Robust Control Of Robot Manipulators Research Articles

A Review on Robust Control of Robot Manipulators for Future Manufacturing

A Review on Robust Control of Robot Manipulators for Future Manufacturing

A model-free continuous integral sliding mode controller for robust control of robotic manipulators

<p>This paper proposes a model-free continuous integral sliding mode controller for robust control of robotic manipulators. The highly nonlinear dynamics of robots and load disturbances cause control challenges. To achieve tracking control under load disturbances and nonlinear parameter variations, the controller is constructed with three continuous terms including an integral term that acts as an adaptive controller. The proposed controller is able to accomplish a non-overshoot transient response, a short settling time, and strong disturbance rejection performance for robotic manipulators. The developed model-free control method is implemented on the PUMA 560 robotic manipulator, and its performance is compared with the proportional-derivative (PD) plus gravity controller. Numerical results under measurement noise and load disturbances are provided in order to show the efficacy, validity, and feasibility of the method.</p>

A model-free continuous integral sliding mode controller for robust control of robotic manipulators

A model-free continuous integral sliding mode controller for robust control of robotic manipulators

Robust Control of Robot Manipulator based on QFT and H∞

Robust controllers have been developed by both control techniques QFT and H∞ applied in the waist, shoulder and elbow of a manipulator of 6 degrees of freedom. The design is based on the identification of a linear model of the robot dynamics which represents the non-linearity of the system using parametric uncertainty. QFT control methodology is used to tune the robust PID-controller and pre-filters of the system, and H∞ controllers are obtained by designing the weighting functions and using the MATLAB hinfopt tool. Finally the performance of robust controllers is compared designed based on the calculation and analysis of some behavioral indices.

A robust control of robot manipulators for physical interaction: stability analysis for the interaction with unknown environments

A robust control of robot manipulators for physical interaction: stability analysis for the interaction with unknown environments

Model-free control of electrically driven robot manipulators using an extended state observer

This paper deals with the robust control of robot manipulators by presenting a novel voltage-based control approach. For this purpose, an Extended State Observer (ESO) is utilized to estimate the dynamical behaver of the system without any need to use the equations of motion including robot dynamics, actuator dynamics and their parameters in the proposed method. Convergence analysis proves that the estimated parameters converge to actual parameters asymptotically. The boundedness of tracking error is guaranteed through the Lyapunov theorem. Simulation results on an articulated robot driven by permanent magnet DC motors, and experimental implementation on a SCARA robot show that the proposed model-free controller has a satisfactory performance as compared to an adaptive uncertainty estimation-based controller.

Robust Control of Robotic Manipulators in the Task-Space Using an Adaptive Observer Based on Chebyshev Polynomials

In this paper, an adaptive observer for robust control of robotic manipulators is proposed. The lumped uncertainty is estimated using Chebyshev polynomials. Usually, the uncertainty upper bound is required in designing observer-controller structures. However, obtaining this bound is a challenging task. To solve this problem, many uncertainty estimation techniques have been proposed in the literature based on neuro-fuzzy systems. As an alternative, in this paper, Chebyshev polynomials have been applied to uncertainty estimation due to their simpler structure and less computational load. Based on strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be verified. The Chebyshev coefficients are tuned based on the adaptation rules obtained in the stability analysis. Also, to compensate the truncation error of the Chebyshev polynomials, a continuous robust control term is designed while in previous related works, usually a discontinuous term is used. An SCARA manipulator actuated by permanent magnet DC motors is used for computer simulations. Simulation results reveal the superiority of the designed method.

