Mode Control Of Robot Manipulators Research Articles

Disturbance observer based adaptive predefined‐time sliding mode control for robot manipulators with uncertainties and disturbances

AbstractThis article develops a predefined‐time sliding mode control approach for systems with external disturbances and uncertainties through a nonlinear disturbance observer (DO). For addressing predefined‐time stabilization problem of robotic manipulator system, a predefined‐time sliding mode surface is proposed, ensuring system states converge to origin within a predefined‐time once sliding mode surface is attained. Compared to conventional fixed‐time and finite‐time control strategies, a distinctive advantage of this scheme is that system settling time can be explicitly chosen in advance and independent of system states. To achieve predefined‐time performance, a disturbance observer is introduced to generate the disturbance estimate, which can be incorporated into controller to counteract disturbance. To address the systems uncertainty, an adaptive law is employed to estimate the unknown upper boundary of system uncertainties. Finally, the effectiveness and performance of the proposed scheme are illustrated by simulation and experiment.

Fractional‐order fixed‐time sliding mode control of robotic manipulators with robust exact differentiator

AbstractThis paper introduces a robust position tracking control structure of exoskeleton robots (ERs). An open environment and body flexibility are the main challenges in ER control system design. First, a fractional‐order nonsingular terminal sliding mode (FONTSM) controller is proposed for a robotic manipulator with uncertainty, where lumped unknown dynamics are computed by time‐delay estimation (TDE). Secondly, a robust exact differentiator (RED) is employed as the output feedback observer for the velocity sensorless robotic system. Thirdly, the stability of the FONTSM control system is demonstrated. It is proved that the system error can move to the specified value along the sliding mode surface at a fixed time and can reach the sliding mode surface after a finite time. Finally, simulations on a 2‐DOF robotic manipulator show the effectiveness of the fixed‐time control strategies. When a combined trigonometric function disturbance is applied, the joint angle error is reduced to 1% by the model‐free controller based on output feedback within approximately 1 s.

Adaptive fast terminal sliding mode control of robotic manipulators based on joint torque estimation and friction compensation

Adaptive fast terminal sliding mode control of robotic manipulators based on joint torque estimation and friction compensation

A novel hybrid observer‐based model‐free adaptive high‐order terminal sliding mode control for robot manipulators with prescribed performance

A novel hybrid observer‐based model‐free adaptive high‐order terminal sliding mode control for robot manipulators with prescribed performance

Adaptive finite-time sliding mode control of robot manipulators with possible actuator failures

Adaptive finite-time sliding mode control of robot manipulators with possible actuator failures

Adaptive Super-Twisting Sliding Mode Control for Robot Manipulators with Input Saturation.

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.

Adaptive sliding mode control of robotic manipulator based on reinforcement learning

AbstractRobotic manipulators usually exhibit time‐varying, nonlinear, and coupled dynamics due to the parameter perturbations, disturbances, and other uncertainties. Traditional control algorithms typically do not possess parameters' self‐adaptive learning ability, limiting the tracking performance of the robot. In order to address these issues, an adaptive sliding mode control method based on reinforcement learning (ASMRL) is proposed in this paper, where a proportional–integral sliding mode (PISM) controller is used to address the nonlinear problem in the system, and deep deterministic policy gradient (DDPG) agent is adopted to conduct the parameters' learning of the PISM controller using its adaptive characteristics and the autonomous learning ability. The simulation results illustrate that the proposed method can effectively achieve better tracking performance compared with two other control methods, demonstrating the effectiveness of the proposed approach.

A Novel Disturbance Observer Based Fixed-Time Sliding Mode Control for Robotic Manipulators with Global Fast Convergence

A Novel Disturbance Observer Based Fixed-Time Sliding Mode Control for Robotic Manipulators with Global Fast Convergence

Robust sliding mode control for robot manipulators with analysis on trade‐off between reaching time and L∞ gain

This paper provides a new sliding mode control (SMC) approach, by which both the nominal and robust stability associated with a trajectory tracking problem for an uncertain robot manipulator are achieved. More precisely, the new control law consists of linear and nonlinear functions of tracking errors, in which the former is for the nominal stability and the latter is to ensure the robust stability of the resulting closed‐loop systems. The nonlinear function can be interpreted as an extended version of conventional SMC approach, and the reaching phase corresponding to a pregiven sliding surface is shown to be completed in a finite time; the tracking errors arrive at the sliding surface in a finite time and do not deviate from it after the arrival. In the sliding phase, the tracking errors are also ensured to converge to the origin. Finally, some simulation results are given to demonstrate both the theoretical validity and practical effectiveness of the proposed control approach.

