Modelling of a Flexible Manoeuvring System Using ANFIS Techniques

The increased utilization of flexible structure systems, such as flexible manipulators and flexible aircraft in various applications, has been motivated by the requirements of industrial automation in recent years. Robust optimal control of flexible structures with active feedback techniques requires accurate models of the base structure, and knowledge of uncertainties of these models. Such information may not be easy to acquire for certain systems. An adaptive Neuro-Fuzzy inference Systems (ANFIS) use the learning ability of neural networks to adjust the membership function parameters in a fuzzy inference system. Hence, modelling using ANFIS is preferred in such applications. This paper discusses modelling of a nonlinear flexible system namely a twin rotor multi-input multi-output system using ANFIS techniques. Pitch and yaw motions are modelled and tested by model validation techniques. The obtained results indicate that ANFIS modelling is powerful to facilitate modelling of complex systems associated with nonlinearity and uncertainty

Modelling And Experimental Vibration Control Of A Two-link Three-dimensional Manipulator With Flexible Links

Current industrial and space manipulators are required to achieve higher speeds in a lighter structure without sacrificing payload capabilities. Consequently, undesirable vibration occurs during the motion. By suitable modelling of the manipulator flexibility, advanced control strategies can be formulated to improve the joint tracking performance and reduce the residual vibration of the end-point in the presence of payload uncertainties.;Toward this goal, an experimental two-link, 3D, anthropomorphic manipulator with flexible links was designed and built to be used as a test bed for the verification and refinement of the proposed modelling and control strategies.;The nonlinear equations of motion for the robot were derived using Lagrangian dynamics. The model was verified using experimental modal analysis techniques. Based on experimental results, a simplified nonlinear model, that contains the relevant modes of the system, was derived and subsequently used in controller designs and state estimation.;A conventional Proportional-plus-Derivative (PD) controller that implements joint angles feedback was designed to be used as a baseline controller due to its wide applicability on industrial manipulators.;By measuring the links tip vibration using accelerometers, several adaptive controllers and state observers were designed and implemented successfully on the manipulator, namely, a gain-scheduling linear quadratic regulator, a model reference adaptive controller, an adaptive inverse dynamics controller, a least-squares nonlinear state estimator and a robust sliding observer. The controllers performance and robustness were tested and experimentally verified against the change of the payload.;The control strategies and identification techniques, developed in this thesis, are applicable to a wide range of robot manipulators including industrial manipulators

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

Peer reviewedPostprin

A robust adaptive robot controller

A globally convergent adaptive control scheme for robot motion control with the following features is proposed. First, the adaptation law possesses enhanced robustness with respect to noisy velocity measurements. Second, the controller does not require the inclusion of high gain loops that may excite the unmodeled dynamics and amplify the noise level. Third, we derive for the unknown parameter design a relationship between compensator gains and closed-loop convergence rates that is independent of the robot task. A simulation example of a two-DOF manipulator featuring some aspects of the control scheme is give

Experimental comparison of parameter estimation methods in adaptive robot control

In the literature on adaptive robot control a large variety of parameter estimation methods have been proposed, ranging from tracking-error-driven gradient methods to combined tracking- and prediction-error-driven least-squares type adaptation methods. This paper presents experimental data from a comparative study between these adaptation methods, performed on a two-degrees-of-freedom robot manipulator. Our results show that the prediction error concept is sensitive to unavoidable model uncertainties. We also demonstrate empirically the fast convergence properties of least-squares adaptation relative to gradient approaches. However, in view of the noise sensitivity of the least-squares method, the marginal performance benefits, and the computational burden, we (cautiously) conclude that the tracking-error driven gradient method is preferred for parameter adaptation in robotic applications

Adaptive control of a manipulator with a flexible link

An adaptive controller for a manipulator with one rigid link and one flexible link is presented. The performance and robustness of the controller are demonstrated by numerical simulation results. In the simulations, the manipulator moves in a gravitational field and a finite element model represents the flexible link

High speed precision motion strategies for lightweight structures

Work during the recording period proceeded along the lines of the proposal, i.e., three aspects of high speed motion planning and control of flexible structures were explored: fine motion control, gross motion planning and control, and automation using light weight arms. In addition, modeling the large manipulator arm to be used in experiments and theory has lead to some contributions in that area. These aspects are reported below. Conference, workshop and journal submissions, and presentations related to this work were seven in number, and are listed. Copies of written papers and abstracts are included

Cooperative Adaptive Control for Cloud-Based Robotics

This paper studies collaboration through the cloud in the context of cooperative adaptive control for robot manipulators. We first consider the case of multiple robots manipulating a common object through synchronous centralized update laws to identify unknown inertial parameters. Through this development, we introduce a notion of Collective Sufficient Richness, wherein parameter convergence can be enabled through teamwork in the group. The introduction of this property and the analysis of stable adaptive controllers that benefit from it constitute the main new contributions of this work. Building on this original example, we then consider decentralized update laws, time-varying network topologies, and the influence of communication delays on this process. Perhaps surprisingly, these nonidealized networked conditions inherit the same benefits of convergence being determined through collective effects for the group. Simple simulations of a planar manipulator identifying an unknown load are provided to illustrate the central idea and benefits of Collective Sufficient Richness.Comment: ICRA 201