Serial Robot Manipulators Research Articles

Disturbance Observer-Based Force Estimation and Fault Detection for Robotic Manipulator in Radioactive Environments

Robotic systems with force sensing have great potential for use in radioactive environments. In this study, a modified observer-based method was developed to calculate the unknown external force without adding a redundant sensor and achieve the fault detection in the presence of a force sensor. A dynamic model of a serial robotic manipulator was built and the design procedure for a modified disturbance observer (MDO) was established. The output of observer was then used to suppress the disturbance and generate the fault signature. Moreover, the stability analysis shows that the convergence of the observer error is ultimately bounded. Simulation results under the step and composite sinusoidal disturbance torque demonstrate the performance of the force estimation and disturbance rejection. The results, obtained using the Kinova Jaco2 robot manipulator, show that the estimated errors of the external force in X-Y-Z direction are bounded within ±0.5 N, ±2 N, and ±3 N, respectively. Finally, the effectiveness of fault detection is also verified by the experiment results.

An Active Fault-Tolerant Control for Robotic Manipulators Using Adaptive Non-Singular Fast Terminal Sliding Mode Control and Disturbance Observer

In this study, a fault-tolerant control (FTC) tactic using a sliding mode controller–observer method for uncertain and faulty robotic manipulators is proposed. First, a finite-time disturbance observer (DO) is proposed based on the sliding mode observer to approximate the lumped uncertainties and faults (LUaF). The observer offers high precision, quick convergence, low chattering, and finite-time convergence estimating information. Then, the estimated signal is employed to construct an adaptive non-singular fast terminal sliding mode control law, in which an adaptive law is employed to approximate the switching gain. This estimation helps the controller automatically adapt to the LUaF. Consequently, the combination of the proposed controller–observer approach delivers better qualities such as increased position tracking accuracy, reducing chattering effect, providing finite-time convergence, and robustness against the effect of the LUaF. The Lyapunov theory is employed to illustrate the robotic system’s stability and finite-time convergence. Finally, simulations using a 2-DOF serial robotic manipulator verify the efficacy of the proposed method.

Robust adaptive fuzzy sliding mode trajectory tracking control for serial robotic manipulators

The ever increasingly stringent performance requirements of industrial robotic applications highlight significant importance of advanced robust control designs for serial robots that are generally subject to various uncertainties and external disturbances. Therefore, this paper proposes and investigates the design and implementation of a robust adaptive fuzzy sliding mode controller in the task space for uncertain serial robotic manipulators. The sliding mode control is well known for its robustness to system parameter variations and external disturbances, and is thus a highly desirable and cost-effective approach to achieve high precision control task for serial robots. The proposed controller is designed based on a fuzzy logic approximation to accomplish trajectory tracking with high accuracy and simultaneously attenuate effects from uncertainties. In the controller, the high-frequency uncertain term is approximated by using a fuzzy logic system while the low-frequency term is adaptively updated in real time based on a parametric adaption law. The control efficacy and effectiveness of the proposed control algorithm are comparatively verified against a recently proposed conventional controller. The test results demonstrate that the proposed controller has better trajectory tracking performances and is more robust against large disturbances than the conventional controller under the same operating conditions.

Realization of Force Detection and Feedback Control for Slave Manipulator of Master/Slave Surgical Robot.

Robot-assisted minimally invasive surgery (MIS) has received increasing attention, both in the academic field and clinical operation. Master/slave control is the most widely adopted manipulation mode for surgical robots. Thus, sensing the force of the surgical instruments located at the end of the slave manipulator through the main manipulator is critical to the operation. This study mainly addressed the force detection of the surgical instrument and force feedback control of the serial surgical robotic arm. A measurement device was developed to record the tool end force from the slave manipulator. An elastic element with an orthogonal beam structure was designed to sense the strain induced by force interactions. The relationship between the acting force and the output voltage was obtained through experiment, and the three-dimensional force output was decomposed using an extreme learning machine algorithm while considering the nonlinearity. The control of the force from the slave manipulator end was achieved. An impedance control strategy was adopted to restrict the force interaction amplitude. Modeling, simulation, and experimental verification were completed on the serial robotic manipulator platform along with virtual control in the MATLAB/Simulink software environment. The experimental results show that the measured force from the slave manipulator can provide feedback for impedance control with a delay of 0.15 s.

