Sliding mode robot control with friction and payload estimation

The paper deals with robust motion control of robotic systems with unknown friction parameters and payload mass. The parameters of the robot arm were considered known with a given precision. To solve the control of the robot with unknown payload mass and friction parameters, sliding mode control algorithm was proposed combined with robust parameter adaptation techniques. Using Lyapunov method it was shown that the resulting controller achieves a guaranteed final tracking accuracy. Simulation results are presented to illustrate the effectiveness and achievable control performance of the proposed scheme

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

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

Peer reviewedPostprin

Robust control of geared and direct-drive robotic manipulators under parameter and model uncertainties

Thesis (M.S.) University of Alaska Fairbanks, 2005The major contribution of this thesis is the design and evaluation of a chattering-free sliding mode controller (SMC), which is a novel application for 2 degree-of-freedom (DOF) planar robot arms exposed to load variations. The performance of the SMC is evaluated in comparison to a proportional-derivative-plus (PD+) controller, as an example of nonlinear model-based controllers, as well as classical linear controllers, such as proportional-derivative (PD) and proportional-integral-derivative (PID). The performance of all four methods has been tested via realistic and detailed simulation models developed for both geared and direct-drive type 2-DOF planar robot arms. The model used in simulations reflects the dynamics of the arm, as well as the actuator dynamics and pulse width modulation (PWM) switching of the power converters. Simulations are performed under unknown load variations for both step and sinusoidal type reference joint trajectories. The results demonstrate that the chattering-free SMC provides increased accuracy and robustness than that of the other controllers and requires no prior knowledge of the system dynamic model and the load variation that the end-effector is subjected to. The results obtained could be extended to the control of a variety of geared and direct-drive type robotic configurations.Introduction -- Modeling of 2-DOF planar elbow manipulator -- Control of 2-DOF planar elbow manipulator -- Simulation results -- Conclusions and future work -- References -- Appendix

Design of Adaptive Sliding Mode Fuzzy Control for Robot Manipulator Based on Extended Kalman Filter

In this work, a new adaptive motion control scheme for robust performance control of robot manipulators is presented. The proposed scheme is designed by combining the fuzzy logic control with the sliding mode control based on extended Kalman filter. Fuzzy logic controllers have been used successfully in many applications and were shown to be superior to the classical controllers for some nonlinear systems. Sliding mode control is a powerful approach for controlling nonlinear and uncertain systems. It is a robust control method and can be applied in the presence of model uncertainties and parameter disturbances, provided that the bounds of these uncertainties and disturbances are known. We have designed a new adaptive Sliding Mode Fuzzy Control (SMFC) method that requires only position measurements. These measurements and the input torques are used in an extended Kalman filter (EKF) to estimate the inertial parameters of the full nonlinear robot model as well as the joint positions and velocities. These estimates are used by the SMFC to generate the input torques. The combination of the EKF and the SMFC is shown to result in a stable adaptive control scheme called trajectory-tracking adaptive robot with extended Kalman (TAREK) method. The theory behind TAREK method provides clear guidelines on the selection of the design parameters for the controller. The proposed controller is applied to a two-link robot manipulator. Computer simulations show the robust performance of the proposed scheme

New dry friction model with load- and velocity- dependence and dynamic identification of multi-dof robots

International audience— Usually, the joint transmission friction model for robots is composed of a viscous friction force and of a constant dry sliding friction force. However, according to the Coulomb law, the dry friction force depends linearly on the load driven by the transmission, which has to be taken into account for robots working with large variation of the payload or inertial and gravity forces. Moreover, for robots actuating at low velocity, the Stribeck effect must be taken into account. This paper proposes a new inverse dynamic identification model for n degrees of freedom (dof) serial robot, where the dry sliding friction force is a linear function of both the dynamic and the external forces, with a velocity-dependent coefficient. A new sequential identification procedure is carried out. At a first step, the friction model parameters are identified for each joint (1 dof), moving one joint at a time (this step has been validated in [23]). At a second step, these values are fixed in the n dof dynamic model for the identification of all robot inertial and gravity parameters. For the two steps, the identification concatenates all the joint data collected while the robot is tracking planned trajectories with different payloads to get a global least squares estimation of inertial and new friction parameters. An experimental validation is carried out with an industrial 3 dof robot