Mansour Karkoub

Robust control of flexible manipulators via μ-synthesis

Abstract An experimental flexible arm serves as testbed to investigate the efficacy of the μ -synthesis design technique in the control of flexible manipulators. A linearized model of the testbed is derived for control design. Discrepancies and errors between the linearized model and the physical system are accounted for in the control design via uncertainty models. These uncertainties include: unmodeled high-frequency dynamics, errors in natural frequencies and damping levels and actuator and sensor errors. Colocated and noncolocated controllers are designed using μ -synthesis. It is observed, theoretically and experimentally, that the μ -synthesis design technique is a viable control tool for tip tracking with flexible manipulators.

Modelling and μ-synthesis control of flexible manipulators

Abstract A dynamical model for a flexible manipulator is derived using the Timoshenko beam theory and the assumed mode method. A control design model is subsequently obtained. The linear model is qualitatively compared to the original model and the discrepancies are quantified in terms of uncertainty weights and included in the control design process. The μ -synthesis control design technique is then used to synthesize robust controllers for the flexible robot arm using different feedback signals. The designed controllers have lead to good tip tracking especially when a sensor is placed at the tip. The derived controllers are robust to unmodelled dynamics, input and actuation uncertainties, and noise.

Predicting axial piston pump performance using neural networks

Abstract A neural network model for an axial piston pump (bent-axis design) is derived in this paper. The model uses data obtained from an experimental setup. The purpose of this ongoing study is the reduction of the power loss at high pressures. However, at the beginning, a study is being done to predict the behavior of the current design of the pump. The neural network model has a feedforward architecture and uses the Levenberg–Marquardt optimization technique in the training process. The model was able to predict the behavior of the pump accurately.

Modelling pressure distribution in a rectangular gas bearing using neural networks

Abstract Gas ubricated bearings are of tremendous use especially in the biomedical and aerospace industries. For that reason, gas bearings have been the subject of much research for the past decade or so. Experimental as well as theoretical work has been done to calculate the pressure distribution inside the bearing. The models available to predict the pressure are primitive and need to be improved. This paper discusses a new modelling scheme known as artificial neural networks. The pressure distribution and the load-carrying capacity are predicted using feedforward architecture of neurons. The inputs to the networks are a collection of experimental data. This data is used to train the network using the Levenberg-Marquardt optimization technique. The results of the neural network model are compared to a theoretical model and the results are promising. The neural network model outperforms the avallable theoretical model in predicting the pressure as well as the load-carrying capacity.

Modelling and control of two robotic manipulators handling a constrained object

Abstract In this paper, the problem of modelling and controlling two manipulators handling a constrained object is addressed. At first, a reduced order dynamic model of the system is derived, and several of its properties are outlined. Using the reduced order model, an adaptive control scheme that guarantees the asymptotic convergence of the position of the object, and the forces acting on the object to their desired values is developed. Simulation results of two planar robots moving an object along a plane illustrate the effectiveness of the proposed control scheme.

Haptic Direct-Drive Robot Control Scheme in Virtual Reality

This paper explores the use of a 2-D (Direct-Drive Arm) manipulator for mechanism design applications based on virtual reality (VR). This article reviews the system include a user interface, a simulator, and a robot control scheme. The user interface is a combination of a virtual clay environment and human arm dynamics via robot effector handler. The model of the VR system is built based on a haptic interface device behavior that enables the operator to feel the actual force feedback from the virtual environment just as s/he would from the real environment. A primary stabilizing controller is used to develop a haptic interface device where realistic simulations of the dynamic interaction forces between a human operator and the simulated virtual object/mechanism are required. The stability and performance of the system are studied and analyzed based on the Nyquist stability criterion. Experiments on cutting virtual clay are used to validate the theoretical developments. It was shown that the experimental and theoretical results are in good agreement and that the designed controller is robust to constrained/unconstrained environment.

Control of the elastodynamic vibrations of a flexible slider–crank mechanism using μ-synthesis

Abstract Suppression of the elastodynamic vibrations of a slider–crank mechanism with a very flexible connecting rod is addressed in this paper. A model for the mechanism is derived using Euler–Lagrange equations and the assumed modes method. The control action uses two feedback signals: the crank angle and the connecting rod coupler midpoint deflection. The actuator is located at the crank ground joint. This arrangement of actuators and sensors has been proven to be very effective. Two control schemes are proposed for the control of the flexible slider–crank mechanism. One scheme is a simple PD control scheme with feedback linearization. The second scheme is based on the μ -synthesis control technique. Both the μ -synthesis control scheme and the PD control scheme yielded good results. However, in terms of robustness, the results achieved via the μ -synthesis control design are better than those achieved with the PD control scheme.

Robust control of two-link flexible manipulators using the μ-synthesis technique

Two-link robot manipulators are commonly used in industrial sectors such as manufacturing. Some manipulators are often bulky and their power consumption is relatively high. Others, such as the arm on the space shuttle, are driven slowly to prevent the onset of flexible oscillations. The efficiency of these manipulators can be improved by reducing the weight of some of these arms and/or increasing the speed of others. These modifications complicate the dynamic behavior of the system due to the possible onset of low frequency oscillations. This article addresses the issue of modeling and end-point robust control of two-link flexible manipulators using the μ-synthesis technique. The Timoshenko beam theory along with the assumed modes method are used to derive reference equations of motion for the flexible manipulator. Discrepancies between the control design model and the actual dynamics of the manipulator are attributed to neglected nonlinearities such as cross-coupling, which should be included in the controller design. A linear estimation of these errors will be identified and used in the control design to compensate for the unmodeled dynamics of the flexible arm and parameter uncertainties. The μ-synthesis control design techniques are then employed to synthesize controllers for the two-link flexible robot manipulator.

A neural network prediction model of fluid displacements in porous media

This paper presents the development and design of an artificial neural network that is able to predict the breakthrough oil recovery of immiscible displacement of oil by water in a two-dimensional vertical cross section. The data used in training the neural network was obtained from the results of fine-mesh numerical simulations. Several network architectures were investigated and trained using the back propagation with momentum algorithm. The neural network that gave the best predictive performance was a two-hidden layer network with 8 neurons in the first hidden layer and 8 neurons in the second hidden layer. This network also performed well against a cross validation test. The reservoir simulation data used so far in the training process was for a homogeneous reservoir, the more general case is still under investigation.

Optimal control of suspension systems using smart actuators

Magnetorheological dampers, which are semi-active devices that use MR fluids to produce controllable forces, can be used as smart actuators to reduce the vibrations of mechanical systems. The advantage of these actuators is the low power input requirements and the high output force they produce. An analytical study is performed to examine the effectiveness of this type of actuators in suppressing the vibrations of a passenger car suspension system. A half-car model including passenger dynamics subjected to road disturbance is used. Two MR dampers attached to the front and back tires are used as actuators; An optimal control scheme is used to control the overall suspension system such that the vibrations of the passenger seats as well as the chassis of the car are greatly reduced or eliminated. The simulation results show that properly controlled MR dampers are effective means for vibration suppression for passenger cars.