Modeling and Control of Mechanical Systems in Terms of Quasi-Velocities

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Modeling and Balancing of Spherical Pendulum using a Parallel Kinematic Manipulator

The balancing act of an inverted pendulum with a robotic manipulator is a classical benchmark for testing modern control strategies in conjunction with fast sensor-guided movements. From the control design perspective, it presents a challenging and difficult problem as the system is open-loop unstable and includes nonlinear effects in the actuators, such as friction, backlash, and elasticity. In addition, the necessity of a sensor system that can measure the inclination angles of the pendulum contributes to the complexity of the balancing problem. The pendulum is projected onto the xz and yz planes of the inertial coordinate system. These projections are controlled by a state-space controller. A specially developed sensor system allows the contactless measurement of the inclination angles of the pendulum. This system consists of a small magnet, placed at the bottom of the pendulum and Hall-effect sensors placed below the end effector

Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors

Nonlinear dynamical systems have been used in many disciplines to model complex behaviors, including biological motor control, robotics, perception, economics, traffic prediction, and neuroscience. While often the unexpected emergent behavior of nonlinear systems is the focus of investigations, it is of equal importance to create goal-directed behavior (e.g., stable locomotion from a system of coupled oscillators under perceptual guidance). Modeling goal-directed behavior with nonlinear systems is, however, rather difficult due to the parameter sensitivity of these systems, their complex phase transitions in response to subtle parameter changes, and the difficulty of analyzing and predicting their long-term behavior; intuition and time-consuming parameter tuning play a major role. This letter presents and reviews dynamical movement primitives, a line of research for modeling attractor behaviors of autonomous nonlinear dynamical systems with the help of statistical learning techniques. The essence of our approach is to start with a simple dynamical system

Learning Task Constraints from Demonstration for Hybrid Force/Position Control

We present a novel method for learning hybrid force/position control from demonstration. We learn a dynamic constraint frame aligned to the direction of desired force using Cartesian Dynamic Movement Primitives. In contrast to approaches that utilize a fixed constraint frame, our approach easily accommodates tasks with rapidly changing task constraints over time. We activate only one degree of freedom for force control at any given time, ensuring motion is always possible orthogonal to the direction of desired force. Since we utilize demonstrated forces to learn the constraint frame, we are able to compensate for forces not detected by methods that learn only from the demonstrated kinematic motion, such as frictional forces between the end-effector and the contact surface. We additionally propose novel extensions to the Dynamic Movement Primitive (DMP) framework that encourage robust transition from free-space motion to in-contact motion in spite of environment uncertainty. We incorporate force feedback and a dynamically shifting goal to reduce forces applied to the environment and retain stable contact while enabling force control. Our methods exhibit low impact forces on contact and low steady-state tracking error.Comment: Under revie

Dynamic modelling of a 3-CPU parallel robot via screw theory

The article describes the dynamic modelling of I.Ca.Ro., a novel Cartesian parallel robot recently designed and prototyped by the robotics research group of the Polytechnic University of Marche. By means of screw theory and virtual work principle, a computationally efficient model has been built, with the final aim of realising advanced model based controllers. Then a dynamic analysis has been performed in order to point out possible model simplifications that could lead to a more efficient run time implementation