Robust Control of Robot Manipulators Using Inclusive and Enhanced Time Delay Control

Thanks to its simplicity and robustness, time delay control (TDC) has been recognized as a simple and yet effective alternative to robot model-based controls and/or intelligent controls. An inclusive and enhanced formulation of TDC for robust control of robot manipulators is presented in this paper. The proposed formulation consists of three intuitive terms: 1) time delay estimation (TDE), inherited from the original TDC, for cancellation of mostly continuous nonlinearities; 2) nonlinear desired error dynamics (DED) (i.e., a “mass”–“nonlinear damper”– “nonlinear spring” system) injection term; and 3) a TDE error correction term based on a nonlinear sliding surface. The proposed TDC formulation has an inclusive structure. Depending on the gain/parameter set chosen, the proposed formulation can become Hsia's formulation, Jin's formulations including a type of terminal sliding mode control (SMC), an SMC with a switching signum function, or a novel enhanced formulation. Experimental comparisons were made using a programmable universal manipulator for assembly-type robot manipulator with various parameter sets for the proposed control. Among them, the highest position tracking accuracy was obtained by using a terminal sliding DED with a terminal sliding correction term.

Adaptive Integral Sliding Mode Control With Time-Delay Estimation for Robot Manipulators

This paper presents an adaptive robust controller for robot manipulators using adaptive integral sliding mode control and time-delay estimation (TDE). The proposed controller employs TDE to estimate robot dynamics with uncertainties such as parameter variations and disturbances, an integral sliding surface to eliminate the reaching phase together with the noise-sensitive switching action used in the conventional sliding mode control, and adaptation gain dynamics to achieve the applicable high accuracy. Experimental studies using a programmable universal machine for assembly-type industrial robot manipulator are conducted to verify the effectiveness of the proposed control. The proposed controller is robust, chattering free, and highly accurate.

A New Polytopic Modeling with Uncertain Vertices and Robust Control of Robot Manipulators

This paper proposes \(H_{2}\)/\(H_{\infty }\) full state-feedback synthesis for robot manipulators, using uncertain polytopic linear parameter-varying (LPV) system modeling, with pole placement constraints to assign the poles of closed-loop system in a desired linear matrix inequality (LMI) region. The desired state trajectory of the system is used for generating an uncertain polytopic model of the system applying usual Lagrangian equations. The control gain matrix is derived by solving a set of LMIs to design a robust pole placement controller such that a prescribed mixed \(H_{2}\)/\(H_{\infty }\) performance is fulfilled and the response of the manipulator has a proper damping ratio. A sufficient condition is proposed to guarantee the asymptotic stability of the closed-loop uncertain polytopic LPV system against the uncertainties on the vertices. The proposed scheme is applied to controller synthesis of a two-degree-of-freedom manipulator trajectory-tracking problem. The simulation results show the effectiveness of the proposed controller.

Robust control of robotic manipulators based on left inverse system and variable structure model reference adaptive control

SummaryThis work proposes a control structure to be applied to robotic manipulators, which are articulated mechanical systems composed of links connected by joints. The proposed controller can be divided into two parts. The first one is a left inverse system, which is used to decouple the dynamic behavior of the joints. The second is a sliding mode controller, which is applied for each decoupled joint. It is important to note that the proposed structure, using only input/output measurements, reduces the control signal ‘chattering’, and it is robust to parametric uncertainties. Besides all the characteristics presented, the proposed structure simplifies the design of sliding mode controller to be applied in robotic manipulators. All these features are verified by simulations. Copyright © 2016 John Wiley & Sons, Ltd.

Adaptive RBF network control for robot manipulators

The uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function network (RBF network) as an uncertainty estimator. The proposed network includes a hidden layer with one node, two inputs and a single output. In comparison with other model-free estimators such as multilayer neural networks and fuzzy systems, the proposed estimator is simpler, less computational and more effective. The weights of the RBF network are tuned online using an adaptation law derived by stability analysis. Despite the majority of previous control approaches which are the torque-based control, the proposed control design is the voltage-based control. Simulations and comparisons with a robust neural network control approach show the efficiency of the proposed control approach applied on the articulated robot manipulator driven by permanent magnet DC motors.

