Tracking Control Of Robot Manipulators Research Articles

Disturbance observer-based fixed-time prescribed performance tracking control for robotic manipulator

ABSTRACTThis paper investigates the fixed-time prescribed performance tracking control for the n-DOF uncertain manipulator. First, a novel Barrier Lyapunov Function (BLF) is proposed to guarantee the prescribed performance for the manipulator tracking error. Then, we introduce a disturbance observer to estimate the system uncertainty and disturbance accurately in a predefined time. Next, a composite controller based on the nonsingular fast integral terminal sliding mode is constructed. It is strictly proved that the closed-loop system is stable in fixed-time, which is independent of the initial conditions. Moreover, both transient and steady-state performances of the outputs can be preserved. Finally, numerical simulations and experimental studies are presented to demonstrate the effectiveness of the proposed methods.

Robust tracking control for Robotic Manipulators based on Super-twisting Algorithm

Robotic manipulators are broadly used in industries for different kinds of specialized operational responsibilities and it is very complicated to design of a robust and stable controller for these systems. The performance of industrial robotic manipulators has augmented concerning stability and safety due to growths in robust controller design. However, designing a stable and reliable control method for a robot manipulator is important for real-world applications. This paper proposed a robust composite super-twisting sliding mode controller (STSMC) for robotic manipulators with uncertainties. To improve the robustness of robotic manipulators and reduce the chattering problem of sliding mode control, the Super-twisting algorithm is employed to design a continuous controller, and the stability is proved. Simulations on the 2-DOF robotic manipulator are shown to illustrate the effectiveness of the proposed method in which demonstrated that the proposed model has smoothness at 10−5.

Computational Efficiency-Based Adaptive Tracking Control for Robotic Manipulators with Unknown Input Bouc-Wen Hysteresis.

In order to maintain robotic manipulators at a high level of performance, their controllers should be able to address nonlinearities in the closed-loop system, such as input nonlinearities. Meanwhile, computational efficiency is also required for real-time implementation. In this paper, an unknown input Bouc–Wen hysteresis control problem is investigated for robotic manipulators using adaptive control and a dynamical gain-based approach. The dynamics of hysteresis are modeled as an additional control unit in the closed-loop system and are integrated with the robotic manipulators. Two adaptive parameters are developed for improving the computational efficiency of the proposed control scheme, based on which the outputs of robotic manipulators are driven to track desired trajectories. Lyapunov theory is adopted to prove the effectiveness of the proposed method. Moreover, the tracking error is improved from ultimately bounded to asymptotic tracking compared to most of the existing results. This is of important significance to improve the control quality of robotic manipulators with unknown input Bouc–Wen hysteresis. Numerical examples including fixed-point and trajectory controls are provided to show the validity of our method.

Efficient PID Tracking Control of Robotic Manipulators Driven by Compliant Actuators

This brief aims to show that a linear proportional–integral–derivative (PID) controller is theoretically valid for tracking control of robotic manipulators driven by compliant actuators. The control problem is formulated into a three-time-scale singular perturbation formula, including a slow time scale at the rigid robot dynamics, one actual fast time scale at the actuator dynamics, and another virtual fast time scale at the controller dynamics. A PID-type controller is derived to guarantee semiglobal practical exponential stability of the rigid robot dynamics, and a derivative-type controller is applied to establish global exponential stability of the actuator dynamics. Based on a state transformation to the closed-loop rigid robot dynamics and the extended Tikhonov’s theorem, it is proven that the entire system has semiglobal practical exponential stability under a proper choice of control parameters. The proposed controller is not only structurally simple and model-free resulting in low implementation cost, but also robust against external disturbances and parameter variations. The current design is only valid while the spring stiffness is relatively large compared with other parameters of the robot dynamics. Experimental results based on a single-link compliant robotic manipulator have verified effectiveness of the proposed approach.

Delta Parallel Robotic Manipulator Tracking Control using Fractional Order Controllers

Tracking control of robotic manipulators presents many challenges on the controller design due to the system nonlinear behavior. This paper presents the design of a tracking control for a parallel robotic manipulator using a fractional order PID controller with the feedback linearization technique. The kinematic and dynamic models of the manipulator are obtained, and a parametric identification of the dynamic model is performed using the recursive least squares algorithms. A MSC-ADAMS/MATLAB co-simulation model of the manipulator is built to perform the identification and control tasks. The inverse dynamics method gives a linearized and uncoupled model of the robotic system, which is employed to design the fractional order PID controller. The fractional order PID controller is contrasted with an integer order PID controller on performing tracking tasks. A quantitative performance analysis of the controllers is made calculating the performance indices for different tracking tasks. Obtained results show that the fractional order PID controller has a better performance on tracking tasks than the integer order PID controller.

