Tracking Control Of Robot Manipulators Research Articles

Linguistic Lyapunov reinforcement learning control for robotic manipulators

We propose a Lyapunov theory based linguistic reinforcement learning (RL) framework for stable tracking control of robotic manipulators. In particular, we employ Lyapunov theory to constrain fuzzy rule consequents for ensuring stability of the designed controller. Proposed fuzzy RL controller employs Lyapunov theory dictated rules for discovering an optimal yet stable control strategy for robotic manipulators. Furthermore, our proposed linguistic RL controller handles payload variations and external disturbances quite effectively. We validate linguistic Lyapunov RL controller on two benchmark control problems: (i) a standard two-link robotic arm manipulator, and (ii) a two link selective compliance assembly robotic arm (SCARA). Simulation results and comparison against (a) baseline fuzzy Q learning (FQL) controller, and (b) a recently proposed Lyapunov theory based Markov game controller showcases our controller's superior tracking performance and lower computational complexity. Furthermore, our controller exhibits high stability with disturbances and payload variations.

Control of Robot Manipulators Using Time-Delay Estimation and Fuzzy Logic Systems

A highly accurate model-free controller is proposed for trajectory tracking control of robot manipulators. The proposed controller incorporates time-delay estimation (TDE) to estimate and cancel continuous nonlinearities of robot dynamics, and exploits fuzzy logic systems to suppress the effect of the TDE error, which is due to discontinuous nonlinearities such as friction. To this end, integral sliding mode is defined using desired error dynamics, and a Mamdani-type fuzzy inference system is constructed. As a result, the proposed controller achieves the desired error dynamics well. Implementation of the proposed controller is easy because the design of the controller is intuitive and straightforward, and calculations of the complex robot dynamics are not required. The tracking performance of the proposed controller is verified experimentally using a 3-degree of freedom PUMAtype robot manipulator.

Adaptive Voltage-based Control of Direct-drive Robots Driven by Permanent Magnet Synchronous Motors

Tracking control of the direct-drive robot manipulators in high-speed is a challenging problem. The Coriolis and centrifugal torques become dominant in the high-speed motion control. The dynamical model of the robotic system including the robot manipulator and actuators is highly nonlinear, heavily coupled, uncertain and computationally extensive in non-companion form. In order to overcome these problems, this paper presents a novel adaptive control for direct-drive robot manipulators driven by Permanent Magnet Synchronous Motors (PMSM) in tracking applications. The novelty of this paper is that the proposed adaptive law is free from manipulator dynamics by using the Voltage Control Strategy (VCS). Additionally, a state space model of the robotic system driven by PMSM is presented. The VCS differs from the commonly used control strategy for robot manipulators the so called torque control strategy. The position control of the PMSM is effectively used for the tracking control of the robot manipulator. This idea takes the control problem from the manipulator control to the motor control resulting in a simple yet efficient control design. Compared with the torque control, the control design is simpler, easier to implement with better tracking performance. The control method is verified by stability analysis. Simulation results show superiority of the proposed control to the torque control applied by field oriented control on the direct-drive robot driven by PMSM.

Task Space Trajectory Tracking Control of Robot Manipulators with Uncertain Kinematics and Dynamics

To improve the tracking precision of robot manipulators’ end-effector with uncertain kinematics and dynamics in the task space, a new control method is proposed. The controller is based on time delay estimation and combines with the nonsingular terminal sliding mode (NTSM) and adaptive fuzzy logic control scheme. Kinematic parameters are not exactly required with the consideration of kinematic uncertainties in the controller. No dynamic models or numerous parameters of the robot manipulator system are required with the use of TDE. Thus, the controller is simple structure and suitable for practical applications. Furthermore, errors caused by time delay estimation are compensated by the adaptive fuzzy nonsingular terminal sliding mode scheme. The simulation is performed on a 2-DOF robot manipulator with three cases in the task space. The results show that the proposed controller provides faster convergence rate and higher tracking precision than TDE based NTSM and improved TDE based NTSM controller.