Adaptive Dynamic Boundary Sliding Mode Control for Robotic Manipulators under Varying Disturbances

This paper introduces an Adaptive Dynamic Bounded Sliding Mode Control (ADBSMC) method that incorporates a disturbance observer to enhance the response characteristics of the robot manipulator while eliminating the reliance on a priori knowledge. The proposed method utilizes nonlinear sliding mode manifolds and fast-terminal-type convergence laws to address errors and parameter uncertainties inherent in the nonlinear system models. The adaptive law is designed to cover all boundary conditions based on the model’s state. It can dynamically determine upper and lower bounds without requiring prior knowledge. Consequently, the ADBSMC control method amalgamates the benefits of adaptive law and fast terminal sliding mode, leading to significant enhancements in control performance compared with traditional sliding mode control (SMC), exhibiting robustness against uncertain disturbances. To mitigate external disturbances, a system-adapted disturbance observer is devised, facilitating real-time monitoring and compensation for system disturbances. The stability of ADBSMC is demonstrated through the Lyapunov method. Simulation and experimental results validate the effectiveness and superiority of the ADBSMC control scheme, showcasing its potential for practical applications.

Adaptive radial basis function neural network sliding mode control of robot manipulator based on improved genetic algorithm

ABSTRACT Since the trajectory-tracking control performance of multi-joint robot manipulator may be degraded due to modeling errors and external disturbances, this paper designs a new adaptive robot manipulator trajectory tracking control method through improved genetic algorithm and radial basis function neural network sliding mode control (IGA-RBFNNSMC). Firstly, the genetic algorithm (GA) is improved by establishing superior populations centered on individuals with high fitness values and selecting individuals in the superior populations for crossover and variation. Secondly, the improved genetic algorithm (IGA) is used for the optimization of the center vector and width vector of the Gaussian basis function in radial basis function (RBF) neural network. Then, based on the dynamics model of the robot manipulator, the modeling errors are approximated by RBF neural network and eliminated by sliding mode control (SMC), and the Lyapunov theorem is used to prove the stability and convergence of the control system. Finally, a two-joint robot manipulator is taken as the research objective and the simulation results show that IGA can significantly reduce the solution time on the basis of guaranteed accuracy and IGA-RBFNNSMC can make the trajectory tracking control accurate and more efficient, which proves the effectiveness of the proposed control method.

Adaptive Proportional Integral Derivative Nonsingular Dual Terminal Sliding Mode Control for Robotic Manipulators

This article aims to develop a new Adaptive Proportional Integral Derivative (PID) Nonsingular Dual Terminal Sliding Mode Control, designed for tracking the position of robot manipulators under disturbances and uncertainties. Compared with existing PID Nonsingular Fast Terminal Sliding Mode (PIDNFTSM) controllers, this work effectively avoids singularity problems in control while significantly enhancing the convergence speed of errors. An adaptive reaching law is proposed to estimate the bound information of the first derivative of lumped disturbance by regulating itself based on sliding variables. The overall system stability is proven by using the Lyapunov approach. Subsequent simulation results verify the effectiveness of the proposed controller regarding tracking error reduction, energy efficiency enhancements, and singularity avoidance.

Adaptive Fast-Terminal Neuro-Sliding Mode Control for Robot Manipulators with Unknown Dynamics and Disturbances

This paper presents a novel adaptive fast-terminal neuro-sliding mode control (AFTN-SMC) for a two-link robot manipulator with unknown dynamics and external disturbances. The proposed controller is chattering-free and adaptive to the time-varying system uncertainties. Furthermore, the radial base function neural network (RBFNN) is employed to approximate the unknown state dynamics. The simulations have been completed in MATLAB, which illustrates the successful implementation of the proposed controller. The results showcased the effectiveness of the AFTN-SMC in achieving accurate tracking and stability, even in the presence of uncertainties and parameter variations. The incorporation of the RBFNN in the controller proved to be a valuable tool for approximating the unknown dynamics, enabling accurate estimation and control of the manipulator’s behavior. The research presented in this paper contributes to the advancement in control techniques for robot manipulators in diverse industrial and automation applications.

Design and implementation of an adaptive neural network observer–based backstepping sliding mode controller for robot manipulators

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.

