Experimental comparison of parameter estimation methods in adaptive robot control

In the literature on adaptive robot control a large variety of parameter estimation methods have been proposed, ranging from tracking-error-driven gradient methods to combined tracking- and prediction-error-driven least-squares type adaptation methods. This paper presents experimental data from a comparative study between these adaptation methods, performed on a two-degrees-of-freedom robot manipulator. Our results show that the prediction error concept is sensitive to unavoidable model uncertainties. We also demonstrate empirically the fast convergence properties of least-squares adaptation relative to gradient approaches. However, in view of the noise sensitivity of the least-squares method, the marginal performance benefits, and the computational burden, we (cautiously) conclude that the tracking-error driven gradient method is preferred for parameter adaptation in robotic applications

Automated On-line Diagnosis and Control Configuration in Robotic Systems Using Model Based Analytical Redundancy

Because of the increasingly demanding tasks that robotic systems are asked to perform, there is a need to make them more reliable, intelligent, versatile and self-sufficient. Furthermore, throughout the robotic system?s operation, changes in its internal and external environments arise, which can distort trajectory tracking, slow down its performance, decrease its capabilities, and even bring it to a total halt. Changes in robotic systems are inevitable. They have diverse characteristics, magnitudes and origins, from the all-familiar viscous friction to Coulomb/Sticktion friction, and from structural vibrations to air/underwater environmental change. This thesis presents an on-line environmental Change, Detection, Isolation and Accommodation (CDIA) scheme that provides a robotic system the capabilities to achieve demanding requirements and manage the ever-emerging changes. The CDIA scheme is structured around a priori known dynamic models of the robotic system and the changes (faults). In this approach, the system monitors its internal and external environments, detects any changes, identifies and learns them, and makes necessary corrections into its behavior in order to minimize or counteract their effects. A comprehensive study is presented that deals with every stage, aspect, and variation of the CDIA process. One of the novelties of the proposed approach is that the profile of the change may be either time or state-dependent. The contribution of the CDIA scheme is twofold as it provides robustness with respect to unmodeled dynamics and with respect to torque-dependent, state-dependent, structural and external environment changes. The effectiveness of the proposed approach is verified by the development of the CDIA scheme for a SCARA robot. Results of this extensive numerical study are included to verify the applicability of the proposed scheme

Physical Human-Robot Interaction Control of an Upper Limb Exoskeleton with a Decentralized Neuro-Adaptive Control Scheme

Within the concept of physical human-robot interaction (pHRI), the most important criterion is the safety of the human operator interacting with a high degree of freedom (DoF) robot. Therefore, a robust control scheme is in high demand to establish safe pHRI and stabilize nonlinear, high DoF systems. In this paper, an adaptive decentralized control strategy is designed to accomplish the abovementioned objectives. To do so, a human upper limb model and an exoskeleton model are decentralized and augmented at the subsystem level to enable a decentralized control action design. Moreover, human exogenous force (HEF) that can resist exoskeleton motion is estimated using radial basis function neural networks (RBFNNs). Estimating both human upper limb and robot rigid body parameters, along with HEF estimation, makes the controller adaptable to different operators, ensuring their physical safety. The barrier Lyapunov function (BLF) is employed to guarantee that the robot can operate in a safe workspace while ensuring stability by adjusting the control law. Unknown actuator uncertainty and constraints are also considered in this study to ensure a smooth and safe pHRI. Then, the asymptotic stability of the whole system is established by means of the virtual stability concept and virtual power flows (VPFs) under the proposed robust controller. The experimental results are presented and compared to proportional-derivative (PD) and proportional-integral-derivative (PID) controllers. To show the robustness of the designed controller and its good performance, experiments are performed at different velocities, with different human users, and in the presence of unknown disturbances. The proposed controller showed perfect performance in controlling the robot, whereas PD and PID controllers could not even ensure stable motion in the wrist joints of the robot

Robust high-performance control for robotic manipulators

Model-based and performance-based control techniques are combined for an electrical robotic control system. Thus, two distinct and separate design philosophies have been merged into a single control system having a control law formulation including two distinct and separate components, each of which yields a respective signal component that is combined into a total command signal for the system. Those two separate system components include a feedforward controller and a feedback controller. The feedforward controller is model-based and contains any known part of the manipulator dynamics that can be used for on-line control to produce a nominal feedforward component of the system's control signal. The feedback controller is performance-based and consists of a simple adaptive PID controller which generates an adaptive control signal to complement the nominal feedforward signal

A spatial impedance controller for robotic manipulation

Mechanical impedance is the dynamic generalization of stiffness, and determines interactive behavior by definition. Although the argument for explicitly controlling impedance is strong, impedance control has had only a modest impact on robotic manipulator control practice. This is due in part to the fact that it is difficult to select suitable impedances given tasks. A spatial impedance controller is presented that simplifies impedance selection. Impedance is characterized using ¿spatially affine¿ families of compliance and damping, which are characterized by nonspatial and spatial parameters. Nonspatial parameters are selected independently of configuration of the object with which the robot must interact. Spatial parameters depend on object configurations, but transform in an intuitive, well-defined way. Control laws corresponding to these compliance and damping families are derived assuming a commonly used robot model. While the compliance control law was implemented in simulation and on a real robot, this paper emphasizes the underlying theor

Control strategies for robotic manipulators

This survey is aimed at presenting the major robust control strategies for rigid robot manipulators. The techniques discussed are feedback linearization/Computed torque control, Variable structure compensator, Passivity based approach and Disturbance observer based control. The first one is based on complete dynamic model of a robot. It results in simple linear control which offers guaranteed stability. Variable structure compensator uses a switching/relay action to overcome dynamic uncertainties and disturbances. Passivity based controller make use of passive structure of a robot. If passivity of a feedback system is proved, nonlinearities and uncertainties will not affect the stability. Disturbance observer based controllers estimate disturbances, which can be cancelled out to achieve a nominal model, for which a simple controller can then be designed. This paper, after explaining each control strategy in detail, finally compares these strategies for their pros and cons. Possible solutions to cope with the drawbacks have also been presented in tabular form. © 2012 IEEE

Integral Resonant Control for vibration damping and precise tip-positioning of a single-link flexible manipulator

Peer reviewedPostprin

Second order sliding mode control with adaptation

The paper proposes a new control scheme for nonlinear robotic systems. The basis of the scheme is an existing second order sliding mode controller augmented with an adaptive component. A computed torque-like adaptive controller with a second order system type of measure of tracking accuracy is the source of the adaptation law. Extensive simulations of a nonlinear robotic system with one rotational and one translational degree-of-freedom unmodeled actuator dynamics and parameter mismatch, confirm the suitability of the proposed scheme. The adaptation law allows smaller feedback gains to be used, and thus improves the robustness without degrading the performance. Adaptation may also improve the performance when unmodeled actuator dynamics is present, although to a lesser degree and not uniformly. The stability robustness in this case is slightly wors