An improved particle swarm optimization algorithm for inverse kinematics solution of multi-DOF serial robotic manipulators

An improved particle swarm optimization algorithm for inverse kinematics solution of multi-DOF serial robotic manipulators

Safety-Aware Nonlinear Model Predictive Control for Physical Human-Robot Interaction

This letter proposes a nonlinear model predictive control (NMPC) approach for real-time planning of point-to-point motions of serial robot manipulators that share their workspace with a human. The NMPC law solves a nonlinear program online, based on a kinematic model, and guarantees safety by constraining the robot speed within the time-varying bounds determined by the speed-and-separation-monitoring (SSM) principle. Closed-loop stability is proven in detail, and the performance (in terms of productivity) of the proposed method is tested against standard SSM schemes via experiments on a Kinova Gen3 robot.

A gravitational torque-compensated 2-DOF planar robotic arm design and its active control

Serial robot manipulators have their servo motors with reduction gears on the link joints. When it comes to hyper-redundant robots, this kind of joint actuation mechanism cannot be implemented since this makes hyper-redundant robots too heavy. Instead, cable driven mechanisms are preferred. However, the positioning accuracy is negatively affected by the cables. This paper addresses the positioning accuracy problem of cable driven hyper-redundant robots by employing a 2-DOF robotic arm whose modules are counter-balanced. While the actuators connected to the base actively do most of the work using cables and springs, light and compact actuators connected to the links produce precise motion. The method will result in compact, light and precise hyper-redundant robotic arms. The above-mentioned procedure governed by a control software including a 2D simulator developed is experimentally proved to be a feasible method to compensate the gravitational torque successfully.

Inertial Parameter Identification in Robotics: A Survey

This work aims at reviewing, analyzing and comparing a range of state-of-the-art approaches to inertial parameter identification in the context of robotics. We introduce “BIRDy (Benchmark for Identification of Robot Dynamics)”, an open-source Matlab toolbox, allowing a systematic and formal performance assessment of the considered identification algorithms on either simulated or real serial robot manipulators. Seventeen of the most widely used approaches found in the scientific literature are implemented and compared to each other, namely: the Inverse Dynamic Identification Model with Ordinary, Weighted, Iteratively Reweighted and Total Least-Squares (IDIM-OLS, -WLS, -IRLS, -TLS); the Instrumental Variables method (IDIM-IV), the Maximum Likelihood (ML) method; the Direct and Inverse Dynamic Identification Model approach (DIDIM); the Closed-Loop Output Error (CLOE) method; the Closed-Loop Input Error (CLIE) method; the Direct Dynamic Identification Model with Nonlinear Kalman Filtering (DDIM-NKF), the Adaline Neural Network (AdaNN), the Hopfield-Tank Recurrent Neural Network (HTRNN) and eventually a set of Physically Consistent (PC-) methods allowing the enforcement of parameter physicality using Semi-Definite Programming, namely the PC-IDIM-OLS, -WLS, -IRLS, PC-IDIM-IV, and PC-DIDIM. BIRDy is robot-agnostic and features a complete inertial parameter identification pipeline, from the generation of symbolic kinematic and dynamic models to the identification process itself. This includes functionalities for excitation trajectory computation as well as the collection and pre-processing of experiment data. In this work, the proposed methods are first evaluated in simulation, following a Monte Carlo scheme on models of the 6-DoF TX40 and RV2SQ industrial manipulators, before being tested on the real robot platforms. The robustness, precision, computational efficiency and context of application the different methods are investigated and discussed.

