Design, analysis, and control of a cable-driven parallel platform with a pneumatic muscle active support

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The neck is an important part of the body that connects the head to the torso, supporting the weight and generating the movement of the head. In this paper, a cable-driven parallel platform with a pneumatic muscle active support (CPPPMS) is presented for imitating human necks, where cable actuators imitate neck muscles and a pneumatic muscle actuator imitates spinal muscles, respectively. Analyzing the stiffness of the mechanism is carried out based on screw theory, and this mechanism is optimized according to the stiffness characteristics. While taking the dynamics of the pneumatic muscle active support into consideration as well as the cable dynamics and the dynamics of the Up-platform, a dynamic modeling approach to the CPPPMS is established. In order to overcome the flexibility and uncertainties amid the dynamic model, a sliding mode controller is investigated for trajectory tracking, and the stability of the control system is verified by a Lyapunov function. Moreover, a PD controller is proposed for a comparative study. The results of the simulation indicate that the sliding mode controller is more effective than the PD controller for the CPPPMS, and the CPPPMS provides feasible performances for operations under the sliding mode control

Some issues in the sliding mode control of rigid robotic manipulators

This thesis investigates the problem of robust adaptive sliding mode control for nonlinear rigid robotic manipulators. A number of robustness and convergence results are presented for sliding mode control of robotic manipulators with bounded unknown disturbances, nonlinearities, dynamical couplings and parameter uncertainties. The highlights of the research work are summarized below : • A robust adaptive tracking control for rigid robotic manipulators is proposed. In this scheme, the parameters of the upper bound of system uncertainty are adaptively estimated. The controller estimates are then used as controller parameters to eliminate the effects of system uncertainty and guarantee asymptotic error convergence. • A decentralised adaptive sliding mode control scheme for rigid robotic manipulators is proposed. The known dynamics of the partially known robotic manipulator are separated out to perform linearization. A local feedback controller is then designed to stabilize each subsystem and an adaptive sliding mode compensator is used to handle the effects of uncertain system dynamics. The developed scheme guarantees that the effects of system dynamics are eliminated and that asymptotic error convergence is obtained with respect to the overall robotic control system. • A model reference adaptive control using the terminal sliding mode technique is proposed. A multivariable terminal sliding mode is defined for a model following control system for rigid robotic manipulators. A terminal sliding mode controller is then designed based on only a few uncertain system matrix bounds. The result is a simple and robust controller design that guarantees convergence of the output tracking error in a finite time on the terminal sliding mode

Robust decentralised variable structure control for rigid robotic manipulators

In this thesis, the problem of robust variable structure control for non-linear rigid robotic manipulators is investigated. Robustness and convergence results are presented for variable structure control systems of robotic manipulators with bounded unknown disturbances, nonlinearities, dynamical couplings and parameter uncertainties. The major outcomes of the work described in this thesis are summarised as given below. The basic variable structure theory is surveyed, and some basic ideas such as sliding mode designs, robustness analysis and control1er design methods for linear or non-linear systems are reviewed. Three recent variable structure control schemes for robotic manipulators are discussed and compared to highlight the research developments in this area. A decentralised variable structure model reference adaptive control scheme is proposed for a class of large scale systems. It is shown that, unlike previous decentralised variable structure control schemes, the local variable structure controller design in this scheme requires only three bounds of the subsystem matrices and dynamical interactions instead of the upper and the lower bounds of all unknown subsystem parameters. Using this scheme, not only asymptotic convergence of the output tracking error can be guaranteed, but also the controller design is greatly simplified. In order to eliminate chattering caused by the variable structure technique, local boundary layer controllers are presented. Furthermore, the scheme is applied to the tracking control of robotic manipulators with the result that strong robustness and asymptotic convergence of the output tracking error are obtained

Terminal sliding mode control strategy design for second-order nonlinear system

This study mainly focuses on the terminal sliding mode control (TSMC) strategy design, including an adaptive terminal sliding mode control (ATSMC) and an exact-estimator-based terminal sliding mode control (ETSMC) for second-order nonlinear dynamical systems. In the ATSMC system, an adaptive bound estimation for the lump uncertainty is proposed to ensure the system stability. On the other hand, an exact estimator is designed for exact estimating system uncertainties to solve the trouble of chattering phenomena caused by a sign function in ATSMC law in despite of the utilization of a fixed value or an adaptive tuning algorithm for the lumped uncertainty bound. The effectiveness of the proposed control schemes can be verified in numerical simulations.<br /

Extended grey wolf optimization–based adaptive fast nonsingular terminal sliding mode control of a robotic manipulator