FeedForward Super-Twisting Sliding Mode Control for Robotic Manipulators: Application to PKMs

This article deals with the development and implementation of a novel feedforward super-twisting sliding mode controller for robotic manipulators. A full stability analysis based on a Lyapunov candidate is established showing a local asymptotic finite-time convergence of the proposed controller in the presence of upper bounded disturbances. Its robustness toward parametric uncertainties and system disturbances, thanks to the super-twisting approach, is pointed out. In addition, the feedforward dynamic term of the proposed controller that can compensate for the model nonlinearities is not sensitive to measurement noise. Real-time experiments have been conducted on two parallel manipulators: a 5-DOF SPIDER4 PKM and a 3-DOF Delta PKM. The effectiveness of the proposed controller is validated in different scenarios, including the nominal case and robustness toward parametric variations (payload) and speed changes

Adaptive sliding mode control for robotic manipulators with backlash

This paper provides the consequences for trajectory tracking of uncertain n-DOF robotic manipulators in the presence of external disturbances and backlash. The control strategy consists of decoupling system based on inverse dynamics, sliding mode controller, and optimal controller. The tendency of robotic manipulators with gear backlash is to function in two unique phases: contact phase or backlash phase. The sliding mode controller works in the contact phase, after the decoupling of the manipulator, a adaptive second-order sliding mode controller is designed to guarantee system achieve desirable tracking precision and robust stability. The optimal controller works in the backlash phase, which makes the motor quickly traverse the backlash gap and re-contact the joints without collision. Finally, the proposal is verified in simulation using a model of a PUMA560 robot manipulator identified from real data.

A Novel Adaptive Sliding Mode Control of Robot Manipulator Based on RBF Neural Network and Exponential Convergence Observer

A Novel Adaptive Sliding Mode Control of Robot Manipulator Based on RBF Neural Network and Exponential Convergence Observer

Adaptive nonsingular terminal sliding mode control of robot manipulator based on contour error compensation

To achieve accurate contour tracking of robotic manipulators with system uncertainties, external disturbance and actuator faults, a cross-coupling contour adaptive nonsingular terminal sliding mode control (CCCANTSMC) is proposed. A nonsingular terminal sliding mode manifold is developed which eliminates the singularity completely. In order to avoid the demand of the prior knowledge of system uncertainties, external disturbance and actuator faults in practical applications, an adaptive tuning approach is proposed. The stability of the proposed control strategy is demonstrated by the finite-time stability theory. Then, the developed controller combines adaptive nonlinear terminal sliding mode control (ANTSMC) of joint trajectory tracking and proportion–differentiation control of end-effector contour tracking by introducing the coupling factor between multiple axes based on Jacobian. Moreover, a unified framework of cross-coupling contour compensation and reference position pre-compensation is built. Finally, numerical simulation and experimental results validate the effectiveness of the proposed control strategy.

Design of Adaptive Fractional-Order Fixed-Time Sliding Mode Control for Robotic Manipulators.

In this investigation, the adaptive fractional-order non-singular fixed-time terminal sliding mode (AFoFxNTSM) control for the uncertain dynamics of robotic manipulators with external disturbances is introduced. The idea of fractional-order non-singular fixed-time terminal sliding mode (FoFxNTSM) control is presented as the initial step. This approach, which combines the benefits of a fractional-order parameter with the advantages of NTSM, gives rapid fixed-time convergence, non-singularity, and chatter-free control inputs. After that, an adaptive control strategy is merged with the FoFxNTSM, and the resulting model is given the label AFoFxNTSM. This is done in order to account for the unknown dynamics of the system, which are caused by uncertainties and bounded external disturbances. The Lyapunov analysis reveals how stable the closed-loop system is over a fixed time. The pertinent simulation results are offered here for the purposes of evaluating and illustrating the performance of the suggested scheme applied on a PUMA 560 robot.

Adaptive Fault Tolerant Non-Singular Sliding Mode Control for Robotic Manipulators Based on Fixed-Time Control Law

This paper presents a fault tolerant scheme employing adaptive non-singular fixed-time terminal sliding mode control (AFxNTSM) for the application of robotic manipulators under uncertainties, external disturbances, and actuator faults. To begin, non-singular fixed-time terminal sliding mode control (FxNTSM) is put forth. This control method uses non-singular terminal sliding mode control to quickly reach fixed-time convergence, accomplish satisfactory performance in tracking, and produce non-singular and non-chatter control inputs. Then, without knowing the upper bounds beforehand, AFxNTSM is used as a reliable fault tolerant control (FTC) to estimate actuator faults and unknown dynamics. The fixed-time stability of the closed-loop system is established by the theory of Lyapunov analysis. The computer simulation results of the position tracking, control inputs, and adaptive parameters are presented to verify and illustrate the performance of the proposed strategy.