Adaptive sliding mode disturbance rejection control with prescribed performance for robotic manipulators

This study proposes an adaptive sliding mode disturbance rejection control with prescribed performance for robotic manipulators. A transformation with respect to tracking error using certain performance functions is used to ensure the transient and steady-state performances of the trajectory tracking control for robotic manipulators. Using the transformed error, a nonsingular terminal sliding mode surface is proposed. A continuous terminal sliding mode control (SMC) is presented to stabilize the system. To compensate for the uncertainty and external disturbance, a novel sliding mode disturbance observer is proposed. Considering the unknown boundary of the derivative of a lumped disturbance, an adaptive law based on the idea of equivalent control is designed. Combining the adaptive law, continuous nonsingular terminal SMC, and sliding mode disturbance observer, the adaptive sliding mode disturbance rejection control with prescribed performance is developed. Simulations are carried out to demonstrate the effectiveness of the proposed approach.

Exponential Tracking Control of Robotic Manipulators With Uncertain Dynamics and Kinematics

This paper addresses a long-standing yet well documented open problem on task-space trajectory tracking control of robotic manipulators subject to both uncertain dynamics and uncertain kinematics. The main contribution is to establish a theoretical framework for designing an observer-based controller to achieve exponential tracking control. Two observers are designed for precisely estimating the uncertain kinematics and dynamics. It is theoretically proved that the entire observer–controller system is proved to be globally exponentially stable. Both the estimation errors and the trajectory tracking error can globally exponentially converge to their stable equilibrium points, respectively. To the best knowledge of the author, this works may be the first result for robot exponential tracking control. The tracking performance is, therefore, more robust to system uncertainties. The settling time of the closed-loop tracking error system can be tuned to be small arbitrarily. Experimental tests are also conducted to validate the effectiveness of the designed control framework.

Experimental Evaluation of a Sectorial Fuzzy Controller Plus Adaptive Neural Network Compensation Applied to a 2-DOF Robot Manipulator

In this paper we propose a novel control architecture that employs an adaptive neural network (NN) for feedforward compensation and a sectorial fuzzy controller in the feedback loop applied to the trajectory tracking control of robot manipulators. Both simulation and experimental results are presented in comparison with the original classic Proportional-Derivative (PD) plus feedforward controller, from which this new proposal is based, and two preliminary versions of the application of feedforward sectorial fuzzy control and feedforward adaptive neural nonlinear PD control to the trajectory tracking control of a two-degree of freedom (2-DOF) robot manipulator. The proposed controller has, in general, better performance than its counterparts in terms of transient response and steady-state error while it maintains one of the main characteristics of fuzzy controllers: Its tolerance to parameter deviation.

A New Model-Free Trajectory Tracking Control for Robot Manipulators

In this paper, we propose a novel model-free trajectory tracking control for robot manipulators under complex disturbances. The proposed method utilizes time delay control (TDC) as its control framework to ensure a model-free scheme and uses adaptive nonsingular terminal sliding mode (ANTSM) to obtain high control accuracy and fast dynamic response under lumped disturbance. Thanks to the application of adaptive law, the proposed method can ensure high tracking accuracy and effective suppression of noise effect simultaneously. Stability of the closed-loop control system is proved using Lyapunov method. Finally, the effectiveness and advantages of the newly proposed TDC scheme with ANTSM dynamics are verified through several comparative simulations.

Fractional PD-IλDμ Error Manifolds for Robust Tracking Control of Robotic Manipulators

Linear proportional-integral-derivative (PID) controller stands for the most widespread technique in industrial applications due to its simple structure and easy tuning rules. Recently, considering fractional orders λ and μ, there has been studied the fractional-order PIλDμ (FPID) controller to provide salient advantages in comparison to the conventional integer-order PID, such as, a more flexible structure and a preciser performance. In addition, proportional and derivative (PD) and PID error manifolds have been classically proposed; however, there remains the question on how FPID-like error manifolds perform for the control of nonlinear plants, such as robots. In this paper, this problem is addressed by proposing a PD-IλDμ error manifold for novel vector saturated control. The stability analysis shows convergence into a small vicinity of the origin, wherein, such hybrid combination of integer- and fractional-order error manifolds provides further insights into the closed-loop response of the nonlinear plant. Simulations studies are carried out to illustrate the feasibility of the proposed scheme.