Comparative Study between Robust Control of Robotic Manipulators by Static and Dynamic Neural Networks

A comparative study between static and dynamic neural networks for robotic systems control is considered. So, two approaches of neural robot control were selected, exposed, and compared. One uses a static neural network; the other uses a dynamic neural network. Both compensate the nonlinear modeling and uncertainties of robotic systems. The first approach is direct; it approximates the nonlinearities and uncertainties by a static neural network. The second approach is indirect; it uses a dynamic neural network for the identification of the robot state. The neural network weight tuning algorithms, for the two approaches, are developed based on Lyapunov theory. Simulation results show that the system response, equipped by dynamic neural network controller, has better tracking performance, has faster response time, and is more reliable to face disturbances and robotic uncertainties.

Stability Guaranteed Time Delay Control of Manipulators Using Nonlinear Damping and Terminal Sliding Mode

Time-delay control has been verified as a simple and robust controller for robot manipulators. However, time-delay estimation (TDE) error inherently exists and critically affects both the closed-loop stability and control performance. In this paper, we propose a remedy for the TDE error that involves a combination of a nonlinear damping component and a novel fast-convergent error dynamics. Nonlinear damping incorporated with a backstepping design is adopted to counteract TDE error and ensure closed-loop stability. The fast-convergent error dynamics, constructed by means of terminal sliding mode (TSM), is introduced to enhance the control performance degraded by the TDE error. Through a rigorous stability analysis, it is proved that the tracking error of the closed-loop system due to the proposed control scheme is globally uniformly ultimately bounded. Through simulations and experiments, it is verified that the nonlinear damping counteracts the TDE error, while the TSM speeds up the convergence of the error dynamics. Finally, these two elements together substantially enhance the control accuracy.

Robust control of robot manipulators based on uncertainty and disturbance estimation

SUMMARYIn this work, uncertainty and disturbance estimation (UDE) based robust trajectory tracking controller for rigid link manipulators was proposed. The UDE was employed to estimate the composite uncertainty that comprises the effects of system nonlinearities, external disturbances, and parametric uncertainties. A feedback linearization based controller was designed for trajectory tracking, and the same was augmented by the UDE‐estimated uncertainties to achieve robustness. The resulting controller however required measurement of joint velocities apart from the joint positions. To address the issue, an observer that employed the UDE‐estimated uncertainties for robustness was proposed, giving rise to the UDE‐based controller–observer structure. Closed‐loop stability of the overall system was established. The notable feature of the proposed design was that it neither required accurate plant model nor any information about the uncertainty. Also, the design needed only joint position measurements for its implementation. To demonstrate the effectiveness, simulation results of the proposed approach as applied to the trajectory tracking control of two‐link robotic manipulator and comparison of its performance with some of the well‐known existing controllers were presented. Lastly, hardware implementation of the proposed design for trajectory control of Quanser's single‐link flexible joint module was carried out, and it was shown that the proposed strategy offered a viable approach for designing implementable robust controllers for robots. Copyright © 2011 John Wiley & Sons, Ltd.

A novel adaptive controller for two-degree of freedom polar robot with unknown perturbations

In industrial applications, the performance of robot manipulators is always affected due to the presence of uncertainties and disturbances. This paper proposes a novel adaptive control scheme for robust control of robotic manipulators perturbed by unknown uncertainties and disturbances. First, an active sliding mode controller is designed and a sufficient condition is obtained guarantying reachability of the states to hit the sliding surface in finite time. Then, based on a Lyapunov function candidate an adaptive switching gain is derived which make the controller capable to bring the tracking error to zero without any disturbance exerted upon the stability. By virtue of this controller it can be shown that the controller can track the desired trajectories even in the presence of unknown perturbations. For the problem of determining the control parameters Particle Swarm Optimization (PSO) algorithm has been employed. Our theoretic achievements are verified by numerical simulations.

