DOF Robot Manipulator Research Articles

Design of an adaptive fuzzy-neural inference system-based control approach for robotic manipulators

This paper proposes an adaptive fuzzy-neural inference system (ANFIS)-based control approach for a six degrees of freedom (6-DoF) robotic manipulator. Its main objective is to guarantee the error convergence of the controlled system in the presence of uncertainties and unknown disturbances. The suggested controller is a parallel combination of an ANFIS network with a proportional-integral-derivative (PID) controller. The ANFIS system is used as an estimator to approximate a part of the system and then applied as the feedback linearization in the suggested control structure. The convergence of system errors to zero was proven using Barbalat’s lemma. The suggested control law combines the simplicity and ease of implementation of PID control with the estimation properties of ANFIS networks. The suggested approach was evaluated using a simulation study and further validated experimentally using the 6-DoF IRB-120 robotic manipulator (IRB-120-RM). The obtained results confirmed its superior performance and suitability for practical implementation to industrial actuators.

Nonlinear Functional Observer Design for Robot Manipulators

In this paper, a nonlinear functional observer (NFO) is first proposed for the control design of robot manipulators under model uncertainties, external disturbances, and a lack of joint velocity information. In principle, the proposed NFO can estimate not only lumped disturbances and uncertainties but also unmeasurable joint velocities, which are then fed back into the main controller. Compared to the well-known ESO design, the proposed NFO has a simpler structure, more accurate estimations, and less computational effort, and consequently, it is easier for practical implementation. Moreover, unnecessary observations of joint displacements are avoided when compared to the well-known extended state observer (ESO). Based on the Lyapunov theory, globally uniformly ultimately bounded estimation performance is guaranteed by the proposed NFO. Consequently, it is theoretically proven that the estimation performances of the NFO are better than those of the ESO. Simulations with a two-degree-of-freedom (2-DOF) robot manipulator are conducted to verify the effectiveness of the proposed algorithm in terms of not only the estimation performance but also the closed-loop control performance.

Task-Based Configuration Synthesis of an Underactuated Resilient Robot

A resilient robot can recover its original function after partial damage of the system. This paper develops an underactuated resilient robot that utilizes a combination of passive joints, active joints, adjustable links, and passive links. A novel method based on the genetic algorithm was proposed to determine the goal configuration of a partially damaged robot. The novelty of the method lies in the integration of both discrete and continuous variables. This model is illustrated by a 3-DOF robot manipulator in the simulation.

A Comparative Study of Swarm Intelligence Metaheuristics in UKF-Based Neural Training Applied to the Identification and Control of Robotic Manipulator

This work presents a comprehensive comparative analysis of four prominent swarm intelligence (SI) optimization algorithms: Ant Lion Optimizer (ALO), Bat Algorithm (BA), Grey Wolf Optimizer (GWO), and Moth Flame Optimization (MFO). When compared under the same conditions with other SI algorithms, the Particle Swarm Optimization (PSO) stands out. First, the Unscented Kalman Filter (UKF) parameters to be optimized are selected, and then each SI optimization algorithm is executed within an off-line simulation. Once the UKF initialization parameters P0, Q0, and R0 are obtained, they are applied in real-time in the decentralized neural block control (DNBC) scheme for the trajectory tracking task of a 2-DOF robot manipulator. Finally, the results are compared according to the criteria performance evaluation using each algorithm, along with CPU cost.

Optimization of PID trajectory tracking controller for a 3-DOF robotic manipulator using enhanced Artificial Bee Colony algorithm

This study introduces and compares two optimization techniques, the basic Artificial Bee Colony (ABC) and the enhanced Artificial Bee Colony with multi-elite guidance (MGABC), for determining optimal gains of a Proportional-Integral-Derivative (PID) controller in a 3 degrees of freedom (DOF) rigid link manipulator (RLM) system. The objective function used in the optimization process is a novel function that is based on the well-known Lyapunov stability functions. This function is evaluated against established error-based objective functions commonly used in control systems. The convergence curves of the optimization process demonstrate that the MGABC algorithm outperforms the basic ABC algorithm by effectively exploring the search space and avoiding local optima. The evaluation of the controller's performance in trajectory tracking reveals the superiority of the Lyapunov-based objective function (LBF), with significant improvements over other objective functions such as IAE, ISE, ITAE, MAE and MRSE. The optimized system demonstrates robustness to diverse disturbance conditions and uncertainty in the mass of the payload, while also exhibiting adaptability to joints flexibility without inducing any vibrations in the movement of the end-effector. The proposed techniques and objective function offer promising avenues for the optimization of PID controllers in various robotic applications.