Adaptive Neural Tracking Control of Robotic Manipulators with Guaranteed NN Weight Convergence

Although adaptive control for robotic manipulators has been widely studied, most of them require the acceleration signals of the joints, which are usually difficult to measure directly. Although neural networks (NNs) have been used to approximate the unknown nonlinear dynamics in the robotic systems, the conventional adaptive laws for updating the NN weights cannot guarantee that the obtained NN weights converge to their ideal values, which could degrade the tracking control response. To address these two issues, a new adaptive algorithm with the extracted NN weights error is incorporated into adaptive control, where a novel leakage term is superimposed on the gradient method. By using the Lyapunov approach, the convergence of both the tracking error and the estimation error can be guaranteed simultaneously. In addition, two auxiliary functions are introduced to reformulate the robotic model for designing the adaptive law, and a filter operation is used to avoid measuring the acceleration signals. Comparisons to other well-recognized adaptive laws are given, and extensive simulations based on a 2-DOF SCARA robotic system are given to verify the effectiveness of the proposed control strategy.

Adaptive Neural Network Tracking Control for Robotic Manipulators With Dead Zone.

In this paper, the adaptive neural network (NN) tracking control problem is addressed for robot manipulators subject to dead-zone input. The control objective is to design an adaptive NN controller to guarantee the stability of the systems and obtain good performance. Different from the existing results, which used NN to approximate the nonlinearities directly, NNs are employed to identify the originally designed virtual control signals with unknown nonlinear items in this paper. Moreover, a sequence of virtual control signals and real controller are designed. The adaptive backstepping control method and Lyapunov stability theory are used to prove the proposed controller can ensure all the signals in the systems are semiglobally uniformly ultimately bounded, and the output of the systems can track the reference signal closely. Finally, the proposed adaptive control strategy is applied to the Puma 560 robot manipulator to demonstrate its effectiveness.

Adaptive fuzzy tracking control of robot manipulators actuated by permanent magnet synchronous motors

This paper presents a model-free controller for robot manipulators driven by permanent magnet synchronous motors (PMSM). The contribution of this paper is merging the electrical and mechanical equations of the robotic system to simplify the controller design procedure. In other words, due to the fact that the manipulator links are the load torques of the PMSMs, the uncertainties related to the manipulator dynamics have been transferred to the voltage equations of the PMSMs. Uncertainties including external disturbances and system dynamics are estimated and compensated in the control law using adaptive fuzzy systems. In contrast to most previous related works that ignore the PMSM electrical equations, both the electrical and mechanical equations are taken into consideration. Based on the Barbalat's lemma, the asymptotic convergence of the tracking error to zero is guaranteed. The case study is an articulated manipulator driven by PMSMs. In comparison with a robust controller for a single-link manipulator actuated by PMSM, the proposed method is superior due to faster and smoother responses.

On Operational Space Tracking Control of Robotic Manipulators With Uncertain Dynamic and Kinematic Terms

In this study, a continuous robust-adaptive operational space controller that ensures asymptotic end-effector tracking, despite the uncertainties in robot dynamics and on the velocity level kinematics of the robot, is proposed. Specifically, a smooth robust controller is applied to compensate the parametric uncertainties related to the robot dynamics while an adaptive update algorithm is used to deal with the kinematic uncertainties. Rather than formulating the tracking problem in the joint space, as most of the previous works on the field have done, the controller formulation is presented in the operational space of the robot where the actual task is performed. Additionally, the robust part of the proposed controller is continuous ensuring the asymptotic tracking and relatively smooth controller effort. The stability of the overall system and boundedness of the closed loop signals are ensured via Lyapunov based arguments. Experimental results are presented to illustrate the feasibility and performance of the proposed method.

