Alejandro Rodriguez-Angeles

Mutual synchronization of robots via estimated state feedback: a cooperative approach

In this paper, a controller that solves the problem of position synchronization of two (or more) robot systems, under a cooperative scheme, in the case when only position measurements are available, is presented. The synchronization controller consists of a feedback control law and a set of nonlinear observers. Coupling errors are introduced to create interconnections that render mutual synchronization of the robots. It is shown that the controller yields semiglobal exponential convergence of the synchronization closed-loop errors. Experimental results show, despite obvious model uncertainties, a good agreement with the predicted convergence.

On the linear control of nonlinear mechanical systems

This article describes the design of a linear observer-linear controller-based robust output feedback scheme for output reference trajectory tracking tasks in a large class of fully actuated nonlinear mechanical systems whose generalized position coordinates are measurable. The unknown, possibly state-dependent, additive nonlinearity influencing the input-output description, in terms of the tracking error dynamics, is modeled as an absolutely bounded, additive, unknown “time-varying perturbation” input signal. This procedure simplifies the system tracking error description to that of independent chains of integrators with, known, position-dependent control gains, while additively being perturbed by an unknown, smooth, time-varying signal which is trivially observable. The total state-dependent uncertain input is assumed to be locally approximated by an arbitrary element of, a, fixed, sufficiently high degree family of Taylor polynomials for which a linear observer may be readily designed. Generalized Proportional Integral (GPI) observers, which are the dual counterpart of GPI controllers (see [3]), are shown to naturally estimate, in an arbitrarily close manner, the unknown perturbation input of the simplified system and a certain number of its time derivatives, thanks to its embedded, internal time-polynomial model of the unknown, state-dependent, perturbation input. This information is used to advantage on the linear, observer-based, feedback controller design via a simple cancelation effort. The results are applied to the control of a laboratory prototype in a trajectory tracking problem.

Synchronizing Tracking Control for Flexible Joint Robots via Estimated State Feedback

In this paper, we propose a synchronization controller for flexible joint robots, which are interconnected in a master-slave scheme. The synchronization controller is based on feedback linearization and only requires measurements of the master and slave link positions, since the velocities and accelerations are estimated by means of model-based nonlinear observers. It is shown, using Lyapunov function based stability analysis, that the proposed synchronization controller yields local uniformly ultimately boundedness of the closed loop errors. A tuning gain procedure is presented. The results are supported by simulations in a one degree of freedom master-slave system.

Robust GPI observer under noisy measurements

In this paper, a new approach for the Generalized Proportional Integral Observer when only noisy measurements are available is proposed, based in a integral extention of the system and observer model. A comparison of the GPI observer to the new approach is given via an academic simulation example. With this approach, is possible to estimate completely unknown, but bounded, perturbations and the states of the system, such that a perturbation-cancel plus PD compensation control is proposed for trajectory tracking tasks.

Adaptive control with impedance of cooperative multi-robot system

In this paper it is shown that smooth object maneuvering can be achieved via an adaptive control scheme for cooperative control of multiple manipulators subject to holonomic constraints. Smooth object maneuvering is obtained via a dynamic impedance relationship, Optimal internal forces that ensure a stable grasp are obtained dynamically by linearly constrained gradient flows. Asymptotic stability is proved via a Lyapunov function and smooth performance and convergence are shown via simulations.

An active disturbance rejection controller for a parallel Robot via Generalized Proportional Integral observers

In this article, we address an active disturbance rejection controller design for the output reference trajectory tracking problem in a 3 degree of freedom (DOF) Delta Robot. The proposed method relies on purely linear high gain disturbance observation and linear feedback control techniques. The estimation tasks are carried out with the help of Generalized Proportional Integral (GPI) observers, endowed with output integral injection to counteract zero mean measurement noise effects. As the lumped exogenous and endogenous disturbance inputs are estimated, the observers deliver them to the controllers for on-line disturbance cancelation, while simultaneously the phase variables, related to the measured flat outputs, are being estimated by the same GPI observer. The gathered values of the phase variables are used to complete a linear multivariable output feedback control scheme. The proposed control scheme avoids the traditional computed torque method, reducing the computation time and bypassing the need for explicit, accurate, knowledge of the plant. The estimation and control method is only approximate as small as desired reconstruction, or tracking, errors are guaranteed. The reported results, including laboratory experiments, are significantly better than the results provided by the classical model-based techniques, when the system is subject to endogenous and exogenous uncertainties.

Cooperative synchronization of robots via estimated state feedback

A controller that solves the problem of position synchronization of two (or more) robot systems, under a cooperative scheme, in the case when only position measurements are available, is presented. The synchronization controller consists of a feedback control law and a set of nonlinear observers. It is shown that the controller yields semi-global exponential convergence of the synchronization closed loop errors. Experimental results show, despite obvious model uncertainties, a good agreement with the predicted convergence.

An online inertial sensor-guided motion control for tracking human arm movements by robots

We propose a simple and efficient online strategy based on inertial sensors to follow human arm movements in the task space by robotic platforms. The strategy is composed by the following ingredients: 2 inertial sensors attached to the human arm and forearm, the differential kinematics (DK) as well as the dynamic model of the corresponding robot and a motion control scheme. We use inertial sensors to obtain the orientation measurements of the human bodies in real-time. These body parameters together with the DK module allow us to map human hand trajectories into the joint space of the robot, where a standard PID control is implemented. Overall our strategy is able to transfer motion skills from human to a given robot by means of an online inertial sensor-guided motion control. Moreover, our method can be used for robot teleoperation applications in a master-slave configuration, with a human interface based on inertial sensors. We have successfully validated in simulations the effectiveness of the proposed strategy in a 2 degrees of freedom (dof) planar robot.

An Optimization-Based Impedance Approach for Robot Force Regulation with Prescribed Force Limits

An optimization based approach for the regulation of excessive or insufficient forces at the end-effector level is introduced. The objective is to minimize the interaction force error at the robot end effector, while constraining undesired interaction forces. To that end, a dynamic optimization problem (DOP) is formulated considering a dynamic robot impedance model. Penalty functions are considered in the DOP to handle the constraints on the interaction force. The optimization problem is online solved through the gradient flow approach. Convergence properties are presented and the stability is drawn when the force limits are considered in the analysis. The effectiveness of our proposal is validated via experimental results for a robotic grasping task.

Master/Slave Robotic System for Teaching Motion-Force Manufacturing Tasks

This paper proposes a bilateral master-slave training systems which allows to directly transfer motion and force skills from the human operator to a real slave manipulator trough a master robot. For this, real and virtual surfaces are modeled by geometric constraints, which represent the surface where the task is to be performed. Thus, joint orthogonal decomposition into force and motion is considered. The holonomic constraint model and the joint orthogonal decomposition allow a stability analysis of the whole system, such that convergence properties in motion and force spaces are obtained. The effectiveness of our proposal is experimentally shown.