Event-Triggered Learning Robust Tracking Control of Robotic Systems With Unknown Uncertainties

In this article, a tracking control problem for robotic systems with unknown uncertainties is addressed by using an event-triggered adaptive dynamic programming (ADP) method. First, the tracking control of a n-degree of freedom (DOF) robotic system is transformed to the optimal control of an auxiliary system such that the robust control design of the original system is feasible based on the ADP framework. To reduce the computational burden, an event-triggering mechanism is introduced. The cost function and the optimal control are approximated by a critic neural network (NN), where new weight updating laws are designed to relax the persistence of excitation condition and the requirement of initial stabilizing control. In addition, the stability analysis is rigorously given to prove that the closed-loop system is asymptotically stable while the NNs’ weight approximation error is uniformly ultimately bounded. Finally, a simulation case based on a 2-DOF robotic manipulator is given to verify the effectiveness of the designed control methods.

Swarm Intelligence Based Nonlinear Friction and Dynamic Parameters Identification for a 6-DOF Robotic Manipulator

Swarm Intelligence Based Nonlinear Friction and Dynamic Parameters Identification for a 6-DOF Robotic Manipulator

Fixed-Time RBFNN-Based Prescribed Performance Control for Robot Manipulators: Achieving Global Convergence and Control Performance Improvement

This paper proposes a fixed-time neural network-based prescribed performance control method (FNN-PPCM) for robot manipulators. A fixed-time sliding mode controller (SMC) is designed with its strengths and weaknesses in mind. However, to address the limitations of the controller, the paper suggests alternative approaches for achieving the desired control objective. To maintain stability during a robot’s operation, it is crucial to keep error states within a set range. To form the unconstrained systems corresponding to the robot’s constrained systems, we apply modified prescribed performance functions (PPFs) and transformed errors set. PPFs help regulate steady-state errors within a performance range that has symmetric boundaries around zero, thereby ensuring that the tracking error is zero when the transformed error is zero. Additionally, we use a singularity-free sliding surface designed using transformed errors to determine the fixed-time convergence interval and maximum allowable control errors during steady-state operation. To address lumped uncertainties, we employ a radial basis function neural network (RBFNN) that approximates their value directly. By selecting the transformed errors as the input for the RBFNN, we can minimize these errors while bounding the tracking errors. This results in a more accurate and faster estimation, which is superior to using tracking errors as the input for the RBFNN. The design procedure of our approach is based on fixed-time SMC combined with PPC. The method integrates an RBFNN for precise uncertainty estimation, unconstrained dynamics, and a fixed-time convergence sliding surface based on the transformed error. By using this design, we can achieve fixed-time prescribed performance, effectively address chattering, and only require a partial dynamics model of the robot. We conducted numerical simulations on a 3-DOF robot manipulator to confirm the effectiveness and superiority of the FNN-PPCM.

Design of a novel mixed interval type-2 fuzzy logic controller for 2-DOF robot manipulator with payload

This paper investigates a novel coupling-based mixed interval type-2 fuzzy logic controller (MIT2FLC) for trajectory tracking problems of highly nonlinear and complex robot manipulator plants. The major impediment for these types of plants is coupling between the robotic links at the time of operation. In addition, the performances of these plants are adversely affected by parameter uncertainties, random noise and external disturbances. Therefore, the MIT2FLC approach with an additional degree of freedom in the membership functions is proposed to efficiently deal with the problem of uncertainty and provide robust performance. The Grey wolf optimization (GWO) is implemented to calculate the optimal parameters of the proposed controller. For checking the performance, the MIT2FLC approach is compared with its type-1 fuzzy counterparts, namely mixed type-1 fuzzy logic controller (MT1FLC), type-1 fuzzy logic controller (T1FLC) and PID controllers. Robustness analysis of the proposed controller is investigated for external disturbances, varying system parameters, and random noise.