Optimal controller applied to robotic systems using covariant control equations

ABSTRACT We elaborate an algorithm to optimally control the actuators of robot manipulators. By regarding the robot kinetic energy as a metric, the dynamic model is formulated using tensors in a Riemannian geometry manifold. The control criterion is selected to be invariant, leading to covariant control equations that take advantage of the system dynamics. Motion equations are associated with conjugate control equations resulting in a system of second order ordinary differential equations. The suggested control method consists in solving this system of equations to obtain optimal joint forces off-line and use them as inputs in a control scheme to track optimal trajectories. Position-velocity feedback ensures proper behaviour. Motion control simulations show that the optimal trajectory can be tracked with good accuracy, even with imposed perturbations. A stability analysis for switched systems precedes real-time motion control experiments conducted on a serial robot manipulator with revolute joints to validate our method.

On the elastodynamics of a five-axis lightweight anthropomorphic robotic arm

This paper presents elastodynamic modeling and analysis for a five-axis lightweight robotic arm. Natural frequencies are derived and visualized within the dexterous workspace to show the overall performances and compare them to the frequencies when the robotics is with payload. The comparison shows that the payload has a relatively small influence to the first- and second-order frequencies. Sensitivity analysis is conducted, and the system's frequency is more sensitive to the second joint stiffness than the others. Moreover, observations from the displacement response analysis reveal that the robotics produces linear elastic displacements of the same level between the loaded and unloaded working modes but larger rotational deflections under the loaded working condition. The main contribution of this work lies in that a systematic approach of elastodynamic analysis for serial robotic manipulators is formulated, where the arm gravity and external load are taken into account to investigate the dynamic behaviors of the robotic arms, i.e., frequencies, sensitivity analysis, and displacement responses, under the loaded mode.

Symplectic Discrete-Time Control of Flexible-Joint Robots: Experiments with Two Links

Abstract We present the application and experimental validation of discrete-time control implementation based on symplectic integration - in short referred to as symplectic discrete-time control - to a serial robot manipulator with flexible-joints. Applying the implicit midpoint rule as a simple and popular symplectic integration scheme to both the model of the sampled system and the desired target dynamics, yields a discrete-time control law with the same structure as in continuous time. The arguments are, however, predicted stage values on the subsequent sampling interval resulting from a half implicit Euler step for the target system. The approach can be easily extended to a purely position-based feedback law. The experimental validation on a lightweight robot (KUKA LWR IV+) confirms the expected improvements at low sampling rates, with an increase in the assignable closed-loop stiffness of up to 29 %.

A novel robust finite-time tracking control of uncertain robotic manipulators with disturbances

In this article, an innovative technique to design a robust finite-time state feedback controller for a class of uncertain robotic manipulators is proposed. This controller aims to converge the state variables of the system to a small bound around the origin in a finite time. The main innovation of this article is transforming the model of an uncertain robotic manipulator into a new time-varying form to achieve the finite-time boundedness criteria using asymptotic stability methods. First, based on prior knowledge about the upper bound of uncertainties and disturbances, an innovative finite-time sliding mode controller is designed. Then, the innovative finite-time sliding mode controller is developed for finite-time tracking of time-varying reference signals by the outputs of the system. Finally, the efficiency of the proposed control laws is illustrated for serial robotic manipulators with any number of links through numerical simulations, and it is compared with the nonsingular terminal sliding mode control method as one of the most powerful finite-time techniques.

