A sliding mode control of robot manipulator with variable payload

The paper investigates a modified adaptive super-twisting sliding mode control (ASTSMC) for robotic manipulators with input saturation. To avoid singular perturbation while increasing the convergence rate, a modified sliding mode surface (SMS) is developed in this method. Using the proposed SMS, an ASTSMC is developed for robot manipulators, which not only achieves strong robustness but also ensures finite-time convergence. The boundary of lumped uncertainties cannot be easily obtained. A modified adaptive law is developed such that the boundaries of time-varying disturbance and its derivative are not required. Considering input saturation in practical cases, an ASTSMC with saturation compensation is proposed to reduce the effect of input saturation on tracking performances of robot manipulators. The finite-time convergence of the proposed scheme is analyzed. Through comparative simulations against two other sliding mode control schemes, the proposed method has been validated to possess strong adaptability, effectively adjusting control gains; simultaneously, it demonstrates robustness against disturbances and uncertainties.

This paper intends to design and develop an adaptive fuzzy sliding mode controller (SMC) for robotic manipulator. Since it is not viable to pair the SMC operations with the system model every time, this paper adopts a Fuzzy Inference System (FIS) to replace the system model. It effectively achieves the experimentation in two phases. Accordingly, in the first phase, it attains the accurate features of the system model based on varied samples to characterize the robotic manipulator. In the second stage, it represents the derived fuzzy rules based on adaptive fuzzy membership functions. Moreover, it establishes the self-adaptiveness using Grey Wolf Optimization (GWO) to attain the adaptive fuzzy membership functions. The analysis distinguishes the efficiency of the adopted technique with the optimal investigational scheme and the traditional schemes such as SMC, Fuzzy SMC (FSMC) and GWO-SMC. Moreover, the comparative analysis is also performed by including the noise and validates the effectiveness of the proposed and conventional models. Index Terms : Sliding Mode Control, Robot manipulators, Controller, Noise.

In this paper, radial basis function network (RBFN) with sliding-mode controller (SMC) is designed to the joint position control of two-link robot manipulators for periodic motion and predefined trajectory tracking control. Radial basis function uses curve fitting mode to obtain the nonlinear mapping. The unavoidable learning procedure degrades its transient performance in the existence of disturbance. Sliding-mode control is effective in overcoming uncertainties and has a fast transient response, while the control effort is discontinuous and creates chattering. For this defect, a saturation function is utilized to improve it. The back-propagation (BP) algorithm and Lyapunov stability theorem are used to decide a suitable update law and sliding-mode switch gain, respectively. Thus, the satisfied performance will be obtained, which better than the controller with single RBFN controller, sliding mode controller. The simulated results of a two-link robotic manipulator for the joint frictions, changing link masses and adding external disturbances are provided to show that the effectiveness of the proposed control scheme.

This paper presents a sliding mode controller for trajectory tracking of robotic manipulators with actuator faults. To achieve finite-time convergence of tracking errors and desirable tracking precision, a fast nonsingular terminal sliding mode controller is designed. The uncertainty caused by actuator faults is transformed into system disturbance which is compensated by introducing the dynamic gain technique. As a result, the prior knowledge about upper bound of system disturbance is not required. Moreover, the sign function is replaced by the saturation function in the control law, so the chattering phenomenon is attenuated. Finally, the designed controller is applied to control a two-link robotic manipulator in simulation, and results validate the effectiveness of the control scheme.

This article aims at the problem of trajectory tracking for industrial robotic manipulators with control backlash. An arctangent terminal sliding mode surface is developed to deal with the lumped disturbance and enhance the robustness of the system. A novel adaptive super‐twisting sliding mode control method is developed to achieve fast convergence and continuous control. The chattering in control law is surmounted by using super‐twisting method. The lumped disturbance with unknown upper bound is compensated with the help of the adaptive technique. Based on the Lyapunov stability theory, the sliding mode surface will be arrived in a finite time and the trajectory tracking error will converge to zero in a finite time. The feasibility of the proposed control scheme is validated through an example of a two‐link robotic manipulator.

Ahstract-This paper introduces a sliding mode control (SMC) strategy for robotic manipulators subject to uncertainties, disturbances, and input and output quantization. A continuous SMC method is proposed and a hysteretic quantizer with sector-bounded quantization errors is utilized to quantize the control input signal. Furthermore, a super-twisting algorithm (STA) based observer is adopted to estimate the unmeasured states by using quantized values of output signals. It is demonstrated that under the proposed SMC, the sliding variable can converge to a quasi sliding mode. Finally, a networked two-link robotic manipulator is used to verify the effectiveness of the proposed control method.