Modeling and Control of Robotic Manipulators Based on Symbolic Regression.

Model-based design is an important method of addressing problems associated with designing complex control systems. For complex dynamic systems in the presence of uncertainties, the modeling process from the first principles becomes extremely tedious and simplification in mechanism and parameter measurement may result in model inaccuracy. On the contrary, machine learning has the characteristic of fitting complicated equations, which makes it widely used in the research of model identification. However, it only brings a black-box model where the design schemes based on an analytical model cannot be applied. In this article, a simple and novel scheme for modeling and control of robotic manipulators is proposed; without prior knowledge, a dynamic model in an analytical form is obtained from artificially excited training data using the symbolic regression technique, and then, a controller is designed based on the dynamic model. Due to the ingenious experimental design, on one hand, the amount of training data is far less than the system identification method by machine learning. On the other hand, a decoupling feature is used in the model that greatly simplifies controller design. The experimental results on two-degree of freedom (DOF) and 6-DOF robotic manipulator simulators verify that the scheme is feasible and effective.

Modeling and Simulation for a Five DOF Robotic Arm Manipulator

Nowadays, service robots are used in a large number of applications in the service industry. With the booming service industry in third world countries, service robots are expected to be used in a wider range of applications, so it is of great importance to conduct research on service robots. This paper develops the model of a kinematic models for a 5 DOF robotic arm that used in service industry in SolidWorks. The robotic arm’s mathematical model is predicated on Denavit-Hartemberg (DH) method, which determine the robot joints angle vector. The procedure aims at describing the forward and inverse kinematic analysis, and finally determine the end effector’s position and orientation through the corresponding joints angle related to the coordinate system (CS), in order to control the robotic arm to reach the designated location in space. A simulation model of the robotic arm was built using the Matlab Robotics Toolbox to verify the correctness of forward and inverse kinematics, which also can simplify the calculation and analysis.

Robot Pose Estimation and Normal Trajectory Generation on Curved Surface Using an Enhanced Non-Contact Approach.

The use of robots for machining operations has become very popular in the last few decades. However, the challenge of the robotic-based machining process, such as surface finishing on curved surfaces, still persists. Prior studies (non-contact- and contact-based) have their own limitations, such as fixture error and surface friction. To cope with these challenges, this study proposes an advanced technique for path correction and normal trajectory generation while tracking a curved workpiece's surface. Initially, a key-point selection approach is used to estimate a reference workpiece's coordinates using a depth measuring tool. This approach overcomes the fixture errors and enables the robot to track the desired path, i.e., where the surface normal trajectory is needed. Subsequently, this study employs an attached RGB-D camera on the end-effector of the robot for determining the depth and angle between the robot and the contact surface, which nullifies surface friction issues. The point cloud information of the contact surface is employed by the pose correction algorithm to guarantee the robot's perpendicularity and constant contact with the surface. The efficiency of the proposed technique is analyzed by carrying out several experimental trials using a 6 DOF robot manipulator. The results reveal a better normal trajectory generation than previous state-of-the-art research, with an average angle and depth error of 1.8 degrees and 0.4 mm.

Design and Evaluation of an Intuitive Haptic Teleoperation Control System for 6-DoF Industrial Manipulators

Industrial robots are capable of performing automated tasks repeatedly, reliably and accurately. However, in some scenarios, human-in-the-loop control is required. In this case, having an intuitive system for moving the robot within the working environment is crucial. Additionally, the operator should be aided by sensory feedback to obtain a user-friendly robot control system. Haptic feedback is one way of achieving such a system. This paper designs and assesses an intuitive teleoperation system for controlling an industrial 6-DoF robotic manipulator using a Geomagic Touch haptic interface. The system utilises both virtual environment-induced and physical sensor-induced haptic feedback to provide the user with both a higher amount of environmental awareness and additional safety while manoeuvering the robot within its working area. Different tests show that the system is capable of fully stopping the manipulator without colliding with the environment, and preventing it from entering singularity states with Cartesian end effector velocities of up to 0.25 m/s. Additionally, an operator is capable of executing low-tolerance end effector positioning tasks (∼0.5 mm) with high-frequency control of the robot (∼100 Hz). Fourteen inexperienced volunteers were asked to perform a typical object removal and writing task to gauge the intuitiveness of the system. It was found that when repeating the same test for a second time, the participants performed 22.2% faster on average. The results for the second attempt also became significantly more consistent between participants, as the inter quartile range dropped by 82.7% (from 52 s on the first attempt to 9 s on the second).