A new approach to design a finite‐time extended state observer: Uncertain robotic manipulators application

AbstractIn this article, a new approach to design a finite‐time extended state observer (FT‐ESO) for a class of uncertain nonlinear systems is proposed based on a novel coordinate transformation. First, a time‐varying extended state observer (ESO) is designed to make the estimation error uniformly bounded. In this regard, the dynamic of the estimation error is transformed into a linear system with a generalized disturbance term as its input. Then, the time‐varying gains of the ESO are designed to stabilize the system and consequently, to converge the estimation error to the neighborhood of zero and to remain uniformly bounded as t → t0 + T. In this article, without any knowledge about the disturbance, and uncertainties, a fault‐tolerant FT‐ESO is designed. Simulation results demonstrate the effectiveness of the proposed finite‐time ESO for serial robotic manipulators with any number of links, in the presence of disturbances and an unknown fault.

A novel recursive formulation for dynamic modeling and trajectory tracking control of multi-rigid-link robotic manipulators mounted on a mobile platform

This article establishes an innovative and general approach for the dynamic modeling and trajectory tracking control of a serial robotic manipulator with n-rigid links connected by revolute joints and mounted on an autonomous wheeled mobile platform. To this end, first the Gibbs–Appell formulation is applied to derive the motion equations of the mentioned robotic system in closed form. In fact, by using this dynamic method, one can eliminate the disadvantage of dealing with the Lagrange Multipliers that arise from nonholonomic system constraints. Then, based on a predictive control approach, a general recursive formulation is used to analytically obtain the kinematic control laws. This multivariable kinematic controller determines the desired values of linear and angular velocities for the mobile base and manipulator arms by minimizing a point-wise quadratic cost function for the predicted tracking errors between the current position and the reference trajectory of the system. Again, by relying on predictive control, the dynamic model of the system in state space form and the desired velocities obtained from the kinematic controller are exploited to find proper input control torques for the robotic mechanism in the presence of model uncertainties. Finally, a computer simulation is performed to demonstrate that the proposed algorithm can dynamically model and simultaneously control the trajectories of the mobile base and the end-effector of such a complicated and high-degree-of-freedom robotic system.

Optimum time-energy-jerk trajectory planning for serial robotic manipulators by reparameterized quintic NURBS curves

This paper deals with the trajectory planning for serial robotic manipulators passing through key points by minimizing execution time, energy consumption and joint jerks. Quintic NURBS curves are adopted to fit the trajectory, of which the trajectory is reparameterized with respect to time for generation of geometric path and motion laws, aiming at continuity of the robot velocity, acceleration and jerk. A trajectory planning approach for optimum robot performance is proposed by solving a multi-objective optimization problem to attain optimal curve parameters and distributed execution time along curve segments simultaneously. The proposed technique of trajectory planning is numerically illustrated with a robotic arm and evaluated by experimental measurements. The comparison of total execution time and joint dynamics with/without variables optimization shows the effectiveness of the proposed approach.

Continuous fuzzy nonsingular terminal sliding mode control of flexible joints robot manipulators based on nonlinear finite time observer in the presence of matched and mismatched uncertainties

In the present study, a continuous fuzzy nonsingular terminal sliding mode control (CFNTSMC) was proposed based on the nonlinear finite-time observer (NFTO), in a voltage-based manner, to control the position of a serial-chain n-link flexible-joint electrically-driven robot manipulator in presence of matched and mismatched uncertainties with the electrical and mechanical equations. In order to design the controller sliding surface, a continuous function was proposed, which could make the concerned sliding surface zero at the beginning of the control process so that the proposed control had only the sliding phase. Afterward, an NFTO was adopted to facilitate access to the information of the state variables and matched and mismatched uncertainties. According to the Lyapunov stability theorems, the closed-loop control system had finite-time global asymptotically stability in presence of NFTO and existing uncertainties. Finally, a fuzzy-based control law was proposed to avoid chattering phenomena in the control input, whose computational complexity was extremely low. In order to evaluate the efficiency of the proposed control, multi-stage simulations were conducted for a 2-link flexible-joint serial robot manipulator. The mathematical evidence and simulation results confirmed the desired performance of the proposed CFNTSMC.

