Robust prescribed trajectory tracking control of a robot manipulator using adaptive finite-time sliding mode and extreme learning machine method

This study aims to provide a robust trajectory tracking controller which guarantees the prescribed performance of a robot manipulator, both in transient and steady-state modes, experiencing parametric uncertainties. The main core of the controller is designed based on the adaptive finite-time sliding mode control (SMC) and extreme learning machine (ELM) methods to collectively estimate the parametric model uncertainties and enhance the quality of tracking performance. Accordingly, the global estimation with a fast convergence rate is achieved while the tracking error and the impact of chattering on the control input are mitigated significantly. Following the control design, the stability of the overall control system along with the finite-time convergence rate is proved, and the effectiveness of the proposed method is investigated via extensive simulation studies. The results of simulations confirm that the prescribed transient and steady-state performances are obtained with enough accuracy, fast convergence rate, robustness, and smooth control input which are all required for practical implementation and applications

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

A Novel Adaptive Sliding Mode Controller for a 2-DOF Elastic Robotic Arm

Collaborative robots (or cobots) are robots that are capable of safely operating in a shared environment or interacting with humans. In recent years, cobots have become increasingly common. Compliant actuators are critical in the design of cobots. In real applications, this type of actuation system may be able to reduce the amount of damage caused by an unanticipated collision. As a result, elastic joints are expected to outperform stiff joints in complex situations. In this work, the control of a 2-DOF robot arm with elastic actuators is addressed by proposing a two-loop adaptive controller. For the outer control loop, an adaptive sliding mode controller (ASMC) is adopted to deal with uncertainties and disturbance on the load side of the robot arm. For the inner loops, model reference adaptive controllers (MRAC) are utilised to handle the uncertainties on the motor side of the robot arm. To show the effectiveness of the proposed controller, extensive simulation experiments and a comparison with the conventional sliding mode controller (SMC) are carried out. As a result, the ASMC has a 50.35% lower average RMS error than the SMC controller, and a shorter settling time (5% criterion) (0.44 s compared to 2.11 s).publishedVersio

Operational space control of a lightweight robotic arm actuated by shape memory alloy wires: a comparative study.

This article presents the design and control of a two-link lightweight robotic arm using shape memory alloy wires as actuators. Both a single-wire actuated system and an antagonistic configuration system are tested in open and closed loops. The mathematical model of the shape memory alloy wire, as well as the kinematics and dynamics of the robotic arm, are presented. The operational space control of the robotic arm is performed using a joint space control in the inner loop and closed-loop inverse kinematics in the outer loop. In order to choose the best joint space control approach, a comparative study of four different control approaches (proportional derivative, sliding mode, adaptive, and adaptive sliding mode control) is carried out for the proposed model. From this comparative analysis, the adaptive controller was chosen to perform operational space control. This control helps us to perform accurate positioning of the end-effector of shape memory alloy wire–based robotic arm. The complete operational space control was successfully tested through simulation studies performing position reference tracking in the end-effector space. Through simulation studies, the proposed control solution is successfully verified to control the hysteretic robotic arm

Adaptive robust control and admittance control for contact-driven robotic surface conditioning

[EN] This work presents a hybrid position/force control of robots for surface contact conditioning tasks such as polishing, profiling, deburring, etc. The robot force control is designed using sliding mode ideas to benefit from robustness. On the one hand, a set of equality constraints are defined to attain the desired tool pressure on the surface, as well as to keep the tool orientation perpendicular to the surface. On the other hand, inequality constraints are defined to adapt the tool position to unmodeled features present in the surface, e.g., a protruding window frame. Conventional and non-conventional sliding mode controls are used to fulfill the equality and inequality constraints, respectively. Furthermore, in order to deal with sudden changes of the material stiffness, which are forwarded to the robot tool and can produce instability and bad performance, adaptive switching gain laws are considered not only for the conventional sliding mode control but also for the non-conventional sliding mode control. A lower priority tracking controller is also defined to follow the desired reference trajectory on the target surface. Moreover, the classical admittance control typically used in force control tasks is adapted for the proposed surface contact application in order to experimentally compare the performance of both control approaches. The effectiveness of the proposed method is substantiated by experimental results using a redundant 7R manipulator, whereas its advantages over the classical admittance control approach are experimentally shown.This work was supported in part by the Spanish Government under the Project DPI2017-87656-C2-1-R and the Generalitat Valenciana under Grants VALi+d APOSTD/2016/044 and BEST/2017/029.Solanes Galbis, JE.; Gracia Calandin, LI.; Muñoz-Benavent, P.; Esparza Peidro, A.; Valls Miro, J.; Tornero Montserrat, J. (2018). Adaptive robust control and admittance control for contact-driven robotic surface conditioning. Robotics and Computer-Integrated Manufacturing. 54:115-132. https://doi.org/10.1016/j.rcim.2018.05.003S1151325