This article proposes a novel hybrid metaheuristic technique based on nonsingular terminal sliding mode controller, time delay estimation method, an extended grey wolf optimization algorithm and adaptive super twisting control law. The fast convergence is assured by nonsingular terminal sliding mode controller owing to its inherent nonlinear property and no prior knowledge of the robot dynamics is required due to time delay estimation. The proposed extended grey wolf optimization algorithm determines an optimal approximation of the inertial matrix of the robot. Moreover, adaptive super twisting control based on the Lyapunov approach overcomes the disturbances and compensate the higher dynamics not achievable by the time delay estimation method. First, the fast nonsingular terminal sliding mode controller relying on time delay estimation is designed and is combined with super twisting control for chattering attenuation. The constant gain matrix of the time delay is determined by the proposed extended grey wolf optimization algorithm. Second, an adaptive law based on Lyapunov stability theorem is designed for improving tracking performance in the presence of uncertainties and disturbances. The novelty of the proposed method lies in the adaptive law where the prior knowledge of parametric uncertainties and disturbances is not needed. Moreover, the constant gain matrix of time delay estimation method is obtained using the proposed algorithm. The control method has been tested in simulation on a 3-degrees of freedom robotic manipulator in trajectory tracking mode in the presence of control disturbances and uncertainties. The results obtained confirmed the effectiveness, robustness and the superior precision of the proposed control method compared to the classical ones

Robotic Manipulator Control in the Presence of Uncertainty

openThis research focuses on the problem of manipulator control in the presence of uncertainty and aims to compare different approaches for handling uncertainty while developing robust and adaptive methods that can control the robot without explicit knowledge of uncertainty bounds. Uncertainty is a pervasive challenge in robotics, arising from various sources such as sensor noise, modeling errors, and external disturbances. Effectively addressing uncertainty is crucial for achieving accurate and reliable manipulator control. The research will explore and compare existing methods for uncertainty handling such as robust feedback linearization , sliding mode control and robust adaptive control. These methods provide mechanisms to model and compensate for uncertainty in the control system. Additionally, modified robust and adaptive control methods will be developed that can dynamically adjust control laws based on the observed states, without requiring explicit knowledge of uncertainty bounds. To evaluate the performance of the different approaches, comprehensive experiments will be conducted on a manipulator platform. Various manipulation tasks will be performed under different levels of uncertainty, and the performance of each control approach will be assessed in terms of accuracy, stability, and adaptability. Comparative analysis will be conducted to highlight the strengths and weaknesses of each method and identify the most effective approach for handling uncertainty in manipulator control. The outcomes of this research will contribute to the advancement of manipulator control by providing insights into the effectiveness of different approaches for uncertainty handling. The development of new robust and adaptive control methods will enable manipulators to operate in uncertain environments without requiring explicit knowledge of uncertainty bounds. Ultimately, this research will facilitate the deployment of more reliable and adaptive robotic systems capable of handling uncertainty and improving their performance in various real-world applications.This research focuses on the problem of manipulator control in the presence of uncertainty and aims to compare different approaches for handling uncertainty while developing robust and adaptive methods that can control the robot without explicit knowledge of uncertainty bounds. Uncertainty is a pervasive challenge in robotics, arising from various sources such as sensor noise, modeling errors, and external disturbances. Effectively addressing uncertainty is crucial for achieving accurate and reliable manipulator control. The research will explore and compare existing methods for uncertainty handling such as robust feedback linearization , sliding mode control and robust adaptive control. These methods provide mechanisms to model and compensate for uncertainty in the control system. Additionally, modified robust and adaptive control methods will be developed that can dynamically adjust control laws based on the observed states, without requiring explicit knowledge of uncertainty bounds. To evaluate the performance of the different approaches, comprehensive experiments will be conducted on a manipulator platform. Various manipulation tasks will be performed under different levels of uncertainty, and the performance of each control approach will be assessed in terms of accuracy, stability, and adaptability. Comparative analysis will be conducted to highlight the strengths and weaknesses of each method and identify the most effective approach for handling uncertainty in manipulator control. The outcomes of this research will contribute to the advancement of manipulator control by providing insights into the effectiveness of different approaches for uncertainty handling. The development of new robust and adaptive control methods will enable manipulators to operate in uncertain environments without requiring explicit knowledge of uncertainty bounds. Ultimately, this research will facilitate the deployment of more reliable and adaptive robotic systems capable of handling uncertainty and improving their performance in various real-world applications

Design of PID, FLC and Sliding Mode Controller for 2-DOF Robotic Manipulator: A Comparative Study

Controlling the manipulators in a precise manner is a challenging task. To overcome this difficulty around the world, many researchers have developed various control algorithms but are not providing optimal results. To obtain the optimal results in the current research the authors designed a proportional, integral, and derivative (PID) controller, fuzzy logic controller (FLC), and sliding mode controller (SMC) for a 2-DOF manipulator. The concept of forward and inverse kinematics was initially solved after assigning the D-H parameters for each joint. The purpose of forward or direct kinematics is to obtain the position and orientation of the end effector. Further, the concept of inverse kinematics is used to estimate the joint angles. Later on, the Lagrange-Euler formulation was used to calculate the dynamics of the 2-DOF manipulator, which is required to estimate the torque required for each joint of the robotics arm. The main goal of this research problem is to optimize the angular error between the two successive events. Finally, the developed algorithm is compared with the existing algorithms such as PID and Fuzzy logic controller