Chaos-based Vortex Search algorithm for solving inverse kinematics problem of serial robot manipulators with offset wrist

Vortex Search (VS) algorithm is a single-solution-based optimization algorithm that requires the high maximum number of iterations (NOI) to solve optimization problems. In this study, two methods were proposed to reduce the required maximum NOI of the VS algorithm. These methods are based on using ten chaos maps with the VS algorithm and provide improvements in the exploration and exploitation abilities of the algorithm for reducing the required maximum NOI. Ten chaos-based VS algorithms (CVSs) were obtained by combining these methods with the VS algorithm. The performances of the CVS algorithms were tested by fifty benchmark functions. The results were evaluated in terms of some statistical values and a pairwise statistical test, Wilcoxon Signed-Rank Test. According to the results, it was found that the CVS algorithm obtained by using the Gauss–Mouse chaos map was the best algorithm. And also, it was shown that the proposed CVS algorithm performs better than the classical VS algorithm, even when its maximum NOI was ten times less than the maximum NOI of the VS algorithm. Additionally, the effects of the proposed methods in the exploration and the exploitation abilities of the VS algorithm were visually shown and a comparison about algorithm processing time was presented. In order to test the performance of the proposed CVS algorithm in solving the real-world optimization problems, the inverse kinematics problem of a six Degrees Of Freedom (DOF) serial robot manipulator with offset wrist was solved with both the proposed CVS algorithm and the VS algorithm for two different types of trajectories. The results showed that the proposed algorithm outperforms the VS algorithm in terms of the objective function values and position errors of the end-effector of the serial robot manipulator.

5-DOF serial robot manipulator design, application and inverse kinematic solution through analytical method and simple search technique

In this study a five Degrees of Freedom (DOF) serial robot manipulator was designed and implemented. The inverse kinematics problem, which has not exact analytical solution (only one inverse kinematic solution for a predefined end effector position in three dimensional space), of the robot mechanism was solved by using the combination of the analytical method and a simple search method. In order to use the proposed method in real-time applications, the method is designed so that it can be used to solve the inverse kinematics problem for the next point of the end-effector while the mechanism is working. Moreover, so that to control the implemented mechanism, a user interface program was written by using Visual Basic programming language. Finally, the proposed inverse kinematic solution method was tested on two different trajectories, an arc shaped trajectory that composed of 58 points and a linear trajectory divided into 29 points. The obtained results revealed that the proposed method can be used successfully in solving the inverse kinematic problem of the designed mechanism.

Resolved-Acceleration Control of Serial Robotic Manipulators Using Unit Dual Quaternions

A new method for resolved-acceleration control of serial chain manipulators has been proposed which uses dual quaternion representation of screw-based motion variables, i.e. pose, velocity and acceleration. The corresponding coupled controller considers both translation and orientation errors simultaneously for trajectory tracking and utilizes spatial acceleration to compute the feedforward compensation term for feedback linearization. The proposed coupled control law was validated on a robotic arm along a pre-defined trajectory. The controller demonstrated an improved trajectory tracking performance as compared to the conventional decoupled resolved-acceleration controller which treats translation and orientation error separately.

A Nonlinear Sliding Mode Controller of Serial Robot Manipulators With Two-Level Gain-Learning Ability

This article presents a learning robust controller for high-quality position tracking control of robot manipulators. A basic time-delay estimator is adopted to effectively approximate the system dynamics. A low-level control layer is structured from the control error as an indirect control objective using new nonlinear sliding-mode synthetization. To realize the control objective with desired transient time, a robust sliding mode control signal is then designed based on the obtained estimation results in a high-level control layer. To promptly suppress unpredictable disturbances, adaptation ability is integrated to the controller using two-level gain-learning laws. Reaching gains and sliding gain are automatically tuned for asymptotic control performance. Effectiveness of the designed controller is concretely confirmed by the Lyapunov-based stability criterion, comparative simulations, and real-time experiments.