RGB-D 카메라를 이용한 로봇 매니퓰레이터 기구학 캘리브레이션

This paper proposed the robot manipulator kinematic calibration method based on a distance error model using an easy-touse RGB-D(Depth) camera. The RGB-D camera attached to the robot end effector measures the checkerboard’s corner 3-D position in two different positions. The homogeneous transformation matrix having rotation matrix and translation vector between two different RGB-D camera positions is calculated from the acquired position data from the RGB-D camera. The distance error is measured by comparing the distance moved by the RGB-D camera from the derived homogeneous transformation matrix with the distance moved by the robot manipulator. Simulation and experiments have been conducted to verify the proposed robot manipulator kinematic calibration method with a 6-DOF robot manipulator.

Non-Singular Terminal Sliding Mode Controller with Nonlinear Disturbance Observer for Robotic Manipulator

Aiming at the problems of model uncertainties and other external interference in trajectory tracking control of n-degree of freedom manipulators, a non-singular terminal sliding mode controller with nonlinear disturbance observer (NDO–NTSMC) trajectory tracking method is proposed. A nonlinear disturbance observer (NDO) is designed to forecast and compensate the system external interference, and a nonlinear gain is designed to make the observer error achieve the expected exponential convergence rate so that the feedforward compensation control is realized. Then, a non-singular terminal sliding mode controller (NTSMC) built on nonlinear sliding surface is designed to surmount the singularity fault of classic terminal sliding mode controller (TSMC). Therefore, the time required from any initial state to reach the equilibrium point is finite. In addition, the redesign of the sliding surface ensures the tracking accuracy rate of uncertain systems. Then, based on Lyapunov principle, we complete the stability analysis. Finally, the method is applied to a 2-DOF robotic manipulator model compared with other methods. In the simulation, the manipulator needs to track a continuous trajectory under the condition of joint friction disturbance. The simulation result shows that the torque output of the designed method is chattering-free and smooth, and the tracking effect is precise. Simulation results indicate that the proposed controller has the advantages of excellent tracking performance, strong robustness, and a fast response.

Design of PID, FLC and Sliding Mode Controller for 2-DOF Robotic Manipulator: A Comparative Study

Controlling the manipulators in a precise manner is a challenging task. To overcome this difficulty around the world, many researchers have developed various control algorithms but are not providing optimal results. To obtain the optimal results in the current research the authors designed a proportional, integral, and derivative (PID) controller, fuzzy logic controller (FLC), and sliding mode controller (SMC) for a 2-DOF manipulator. The concept of forward and inverse kinematics was initially solved after assigning the D-H parameters for each joint. The purpose of forward or direct kinematics is to obtain the position and orientation of the end effector. Further, the concept of inverse kinematics is used to estimate the joint angles. Later on, the Lagrange-Euler formulation was used to calculate the dynamics of the 2-DOF manipulator, which is required to estimate the torque required for each joint of the robotics arm. The main goal of this research problem is to optimize the angular error between the two successive events. Finally, the developed algorithm is compared with the existing algorithms such as PID and Fuzzy logic controller.