Practical Tracking Control of Robot Manipulators With Continuous Fractional-Order Nonsingular Terminal Sliding Mode

This paper studies practical tracking control design of robot manipulators with continuous fractional-order nonsingular terminal sliding mode (CFONTSM) based on time-delay estimation (TDE). The proposed control design requires no detailed information about the robot dynamics, leading to an attractive model-free nature thanks to TDE, and ensures fast convergence and high tracking precision under heavy lumped uncertainties due to the FONTSM surface and fast-TSM-type reaching law. Stability of the closed-loop system and finite-time convergence are analyzed using Lyapunov stability theory. Comparative 2-DOF (degree of freedom) simulation and experiment results show that the proposed control design can ensure higher tracking precision and faster convergence compared with TDE-based continuous integer-order NTSM (CIONTSM) design in a wide range of speed; meanwhile, better performance is also observed compared with TDE-based IONTSM and FONTSM control designs using a boundary layer technique.

Tracking Control of Robotic Manipulators With Uncertain Kinematics and Dynamics

This paper investigates a difficult problem of tracking control for robotic manipulations with guaranteed high accuracy. Uncertain kinematics, unknown torques including unknown gravitational torque, unknown friction torque, and uncertain dynamics induced by uncertain moment of inertia and disturbance, are addressed. The approach is developed in the framework of observer-based control design. Two sliding-mode observers are proposed to handle uncertain kinematics and to estimate unknown torques, respectively. Using the estimated information, a control law is then synthesized to guarantee that the desired trajectory can be followed after finite-time with zero tracking error. Experimental results are presented to show the performance of the proposed control approach.

A novel adaptive finite-time tracking control for robotic manipulators using nonsingular terminal sliding mode and RBF neural networks

This paper presents a novel adaptive terminal sliding mode controller for the trajectory tracking of robotic manipulators using radial basis function neural networks (RBFNNs). First, a modified terminal sliding mode (TSM) surface is approached to avoid the singularity problem of conventional TSM. Then, a nonsingular TSM control is designed for joint position tracking of a robotic manipulator. In the control scheme, fully tuned RBFNNs are adopted to approximate the nonlinear unknown dynamics of the robotic manipulator. Adaptive learning algorithms are derived to allow online adjustment of the output weights, the centers and the variances in the RBFNNs. Meanwhile, a continuous robust control term is added to eliminate chattering efforts in the sliding mode control (SMC) system. The stability and finite-time convergence of the closed-loop system are established by using Lyapunov theory. Finally, the simulation results of a two-link robotic manipulator are presented to demonstrate the effectiveness of the proposed control method.

Discrete-time indirect adaptive fuzzy control for robot manipulators

Purpose – The purpose of this paper is to design a discrete indirect adaptive fuzzy controller for a robotic manipulator. This paper addresses how to overcome the approximation error of the fuzzy system and uncertainties for asymptotic tracking control of robotic manipulators. The uncertainties include parametric uncertainty, un-modeled dynamics, discretization error and external disturbances. Design/methodology/approach – The proposed controller is model-free and voltage-based in the form of discrete-time Mamdani fuzzy controller. The parameters of fuzzy controller are adaptively tuned for asymptotic tracking of a desired trajectory. A robust control term is used to compensate the approximation error of the fuzzy system. An adaptive mechanism is derived based on the stability analysis. Findings – The proposed model-free discrete control is robust against all uncertainties associated with the robot manipulator and actuators. The approximation error of the fuzzy system is well compensated to achieve asymptotic tracking of the desired trajectories. Stability analysis and simulation results show its efficiency in the tracking control. Originality/value – A novel discrete indirect adaptive fuzzy controller is designed for electrically driven robot manipulators using the voltage control strategy. The novelty of this paper is compensating the approximation error of the fuzzy system and discretizing error for asymptotic tracking of the desired trajectory.

Discrete adaptive fuzzy control for asymptotic tracking of robotic manipulators

This paper presents a novel discrete adaptive fuzzy controller for electrically driven robot manipulators. It addresses how to overcome the nonlinearity, uncertainties, discretizing error and approximation error of the fuzzy system for asymptotic tracking control of robotic manipulators. The proposed controller is model-free in the form of discrete Mamdani fuzzy controller. The parameters of fuzzy controller are adaptively tuned using an adaptive mechanism derived by stability analysis. A robust control term is used to compensate the approximation error of the fuzzy system for asymptotic tracking of a desired trajectory. The controller is robust against all uncertainties associated with the robot manipulator and actuators. It is easy to implement since it requires only the joint position feedback. Compared with fuzzy controllers which employ all states to guarantee stability, the proposed controller is very simpler. Stability analysis and simulation results show its efficiency in the tracking control.