ABSTRACTIn this article, we give some comments on the article ‘A new terminal sliding mode control for robotic manipulators’. The article presents a new terminal sliding mode control approach for global finite-time tracking of robotic manipulators. We point out a serious error occurred through the article, leading to the ineffectiveness of the proposed approach. A correction is proposed. Comparisons are presented.

In this paper, the time-delayed control (TDC) scheme with a sliding mode controller for a robot manipulator is presented. Since TDC suffers from the selection of an inertia matrix of a robot manipulator, the effect of the constant matrix on the performance is investigated. TDC is designed with the sliding mode controller (SMC) to improve the control performance. For the performance comparison between several control schemes, PD control, SMC, TDC, and TDC-SMC are presented. The performances are compared by simulating the same trajectory tracking task of a three link robot manipulator u nder different control schemes.

RBFNN-based nonsingular fast terminal sliding mode control for robotic manipulators including actuator dynamics

Adaptive second order terminal sliding mode controller for robotic manipulators

In dealing with the trajectory tracking control of robotic manipulator many scholars have had to work in a sliding mode controller. However, SMC has two major limitations one is chattering the other is asymptotical convergence. The chattering problem occurs in traditional SMC due to the switching function of the discontinuous controller and the constant gain parameter of K. Though in this study a Fuzzy Gain Scheduling Terminal Sliding Mode (FGSTSM) controller with a hyperbolic tangent function instead of signum function, is proposed and applied for tracking control of the UR5 robot manipulator. Moreover, the mathematical model of the UR5 robot manipulator using the Newton-Euler algorithm was developed on Maple software of version Maple 18.2. Hence the trajectory tracking control of the UR5 robot manipulator simulation was conducted on MATLAB/SIMULINK of version Matlab R2016a. Then for simulation purposes, a curve (arc) was taken as the desired trajectory for the robot manipulator to track and finally the result is compared with the conventional SMC controller.

Robot manipulators as an indispensable part of automatic operation are becoming increasingly important in intelligent manufacturing systems, such as grinding and assembly. Although control methods of robot manipulators have been extensively studied, high-precision robots still face new challenges from uncertainties caused by changes in the environment and enhancement of interference. As a consequence, the neural network-based observer is utilized to reduce the influence of uncertainties and external disturbances. In this work, a new kind of nonlinear disturbance observer is designed which could well function with observed states. To improve the control efficiency and response characteristic, a kind of new integral sliding manifold is devised and the control input is generated via backstepping techniques. The stability is proved with Lyapunov theory, and the experimental results are given to demonstrate the effectiveness of the proposed controller.

This paper develops a novel non-singular terminal sliding mode control scheme for trajectory tracking of robot manipulators in the presence of external disturbances and uncertainties. Firstly, with the introduction of two nonlinear terms, a newly terminal sliding surface is designed. Then utilizing this sliding surface, a novel non-singular terminal sliding mode controller is proposed to eliminate the reaching interval, singularity issue and subsequently with this controller, the finite time error convergence is also assured. In the proposed controller, radial basis function neural network is employed to approximate highly uncertain nonlinear dynamics of robot manipulators using update laws derived with Lyapunov approach. Meanwhile, the effects of approximation errors are attenuated with $$H_\infty $$ performance criterion by introducing a robust term into the controller. As a result of proposed approach, asymptotic convergence of tracking errors is achieved within finite time and the approximation errors are attenuated to desired levels. The numerical simulation result shows the effectiveness of proposed controller for the case of microbot type robot manipulator.

A finite time synergetic control scheme for robot manipulators

This paper presents an adaptive trajectory tracking neural network control using radial basis function (RBF) for an n-link robot manipulator with robust compensator to achieve the high-precision position tracking. One of the difficulties in designing a suitable control scheme which can achieve accurate trajectory tracking and good control performance is to guarantee the stability and robustness of control system, due to friction forces, external disturbances error, and parameter variations. To deal with this problem, the RBF network is investigated to the joint position control of an n-link robot manipulator. The RBF network is one approach which has shown a great promise in this sort of problems because of its fast learning algorithm and better approximation capabilities. The adaptive RBF network can effectively improve the control performance against large uncertainty of the system. The adaptive turning laws of network parameters are derived using the back-propagation algorithm and the Lyapunov stability theorem, so that the stability of the entire system and the convergence of the weight adaptation are guaranteed. In this control scheme, a robust compensator plays as an auxiliary controller to guarantee the stability and robustness under various environments such as the mass variation, the external disturbances, and modeling uncertainties. Finally, the simulation and experimental results in comparison with adaptive fuzzy and wavelet network control method are provided to verify the effectiveness of the proposed control methodology.