Tracking Control of Robot Manipulators with Unknown Models: A Jacobian-Matrix-Adaption Method

Tracking control of robot manipulators is a fundamental and significant problem in robotic industry. As a conventional solution, the Jacobian-matrix-pseudo-inverse (JMPI) method suffers from two major limitations: one is the requirement on known information of the robot model such as parameter and structure; the other is the position error accumulation phenomenon caused by the open-loop nature. To overcome such two limitations, this paper proposes a novel Jacobian-matrix-adaption (JMA) method for the tracking control of robot manipulators via the zeroing dynamics. Unlike existing works requiring the information of the known robot model, the proposed JMA method uses only the input-output information to control the robot with unknown model. The solution based on the JMA method transforms the internal, implicit, and unmeasurable model information to the external, explicit, and measurable input-output information. Moreover, simulation studies including comparisons and tests substantiate the efficacy and superiority of the proposed JMA method for the tracking control of robot manipulators subject to unknown models.

Tracking Control for Robotic Manipulators using Fractional Order Controllers with Computed Torque Control

This paper presents the tracking tasks control for a robotic manipulator employing a fractional order PID controller with the computed torque control strategy. The proposed control strategy is contrasted with an integer order PID controller with the computed torque control strategy. The robotic system is a two degree of freedom manipulator simulated using a MSCADAMS/MATLAB cosimulation model. The manipulator dynamic model is identified employing the recursive least squares algorithm. The fractional order PID controller is tuned using optimization techniques. Controllers robustness is evaluated against the presence of external disturbances in the joints torques, random noise in the feedback loop and payload variations. Obtained results show that the fractional order PID controller with the computed torque control strategy has better performance and robust stability against the presence of the analyzed external disturbances for tracking tasks.

On Global Trajectory Tracking Control of Robot Manipulators with Flexible Joints

Abstract In this paper a novel approach to the global trajectory tracking control problem for manipulators with flexible joints is presented. The dynamic model of such mechanical systems has a cascade structure that allows to decouple the links dynamics from the actuators ones. We propose a design procedure for nonlinear controller with computed feedforward of robot manipulators with revolute joints in a cylindrical phase space. Stability proof of the closed-loop system is given by constructing a Lyapunov function which is periodic on angular coordinates. We illustrate the implementation of the controller using simulation example.

Trajectory tracking control of robot manipulator based on RBF neural network and fuzzy sliding mode

Aimed at the nonlinearity and uncertainty of the manipulator system, a RBF (radial basis function) neural network-based fuzzy sliding-mode control method was proposed in this paper, in order to make the manipulator track the given trajectory at an ideal dynamic quality. In this method, the equivalent part of the sliding-mode control is approximated by the RBF neural network, in which no model information is required. Meanwhile, a fuzzy controller is developed to make adaptive adjustment of the sliding-mode control’s switching gains according to the distance between the current motor point and the sliding-mode surface, thus effectively the problem of chattering is solved. This method has, to some extent, improved the performance of response and tracking, and reduced the time of adjustment and chattering of input control. The system stability is verified by Lyapunov’s theorem. The simulation result suggests that the algorithm designed for the three-degree-of-freedom (3DOF) manipulator system is effective.

Comments on “Tracking Control of Robotic Manipulators With Uncertain Kinematics and Dynamics”

This letter considers the paper entitled “Tracking Control of Robotic Manipulators With Uncertain Kinematics and Dynamics” by B. Xiao, S. Yin, and O. Kaynak [ IEEE Trans. Ind. Electron. , vol. 63, no. 10, pp. 6439–6449, Oct. 2016], where the authors meant to provide an effective control for finite-time tracking of robot manipulators with both kinematic and dynamic uncertainties. This letter points out several flaws leading to the ineffectiveness of the main result in the paper. A correction is proposed.

Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence

In this paper, the global adaptive neural control with finite-time (FT) convergence learning performance for a general class of nonlinear robot manipulators has been investigated. The scheme proposed in this paper offers a subtle blend of neural controller with robust controller, which palliates the limitation of neural approximation region to ensure globally uniformly ultimately bounded (GUUB) stability by integrating a switching mechanism. Morever, the proposed scheme guarantees the estimated neural weights converging to optimal values in finite time by embedding an adaptive learning algorithm driven by the estimated weights error. The optimal weights obtained through the learning process of the neural networks (NNs) will be reused next time for repeated tasks, and can thus reduce computational load, improve transient performance and enhance robustness. The simulation studies have been carried out to demonstrate the superior performance of the controller in comparison to the conventional methods.