Adaptive Sliding Mode Control for Robotic Surface Treatment Using Force Feedback

[EN] This work presents a hybrid position-force control of robots in order to apply surface treatments such as polishing, grinding, finishing, deburring, etc. The robot force control is designed using sliding mode concepts to benefit from robustness. In particular, the sliding mode force task is defined using equality constraints to attain the desired tool pressure on the surface, as well as to keep the tool orientation perpendicular to the surface. In order to deal with sudden changes in material stiffness, which are ultimately transferred to the polishing tool and can produce instability and compromise polishing performance, several adaptive switching gain laws are considered and compared. Moreover, a lower priority tracking controller is defined to follow the desired reference trajectory on the surface being polished. Hence, deviations from the reference trajectory are allowed if such deviations are required to satisfy the constraints mentioned above. Finally, a third-level task is also considered for the case of redundant robots in order to use the remaining degrees of freedom to keep the manipulator close to the home configuration with safety in mind. The main advantages of the method are increased robustness and low computational cost. The applicability and effectiveness of the proposed approach are substantiated by experimental results using a redundant 7R manipulator: the Rethink Robotics Sawyer collaborative robot.This work was supported in part by the Spanish Government under the project DPI2017-87656-C2-1-R and the Generalitat Valenciana under Grants VALi + d APOSTD/2016/044 and BEST/2017/029.Gracia Calandin, LI.; Solanes Galbis, JE.; Muñoz-Benavent, P.; Valls Miro, J.; Perez-Vidal, C.; Tornero Montserrat, J. (2018). Adaptive Sliding Mode Control for Robotic Surface Treatment Using Force Feedback. Mechatronics. 52:102-118. https://doi.org/10.1016/j.mechatronics.2018.04.008S1021185

Robust Prescribed Trajectory Tracking Control of a Robot Manipulator Using Adaptive Finite-Time Sliding Mode and Extreme Learning Machine Method

peer reviewedThis study aims to provide a robust trajectory tracking controller which guarantees the prescribed performance of a robot manipulator, both in transient and steady-state modes, experiencing parametric uncertainties. The main core of the controller is designed based on the adaptive finite-time sliding mode control (SMC) and extreme learning machine (ELM) methods to collectively estimate the parametric model uncertainties and enhance the quality of tracking performance. Accordingly, the global estimation with a fast convergence rate is achieved while the tracking error and the impact of chattering on the control input are mitigated significantly. Following the control design, the stability of the overall control system along with the finite-time convergence rate is proved, and the effectiveness of the proposed method is investigated via extensive simulation studies. The results of simulations confirm that the prescribed transient and steady-state performances are obtained with enough accuracy, fast convergence rate, robustness, and smooth control input which are all required for practical implementation and applications

Hierarchical Adaptive Control of Modular and Reconfigurable Robot Manipulator Platforms

Within the rapidly growing interest in today's robotics industry, modular and reconfigurable robots (MRRs) are among the most auspicious systems to expand the adaptability of robotic applications. They are adaptable to multiple industrial field applications but they also have additional advantages such as versatile hardware, easier maintenance, and transportability. However, such features render the controller design that manages a variety of robot configurations with reliable performance more complex since their system dynamics involve not only nonlinearities and uncertainties but also changing dynamics parameters after the reconfiguration. In this thesis, the motion control problem of MRR manipulators is addressed and hierarchical adaptive control architecture is developed for MRRs. This hierarchical structure allows the adjustment of the nominal parameters of an MRR system for system parameter identification and control design purposes after the robot is reconfigured. This architecture simplifies the design of adaptive control for MRRs which is effective in the presence of dynamic parameter uncertainty, unmodeled dynamics, and disturbance. The proposed architecture provides flexibility in choosing adaptive algorithms applicable to MRRs. The developed architecture consists of high-level and low-level modules. The high-level module handles the dynamic parameters changes and reconstructs the parametric model used for on-line parameter identification after the modules are reassembled. The low-level structure consists of an adaptive algorithm updated by an on-line parameter estimation to handle the dynamic parameter uncertainties. Furthermore, a robust adaptive term is added into this low-level controller to compensate for the unmodeled dynamics and disturbances. The proposed adaptive control algorithms guarantee uniformly ultimate boundedness (UUB) of the MRR trajectories in terms of robust stability despite the dynamic parameter uncertainty, unmodeled dynamics, changes in the system dynamics, and disturbance

Preliminary Design of Robust Adaptive Control Laws for an Aerial Manipulator

One platform commonly used in modern control systems research is the quadcopter unmanned aerial vehicle (UAV). This platform finds many uses in both civilian and military aviation, such as aerial surveillance and imaging, search and rescue operations, and remote sensing. To perform these tasks, it is increasingly common to rely on autonomous UAVs to allow the vehicle to perform desired tasks without an operator. One weakness of the quadcopter UAV is its underactuation, since this vehicle has six degrees of freedom and only four control inputs. To overcome this complexity, it is proposed to actuate the vehicle propellers, creating a tiltrotor vehicle, in this thesis of the H-configuration. To this end, the equations of motion of the vehicle will be established, and an original robust model reference adaptive control law will be formulated to control the vehicle in the presence of disturbances. Another current goal in UAV research is in providing a method for the vehicle to manipulate its environment. In this thesis, a two-link robotic manipulator mounted on a cylindrical hinge will be used. This manipulator will have its own trajectory generation and control formulation for its end-effector, after which it will be mounted to the H-configuration tiltrotor. This combined aerial manipulator will be numerically simulated with the manipulator and tiltrotor control laws running simultaneously, demonstrating the feasibility of the combined system