Modelling of Bound Estimation Laws and Robust Controllers for Robustness to Parametric Uncertainty for Control of Robot Manipulators

In this paper, first, a new approach is proposed for derivation of bound estimation laws for robust control of robot manipulators. In this approach, functions depending on robot kinematics and control parameters and integration techniques can be used for derivation of the bound estimation laws based on the Lyapunov theory, thus, stability of the uncertain system is guaranteed. Five new bound estimation laws are proposed, and in this derivations, five novel functions depending on robot kinematics and control parameters and proper integration techniques, such as substitution method, integration by part and integration by partial fractions are used. Then, four new robust control inputs are proposed based on each derived bound estimation law. Lyapunov theory based on Corless and Leitmann (IEEE Trans Automat Contr 26:1139---1144, 1981) approach is used for designing the robust control input achieving uniform boundedness error convergence. This study also allows derivation of other bound estimation laws for robust controllers provided that appropriate novel functions and proper integration techniques are chosen.

Decentralized robust control of robot manipulators with harmonic drive transmission and application to modular and reconfigurable serial arms

SUMMARYIn this paper, we propose a decentralized robust control algorithm for modular and reconfigurable robots (MRRs) based on Lyapunov's stability analysis and backstepping techniques. In using decentralized control schemes with robot manipulators, each joint is considered as an independent subsystem, and the dynamical effects from the other links and joints are treated as disturbance. However, there exist many uncertainties due to unmodeled dynamics, varying payloads, harmonic drive (HD) compliance, HD complex gear meshing mechanisms, etc. Also, while the reconfigurability of MRRs is advantageous, modifying the configuration will result in changes to the robot dynamics parameters, thereby making it challenging to tune the control system. All of the above mentioned disturbances in addition to reconfigurability present a challenge in controlling MRRs. The proposed controller is well suited for MRR applications because of its simple structure that does not require the exact knowledge of the dynamic parameters of the configurations. Desired tracking performance can be achieved via tuning a limited set of parameters of the robust controller. If the numbers of degrees of freedom are held constant, these parameters are shown to be relatively independent of the configuration, and can be held constant between changes in configuration. This strategy is novel compared to existing MRR control methods. In order to validate the controller performance, experimental setup and results are also presented.

Robust control of robotic manipulators based on integral sliding mode

For rigid body robot manipulators, the computed torque approach provides asymptotic stability for tracking control tasks. However, the state dependent matrices needed to complete the computed torque algorithm are normally unknown and possibly too complex for a real-time implementation. This paper proposes a simple controller with computed-torque-like structure enhanced by integral sliding mode, having pole-placement capability. For the reduction of the chattering effect generated by the sliding mode part, the integral sliding mode is posed as a perturbation estimator with quasi-continuous control action provided by an additional low-pass filter. The time-constant of the latter tunes the controller functionality between the perturbation compensation and a pure integral sliding mode control, as well as between chattering reduction and system robustness. A comparative simulation study between conventional sliding mode control, integral sliding mode control, and integral sliding mode in form of a perturbation estimator for a two-link robot arm validates the proposed design.

A Markov Game-Adaptive Fuzzy Controller for Robot Manipulators

This paper develops an adaptive fuzzy controller for robot manipulators using a Markov game formulation. The Markov game framework offers a promising platform for robust control of robot manipulators in the presence of bounded external disturbances and unknown parameter variations. We propose fuzzy Markov games as an adaptation of fuzzy Q-learning (FQL) to a continuous-action variation of Markov games, wherein the reinforcement signal is used to tune online the conclusion part of a fuzzy Markov game controller. The proposed Markov game-adaptive fuzzy controller uses a simple fuzzy inference system (FIS), is computationally efficient, generates a swift control, and requires no exact dynamics of the robot system. To illustrate the superiority of Markov game-adaptive fuzzy control, we compare the performance of the controller against a) the Markov game-based robust neural controller, b) the reinforcement learning (RL)-adaptive fuzzy controller, c) the FQL controller, d) the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> theory-based robust neural game controller, and e) a standard RL-based robust neural controller, on two highly nonlinear robot arm control problems of i) a standard two-link rigid robot arm and ii) a 2-DOF SCARA robot manipulator. The proposed Markov game-adaptive fuzzy controller outperformed other controllers in terms of tracking errors and control torque requirements, over different desired trajectories. The results also demonstrate the viability of FISs for accelerating learning in Markov games and extending Markov game-based control to continuous state-action space problems.