Yiannis Karayiannidis

Brief paper: Force/position tracking for a robotic manipulator in compliant contact with a surface using neuro-adaptive control

The problem of force/position tracking for a robotic manipulator in compliant contact with a surface under non-parametric uncertainties is considered. In particular, structural uncertainties are assumed to characterize the compliance and surface friction models, as well as the robot dynamic model. A novel neuro-adaptive controller is proposed, that exploits the approximation capabilities of the linear in the weights neural networks, guaranteeing the uniform ultimate boundedness of force and position error with respect to arbitrarily small sets, plus the boundedness of all signals in the closed loop. Simulations highlight the approach.

Force position control for a robot finger with a soft tip and kinematic uncertainties

We consider the problem of force and position regulation for a robot finger with a soft tip in contact with a surface with unknown geometrical characteristics. An adaptive controller is proposed, and the asymptotic convergence of the applied force error and the estimated position error of the tip to zero is shown for the spatial case. Simulation results demonstrate the controller performance.

Brief paper: Adaptive control of robot contact tasks with on-line learning of planar surfaces

In robot constrained motion problems on planar surfaces with frictional contacts, uncertainties on the contacted surface not only affect the control system performance but also distort control targets. The surface normal direction cosines are in this case uncertain parameters that are involved in both the control law and the control targets. This work proposes an adaptive learning controller that uses force and joint position/velocity measurements to simultaneously learn the surface orientation and achieve the desired goal. Simulation examples for a 6 dof robot are used to illustrate the theoretical results and the performance of the proposed controller in practical cases.

Force/Position Tracking of a Robot in Compliant Contact with Unknown Stiffness and Surface Kinematics

This work deals with the problem of force/position trajectory tracking under uncertainties arising from surface position and orientation. A robotic finger with a soft hemispherical tip of uncertain compliance parameter is considered in contact with a rigid flat surface. A novel adaptive controller is designed using online estimates of the unknown parameters and is proved to achieve force and position tracking by ensuring the convergence of the estimated normal to the surface direction to its actual value. The performance of the proposed controller is demonstrated by a simulation example.

An adaptive law for slope identification and force position regulation using motion variables

This work proposes an adaptive control law for the force position regulation problem under surface kinematic uncertainties. A compliant contact with friction is considered. The control law achieves exact regulation of force and position along the surface tangent by identifying the surface slope. The asymptotic stability of the closed loop system equilibrium point is proved in a local sense and is demonstrated by a simulation example

Prescribed performance control for robot joint trajectory tracking under parametric and model uncertainties

This work proposes a control law for the robot joint trajectory tracking in free space that achieves a prescribed performance of the joint position error under parametric uncertainties; the control law is extended for the case of bounded disturbances. A performance function incorporating predefined performance indices is used to produce a transformed error that is injected in the controller. Furthermore, asymptotic stability of the velocity error in case of zero disturbances and uniformly ultimate boundedness in an arbitrarily small region for bounded disturbances is achieved. Simulation results confirm the theoretical findings and compare the proposed controller with a conventional one.

Force/position control self-tuned to unknown surface slopes using motion variables

This work considers the problem of force/position regulation for a robotic finger in compliant contact with an unknown curved surface resulting in uncertain force and position control subspaces. The proposed controller is an adaptive control scheme of a simple structure that achieves the desired target by the on-line tuning of the position and force control actions to their corresponding actual subspaces at the desired point using motion state feedback. The local asymptotic stability of the system equilibrium point is proved and an estimate of the region of attraction is given. The controller performance is illustrated by a simulation example.

Force/position tracking for a robotic finger in compliant contact with a surface using neuro-adaptive control

In this work, the problem of force/position tracking for a robotic finger in compliant contact with a surface under non-parametric uncertainties is considered. In particular, structural uncertainties are assumed to characterize the compliance model as well as the robot dynamic model. A novel neuro-adaptive controller is proposed that exploits the approximation capabilities of the linear in the weights neural networks and the uniform ultimate boundedness of force and position error is proved. Simulation results illustrate the performance of the proposed controller

PID type robot joint position regulation with prescribed performance guaranties

This paper proposes a PID type regulator that achieves not only the global asymptotic convergence of the robot joint velocities and position errors to zero but it also guarantees a prescribed performance for the position error transient that is independent of system constants and control parameters. The proportional term of the control input uses a transformed error (TP) which incorporates the desired performance function; given sufficiently high proportional and damping gains, the proposed TPID controller ensures the position errors prescribed performance irrespective of constant disturbances and choice of control gains. Control parameter selection is merely confined in achieving admissible input torques. Simulation results for a three dof spatial robot confirm the theoretical analysis and illustrate the robustness of the prescribed performance regulator in case of time-variant bounded disturbances.

Robot contact tasks in the presence of control target distortions

This work refers to the problem of controlling robot motion and force in frictional contacts under environmental errors and particularly orientation errors that distort the desired control targets and control subspaces. The proposed method uses online estimates of the surface normal (tangent) direction to dynamically modify the control target and control space decomposition. It is proved that these estimates converge to the actual value even though the elasticity and friction parameters are unknown. The proposed control solution is demonstrated through simulation examples in three-dimensional robot motion tasks contacting both planar and curved surfaces.