Novel quasi-continuous super-twisting high-order sliding mode controllers for output feedback tracking control of robot manipulators

In this paper, a robust output feedback tracking control scheme for uncertain robot manipulators with only position measurements is investigated. First, a quasi-continuous second-order sliding mode (QC2S)-based exact differentiator and super-twisting second-order sliding mode (STW2S) controllers are designed to guarantee finite time convergence. Although the QC2S produces continuous control and less chattering than that of a conventional sliding mode controller and other high-order sliding mode controllers, a large amount of chattering exists when the sliding manifold is defined by the equation [Formula: see text]. To decrease the chattering, an uncertainty observer is used to compensate for the uncertainty effects, and this controller may possess a smaller switching gain. Compared to the QC2S controller, the STW2S has less chattering and tracking error when the system remains on the sliding manifold [Formula: see text]. Therefore, to further eliminate the chattering and obtain a faster transient response and higher tracking precision, we develop a quasi-continuous super-twisting second-order sliding mode controller, which integrates both the merits of QC2S and STW2S controllers. The stability and convergence of the proposed scheme are theoretically demonstrated. Finally, computer simulation results for a PUMA560 robot comparing with conventional QC2S and STW2S controllers are shown to verify the effectiveness of the proposed algorithm.

Enhancing precision performance of trajectory tracking controller for robot manipulators using RBFNN and adaptive bound

In this paper the design issues of trajectory tracking controller for robot manipulators are considered. The performance of classical model based controllers is reduced due to the presence of inherently existing uncertainties in the dynamic model of the robot manipulator. An intermediate approach between model based controllers and neural network based controllers is adopted to enhance the precision of trajectory tracking. The performance of the model based controller is enhanced by adding an RBF neural network and an adaptive bound part. The controller is able to learn the existing structured and unstructured uncertainties in the system in online manner. The RBF network learns the unknown part of the robot dynamics with no requirement of the offline training. The adaptive bound part is used to estimate the unknown bounds on unstructured uncertainties and neural network reconstruction error. The overall system is proved to be asymptotically stable. Finally, the numerical simulation results are produced with various controllers and the effectiveness of the proposed controller is shown in a comparative study for the case of a Microbot type robot Manipulator.

On the GPI approach with unknown inertia matrix in robot manipulators

This article discusses the design of generalised proportional integral observers for the tracking control of robot manipulators. The unknown, possibly state-dependent, additive nonlinearity influencing the input–output description, in terms of the tracking error dynamics is modelled for observer construction purposes, as an absolutely bounded, additive, unknown time-varying perturbation input signal. A disadvantage of the approach lies in the fact that the system inertia matrix is usually required for implementation. In this work, it is shown how the approach can be modified to avoid the use of the inertia matrix when it is unknown. A set of experiments with three different test beds is carried out to show the good performance of the proposed algorithm.

Adaptive Fuzzy and Robust H∞ Compensation Control for Uncertain Robot

In this paper, two types of robust adaptive compensation control schemes for the trajectory tracking control of robot manipulator with uncertain dynamics are proposed. The proposed controllers incorporate the computed-torque control scheme as a nominal portion of the controller; an adaptive fuzzy control algorithm to approximate the structured uncertainties; and a nonlinear H ∞ tracking control model as a feedback portion to eliminate the effects of the unstructured uncertainties and approximation errors. The validity of the robust adaptive compensation control schemes is investigated by numerical simulations of a two-link rotary robot manipulator

Finite-time tracking control for robot manipulators with actuator saturation

This paper addresses the finite-time tracking of robot manipulators in the presence of actuator saturation. The commonly-used proportional-derivative (PD) plus dynamics compensation (PD+) scheme is extended by replacing the linear errors in the PD+ scheme with saturated non-smooth but continuous exponential-like ones. Advantages of the proposed controller include semi-global finite-time tracking stability featuring faster transient and high-precision performances and the ability to ensure that actuator constraints are not violated. This is accomplished by selecting control gains a priori, removing the possibility of actuator failure due to excessive torque input levels. Lyapunov's direct method and finite-time stability are employed to prove semi-global finite-time tracking. Simulations performed on a three degree-of-freedom (DOF) manipulator are provided to illustrate the effectiveness and the improved performance of the formulated algorithm.

