Fault detection for robot manipulators via second order sliding modes

This paper presents a model-based fault detection (FD) and isolation scheme for rigid manipulators. A single fault acting on a specific actuator or on a specific sensor of the manipulator is detected (and, if possible, the exact location of the fault), and an estimation of the fault signal is performed. Input-signal estimator and output observers are considered in order to make the FD procedure possible. By using the suboptimal second-order sliding-mode (SOSM) algorithm to design the input laws of the observers, satisfactory stability properties of the observation error are established. The proposed algorithm is verified in simulation and experimentally on a COMAU SMART3-S2 robot manipulator

Characterization of the Dynamical Model of a Force Sensor for Robot Manipulators

In this work, a planar manipulator in the vertical plane is considered. Starting from the manipulator kinematics, a dynamical model of the sensor and its tip is formulated. Then, identification experiments to estimate the unknown parameters of the sensor and tip dynamical model are designed. The presented identification procedure is oriented to minimize the noise effects on the estimate, by choosing parametrized experiments which are optimized considering a scalar valued information function of the collected data. The model is then used to make the sensor measurements more accurate. Finally, it is analyzed how to obtain the absolute value and the direction of the contact force. Note that by enhancing the quality of the force measurements, the application of robust position controllers provides improved performances

Distributed identification of the Cell Transmission traffic model: A case study

The problem of the distributed identification of a macroscopic first-order traffic model, viz. the Cell Transmission Model (CTM), is considered in the paper. The parameters to be identified characterize the dynamics of the density in different sections of the freeway (cells). We explore different distributed identification schemes. The purposes of the approach are mainly to obtain good prediction models through the minimization of the one-step ahead prediction error of the densities of the cells, and to reduce the computational time and the effort required to perform the identification. The methodology is validated relying on real-life data measured on a portion of the A12 freeway in The Netherlands. An evaluation of the performance of the identified model used as a set of virtual sensors in different scenarios is presented. © 2012 AACC American Automatic Control Council)