Experimental Study of Robotic Polishing Process for Complex Violin Surface

This paper presents a robotic polishing process for complex violin surfaces to increase efficiency and minimize the cost and consumed time caused by using labor and traditional polishing machines. The polishing process is implemented based on modeling a smooth path, controlled contact force embedded with gravity compensation and material removal depth. A cubic Non-Uniform Rational Bases-Spline (NURBS) interpolation curve combined with an S-curve trajectory model is used to generate a smooth polishing path on a complex violin surface to achieve stable motion during the polishing process. An online admittance controller added to the fast gravity compensation algorithm maintains an accurate polishing force for equal removal depth on all polished surface areas. Then, based on Pythagorean theory, the removal depth model is calculated for the violin’s complex surface before and after polishing to estimate the accuracy of the polishing process. Experimental studies were conducted by polishing a wooden surface using the 6DOF robot manipulator to validate this methodology. The experimental results demonstrated that the robot had accurate polishing force based on the online admittance controller with gravity compensation. It also showed a precise proportional uniformity of removal depths at the different normal forces of 10, 15, and 20 N. The final results indicated that the proposed experimental polishing approach is accurate and polishes complex surfaces effectively.

Accurate TCP Position and Orientation Trajectory Generation in 6DOF Robotic Manipulators and CNC Machine Tools using FIR Filtering and Haversine Synchronisation

The use of robotics in manufacturing is rapidly growing. One promising application in particular is the use of robots as machining platforms. Robotic machining offers many advantages such as large operating envelopes, flexibility in operations and low installation and running costs. Robotic machining platforms much like computer numerically controlled (CNC) machine tools are commanded by numerical controllers (NCs) using the well established G-code part programming language (ISO-6983). Toolpaths are defined in the work piece coordinate system (WCS) as a series of discrete tool centre point (TCP) positions and tool orientations. The role of the NC is to generate smooth feed drive or robot joint commands such that the tool or end effector travels along the commanded toolpath whilst satisfying TCP position and orientation tolerances and robot/machine tool kinematic constraints. Interpolating tool orientations directly in the WCS requires complex real-time spherical interpolation computations. Overcoming this challenge, this paper introduces a C4 continuous tool path trajectory generation method for 6DOF motion. The tool centre point position is smoothed in the WCS, however, the tool orientation is smoothed directly in the rotary coordinate system without the requirement for spherical interpolation. The interpolated tool orientation trajectory follows the path of the great circle arc using the modified Haversine method eliminating non-linear interpolation errors present in linear interpolation of rotary axis positions (machine coordinate system interpolation). The proposed method is validated by simulation of a 6DOF robotic manipulator.

Kinematics analysis of a medication delivery robot based on a 3-DOF manipulator

Medication delivery robots, as a type of service robots, are becoming increasingly important in the field of helping the elderly and disabled. Conventional drug delivery robots focus on mobility and often lack the ability to pick and place drugs independently. Based on this phenomenon, a new medication delivery robot structure based on a 3-DOF robot manipulator is designed in this paper. According to the motion process, the 3-DOF manipulator is modeled and simulated. Meanwhile, the influence of different trajectory planning methods on the drug taking and discharging process are compared, which provides theoretical and technical support for the research of robots in related fields.

Fuzzy TSMCSPO for Trajectory Tracking of Nuclear Reactor Dismantlement Robot Manipulator

In this study, a fuzzy logic system to tune the parameters of terminal sliding mode control with a sliding perturbation observer (TSMCSPO) in real-time is proposed according to the system’s state changes to improve the control performance of TSMCSPO for a 5-DOF robot manipulator used for nuclear reactor dismantlement. Accurate trajectory tracking control is required when using a multi-DOF robot manipulator to cut the decommissioned nuclear reactor. A TSMCSPO scheme using TSMC in SPO has been proposed in previous research, which improves the estimation and convergence performance of traditional SMCSPO. In TSMCSPO, the parameters of the controller are important to enhance the control performance. Moreover, due to the influence of the working environment and the uncertainty of the system, the characteristics of the system will be changed during the trajectory tracking control of the multi-degree-of-freedom robot manipulator. Therefore, real-time optimal parameter tuning is required. In this regard, using fuzzy logic to tune the parameters is an effective method. By designing fuzzy rules, the controller parameters can be optimized. In this study, the proposed algorithm and, the trajectory tracking control simulation of the robot manipulator in the nuclear reactor vessel internal (RVI) are implemented in MATLAB/Simulink environment. The control algorithm is verified by the experiments on a real robot manipulator platform. The outstanding control performance of the algorithm is demonstrated by comparing the trajectory tracking error between the proposed fuzzy TSMCSPO scheme and TSMCSPO with fixed parameters.