Robust Adaptive Fuzzy Control for Robot Manipulator with Uncertain Dynamics

A robust adaptive fuzzy control scheme combined with an adaptive fuzzy control algorithm and H∞ control model is proposed for the trajectory tracking control of robot manipulator with structured and unstructured uncertainties. The adaptive fuzzy control algorithm is employed to approximate structured uncertainties, and the nonlinear robust H control model is designed to eliminate the effect of the unstructured uncertainties and approximation errors. The validity of the robust adaptive fuzzy control scheme is demonstrated by numerical simulations of a two-link rotary robot manipulator.

Design Robust Fuzzy Sliding Mode Control Technique for Robot Manipulator Systems with Modeling Uncertainties

This paper describes the design and implementation of robust nonlinear sliding mode control strategies for robot manipulators whose dynamic or kinematic models are uncertain. Therefore a fuzzy sliding mode tracking controller for robot manipulators with uncertainty in the kinematic and dynamic models is design and analyzes. The controller is developed based on the unit quaternion representation so that singularities associated with the otherwise commonly used three parameter representations are avoided. Simulation results for a planar application of the continuum or hyper-redundant robot manipulator (CRM) are provided to illustrate the performance of the developed adaptive controller. These manipulators do not have rigid joints, hence, they are difficult to model and this leads to significant challenges in developing high-performance control algorithms. In this research, a joint level controller for continuum robots is described which utilizes a fuzzy methodology component to compensate for dynamic uncertainties.

Dynamics Modeling and Tracking Control of Robot Manipulators in Random Vibration Environment

In this technical brief, the problem of modeling and tracking control for the manipulator with multi-revolute joints in random vibration environment is considered. By analyzing the effect of environment to the mass points and introducing an equivalent stochastic noise process, a stochastic Hamiltonian dynamic model is constructed to describe the motion of the manipulator. Based on the constructed model, a state feedback backstepping controller in vector form is designed such that the unique solution of the closed-loop system is bounded in probability, and the mean square of the tracking error converges to an arbitrarily small neighborhood of zero.

Switched adaptive tracking control of robot manipulators with friction and changing loads

A switched adaptive controller is designed for robot manipulators with friction and changing loads. The nonlinear friction is depicted by a nonlinear friction model, and a switched nonlinear system is used to model the parameter jump caused by load change. Hyperstability theory is used in the designing procedure, which provides more options for adaptive laws than Lyapunov theory. In the presence of friction and changing loads, asymptotic tracking is achieved under arbitrary switching, which is not able to accomplish by a non-switched adaptive controller. The proposed method is validated by a simulation of a 2 degree of freedom manipulator.

Adaptive fuzzy quasi-continuous high-order sliding mode controller for output feedback tracking control of robot manipulators

This article develops a new output feedback tracking control scheme for uncertain robot manipulators with only position measurements. Unlike the conventional sliding mode controller, a quasi-continuous second-order sliding mode controller (QC2C) is first designed. Although the QC2C produces continuous control and less chattering than conventional sliding mode and other high-order sliding mode controllers, chattering exists when the sliding manifold is defined by the equation [Formula: see text]. To alleviate the chattering, an adaptive fuzzy QC2C (FQC2C) is designed, in which the fuzzy system is used to adaptively tune the sliding mode controller gain. Furthermore, in order to eliminate chattering and achieve higher tracking accuracy, quasi-continuous third-order sliding mode controller (QC3C) and fuzzy QC3C (FQC3C) are investigated. These controllers incorporate a super-twisting second-order sliding mode observer for estimating the joint velocities, and a robust exact differentiator to estimate the sliding manifold derivative; therefore, the velocity measurement is not required. Finally, computer simulation results for a PUMA560 industrial robot are also shown to verify the effectiveness of the proposed strategy.

Time-scale separation of a class of robust PD-type tracking controllers for robot manipulators

In this paper, the trajectory tracking control of robot manipulators is studied from the theoretical and practical point of view. By using the theory of singularly perturbed systems, a class of PD-type robust controllers is introduced. Our analysis departs from parameterizing the proportional and derivative gains with a perturbing parameter. We prove that the smaller the value of perturbing parameter, the smaller the ultimate bound of the joint position tracking error. Derived from the introduced analysis, two forms of extending the proposed class of controllers are discussed. In one, error-varying PD gains are considered while in the another one, a dynamic extension to avoid joint velocity measurements is incorporated. An experimental study in a planar two degrees-of-freedom direct-drive robot is also presented. Under similar implementation conditions, four controllers are tested. The best performance is obtained for a nonlinear PD controller derived from the proposed class of controllers.