detail structure and working principle of the wire locking device are proposed in this paper. The wire tensioning and guiding mechanism as well as the layout of wires are also introduced in this paper. Mechanical calculation of the force amplicatory structure is proposed in this paper to ensure that sufficient locking force can be provided. The workspace of the snake-like arm is drawn by simulation, and the large workspace allows the snake-like arm to be used in most laparoscopic surgeries.

Motion Control of 6-DOF Manipulator Based on EtherCAT

We introduce a real-time motion control system that uses the EtherCAT protocol and apply it to a manipulator with six degrees of freedom. The complexity of a multi-joint manipulator leads to higher requirements for synchronous and real-time performance. EtherCAT technology can greatly improve the performance in terms of accuracy, speed, capability, and band width in industrial control, which is crucial in our robot projects. In this paper, we discuss a servo motion control system based on EtherCAT using IgH as the open-source master station. A Linux operating system is adopted because of the advantages of open-source, high-efficiency, and high-stability operation as well as multi-platform support, which provide more flexibility, freedom, and extendability to developers. Considerable research has been conducted to explore EtherCAT technologies, completely implementing home-made codes with the aid of open-source libraries, debugging the master-slave communication process, and testing the resulting motion controller running on Linux or POSIX-compatible operating systems. To improve the real-time response of servo control, a real-time Xenomai kernel has been compiled, adopted, and tested, and it showed significant enhancement of the real time of a servo motion control system. Furthermore, we explore trajectory planning and inverse kinematic solutions. A trajectory planning method based on B-spline interpolation of three degrees, which makes each part of the trajectory planning curve have relative independence and continuity, is proposed for the kinematic trajectory planning problem in Cartesian space. A coordinate system is established using the modified D-H parameters method to obtain the inverse kinematics of the manipulator. The simulation and experimental results show that the calculation speed of inverse solutions is excellent and the motion of the manipulator is continuous and smooth.