Robot Manipulators In Presence Research Articles

Adaptive fuzzy global coupled nonsingular fast terminal sliding mode control of n-rigid-link elastic-joint robot manipulators in presence of uncertainties

This paper presented a high-speed and low power consumption adaptive fuzzy global coupled nonsingular fast terminal sliding mode control (NFTSMC) to the position tracking control of the n-rigid-link elastic-joint robot manipulators (RMs) in the presence of the uncertainties. To the applying the proposed scheme to the system that is in the category of the under-actuated mechanical systems, first, the dynamic equations are divided into two subsystems in such a way that they are coupled together using an intermediate variable. Then, a fuzzy approximator in fuzzy global coupled NFTSMC is proposed to eliminate the singularity, increase the tracking speed, and reduce the amplitude of the chattering phenomenon in the control input signal. Finally, to estimate the boundary of the system uncertainties, an adaptive fuzzy global coupled NFTSMC with only one adaptive law is proposed which able to overcome the uncertainties effect, eliminate the chatting phenomenon and reduce the amplitude of the control input to an actual limit. The computational complexity of the fuzzy parts is extremely low. The mathematical proof guarantees the global finite-time asymptotic stability of the closed-loop control system under fuzzy and adaptive fuzzy global coupled NFTSMC in the presence of the uncertainties. The effectiveness of the proposed scheme is demonstrated by the qualitative and quantitative studies for the simulation and practical implementation on the two-link and single-link elastic-joint RMs, respectively.

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.

Voltage based sliding mode control of flexible joint robot manipulators in presence of uncertainties

In this paper, a voltage-based sliding mode control (SMC) is presented to control the position of n rigid-link flexible-joint (RLFJ) serial robot manipulator in the presence of structured and unstructured uncertainties. In addition to good performance, the proposed method has three attracting properties: First, it has been developed for a class of RLFJ robot manipulators with a n degree of freedom (DOF). Second, the design process includes all the manipulator dynamical equations, the mechanical and the electrical part of the actuator. Third, the simple structure and low volume of computing make practical implementation possible. Using the idea of the independent joint control strategy, the system dynamic equations are decomposed into n independent subsystems. For each subsystem, three sliding surfaces are defined. Then using these sliding surfaces, control input laws are designed for all subsystems simultaneously. It is proved that the proposed method can guarantee the asymptotic stability of each subsystems and the global asymptotic stability of the closed-loop system in the presence of uncertainties. The results of simulations, as well as experimental results produced using MATLAB/Simulink external mode control on a flexible-joint electrically driven robot manipulator, illustrate high efficiency of the proposed control schemes.

Performance quantification of observer-based robot impact detection under modeling uncertainties

This work proposes a design and performance quantification methodology for observer-based impact detection in serial robot manipulators in presence of modelling errors and without force/torque sensor. After expressing the modelling errors between the physical robot and its inverse dynamic model as the sum of contributions due to dynamic parameters uncertainties and numerical differentiation errors for a given trajectory, an observer of the external disturbance torque is designed based on the inverse dynamic model and using a Kalman filter. The influence of each design parameter of the observer on the quality of the external torque estimation is studied first based on simulation results. Then a frequency analysis is conducted to distinguish between the influence of the exact external torque, the modelling uncertainties and the measurement noise on the estimated external torque. Finally a methodology is proposed to determine the optimal design corresponding to the shortest detection time depending on the expected sensitivity.

A novel self-adaptive modified bat fuzzy sliding mode control of robot manipulator in presence of uncertainties in task space

SUMMARYIn this paper, an optimal fuzzy sliding mode controller has been designed for controlling the end-effector position in the task space. In the proposed control, feedback linearization method, sliding mode control, first-order fuzzy TSK system and optimization algorithm are utilized. In the proposed controller, a novel heuristic algorithm namely self-adaptive modified bat algorithm (SAMBA) is employed. To achieve an optimal performance, the parameters of the proposed controller as well as the input membership functions are optimized by SAMBA simultaneously. In this method, the bounds of structural and non-structural uncertainties are reduced by using feedback linearization method, and to overcome the remaining uncertainties, sliding mode control is employed. Mathematical proof demonstrates that the closed loop system with the proposed control has global asymptotic stability. The presence of sliding mode control gives rise to the adverse phenomenon of chattering in the end-effector position tracking in the task space. Subsequently, to prevent the occurrence of chattering in control input, a first-order TSK fuzzy approximator is utilized. Finally, to determine the fuzzy sliding mode controller coefficients, the optimization algorithm of Self-Adaptive Modified Bat is employed. To investigate the performance of the proposed control, a two-degree-of-freedom manipulator is used as a case study. The simulation results indicate the favorable performance of the proposed method.

PD-SVM Integrated Controller for Robotic Manipulator Tracking Control

Highly precise tracking of a robotic manipulator in presence of uncertainties like noise, disturbances, and friction has been addressed in this particular paper. An integrated proportional derivative and support vector machine (SVMPD) controller has been proposed for manipulator tracking. To illustrate the efficiency of the proposed controller, simulations have been done on a 2-DOF manipulator system. Performance of the proposed controller has been checked and verified with respect to to a simple PID controller and the radial bias neural network proportional integral derivative (RBNNPD) controller. It has been proved that the proposed controller can achieve better tracking performance as compared to other controllers as the range of errors is less and the time taken by the controller has reduced up to 14 times as compared to RBNN.

Adaptive neuro‐predictive control for redundant robot manipulators in presence of static and dynamic obstacles: A Lyapunov‐based approach

SUMMARYThis paper presents a novel approach for online path tracking and obstacle avoidance of redundant robot manipulators. To this end, a nonlinear model predictive control (NMPC) method is designed that can track a desired path or reaches a moving target in the Cartesian space while avoiding static or moving obstacles as well as singular configurations in the workspace of the robot. The finite cost function of the NMPC is optimized at every sampling time, yielding an online optimal approach. In order to avoid collisions with moving obstacles and, at the same time, capturing a moving target, the future positions of the obstacles and the moving target are predicted using artificial neural networks (ANNs). Moreover, ANNs are employed to find a proper nonlinear model for the NMPC. The adaptation laws for the ANNs are obtained using the Lyapunov's direct method. The advantages of the proposed method are fourfold: (i) an adaptive and optimal approach is obtained, which can cope with changes in the system parameters; (ii) no inverse kinematics of the redundant manipulators is required; (iii) no prior knowledge about the obstacles and motion of the moving object is required; and (iv) stability of the closed‐loop system is guaranteed. Numerical simulations, performed on a four degree‐of‐freedom redundant spatial manipulator actuated by DC servomotors, show effectiveness of the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.

Robust fuzzy sliding mode control for tracking the robot manipulator in joint space and in presence of uncertainties

SUMMARYThis paper proposes a simple fuzzy sliding mode control to achieve the best trajectory tracking for the robot manipulator. In the core of the proposed method, by applying the feedback linearization technique, the known dynamics of the robot's manipulator is removed; then, in order to overcome the remaining uncertainties, a classic sliding mode control is designed. Afterward, by applying the TS fuzzy model, the classic sliding mode controller is converted to fuzzy sliding mode controller with very simple rule base. The mathematical analysis shows that the robot manipulator with the new proposed control in tracking the robot manipulator in presence of uncertainties has the globally asymptotic stability. Finally, to show the performance of the proposed method, the controller is simulated on a robot manipulator with two degrees of freedom as case study of the research. Simulation results demonstrate the superiority of the proposed control scheme in presence of the structured and unstructured uncertainties.