Experimental evaluation of position control methods for hydraulic systems

Presents a unified and systematic assessment of ten position control strategies for a hydraulic servo system with single-ended cylinder driven by a proportional directional control valve. We aim at identifying those methods that achieve better tracking, have a low sensitivity to system uncertainties, and offer a good balance between development effort and end results. A formal approach for solving this problem relies on several practical metrics, which is introduced herein. Their choice is important, as the comparison results between controllers can vary significantly, depending on the selected criterion. Apart from the quantitative assessment, we also raise aspects which are difficult to quantify, but which must stay in attention when considering the position control problem for this class of hydraulic servo systems.

Non-linear position control for hover and automatic landing of unmanned aerial vehicles

This study presents a disturbance attenuation controller for horizontal position stabilisation for hover and automatic landings of a rotary-wing unmanned aerial vehicle (RUAV) operating close to the landing deck in rough seas. Based on a helicopter model representing aerodynamics during the landing phase, a non-linear state feedback H∞ controller is designed to achieve rapid horizontal position tracking in a gusty environment. Practical constraints including flapping dynamics, servo dynamics and time lag effect are considered. A high-fidelity closed-loop simulation using parameters of the Vario XLC gas-turbine helicopter verifies performance of the proposed horizontal position controller. The proposed controller not only increases the disturbance attenuation capability of the RUAV, but also enables rapid position response when gusts occur. Comparative studies show that the H∞ controller exhibits performance improvement and can be applied to ship/RUAV landing systems.

Position control of DC motors with experience mapping based prediction controller

The paper presents a new controller inspired by the human experience based, voluntary body action control (dubbed motor control) learning mechanism. The controller is called Experience Mapping based Prediction Controller (EMPC). EMPC is designed with auto-learning features without the need for the plant model. The core of the controller is formed around the motor action prediction-control mechanism of humans based on past experiential learning with the ability to adapt to environmental changes intelligently. EMPC is utilized for high precision position control of DC motors. The simulation results are presented to show that accurate position control is achieved using EMPC for step and dynamic demands. The performance of EMPC is compared with conventional PD controller and MRAC based position controller under different system conditions. Position Control using EMPC is practically implemented and the results are presented.

Sliding mode position control for real-time control of induction motors

A sliding mode position control for high-performance real-time applications of induction motors in developed in this work. The design also incorporates a simple flux estimator in order to avoid the flux sensors. Then, the proposed control scheme presents a low computational cost and therefore can be implemented easily in a real-time applications using a low cost DSP-processor. The stability analysis of the controller under parameter uncertainties and load disturbances in provided using Lyapunov stability theory. Finally, simulated and experimental results show that the proposed controller with the proposed observer provides a good trajectory tracking and that this scheme is robust with respect to plant parameter variations and external load disturbances.

A robust position control for induction machines

ICEM 2010

Position control of shape memory alloy actuators using self tuning fuzzy PID controller

Shape Memory Alloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications such as aeronautics, surgical tools, robotics and so on. Although the conventional PID controller can be used with slow response systems, there has been limited success in precise motion control of SMA actuators, since the systems are disturbed by unknown factors beside their inherent nonlinear hysteresis and changes in the surrounding environment of the systems. This paper presents a new development of a SMA position control system by using a self-tuning fuzzy PID controller. This control algorithm is used by tuning the parameters of the PID controller thereby integrating fuzzy inference and producing a fuzzy adaptive PID controller, which can then be used to improve the control performance of nonlinear systems. The experimental results of position control of SMA actuators using conventional and self-tuning fuzzy PID controllers are both included in this paper.

Radiation dose reduction in postoperative computed position control of cochlear implant electrodes in lambs - An experimental study

OBJECTIVE Cochlear implants (CI) are standard treatment for prelingually deafened children and postlingually deafened adults. Computed tomography (CT) is the standard method for postoperative imaging of the electrode position. CT scans accurately reflect electrode depth and position, which is essential prior to use. However, routine CT examinations expose patients to radiation, which is especially problematic in children. We examined whether new CT protocols could reduce radiation doses while preserving diagnostic accuracy. METHODS To investigate whether electrode position can be assessed by low-dose CT protocols, a cadaveric lamb model was used because the inner ear morphology is similar to humans. The scans were performed at various volumetric CT dose-indexes CTDIvol)/kV combinations. For each constant CTDIvol the tube voltage was varied (i.e., 80, 100, 120 and 140kV). This procedure was repeated at different CTDIvol values (21mGy, 11mGy, 5.5mGy, 2.8mGy and 1.8mGy). To keep the CTDIvol constant at different tube voltages, the tube current values were adjusted. Independent evaluations of the images were performed by two experienced and blinded neuroradiologists. The criteria diagnostic usefulness, image quality and artifacts (scaled 1-4) were assessed in 14 cochlear-implanted cadaveric lamb heads with variable tube voltages. RESULTS Results showed that the standard CT dose could be substantially reduced without sacrificing diagnostic accuracy of electrode position. The assessment of the CI electrode position was feasible in almost all cases up to a CTDIvol of 2-3mGy. The number of artifacts did not increase for images within this dose range as compared to higher dosages. The extent of the artifacts caused by the implanted metal-containing CI electrode does not depend on the radiation dose and is not perceptibly influenced by changes in the tube voltage. Summarizing the evaluation of the CI electrode position is possible even at a very low radiation dose. CONCLUSIONS CT imaging of the temporal bone for postoperative electrode position control of the CI is possible with a very low and significantly radiation dose. The tube current-time product and voltage can be reduced by 50% without increasing artifacts. Low-dose postoperative CT scans are sufficient for localizing the CI electrode.

A robust adaptive hybrid force/position control scheme of two planar manipulators handling an unknown object interacting with an environment

This paper presents a robust hybrid force/position control scheme of two cooperative manipulators handling an unknown object interacting with an unknown environment. The uncertainty of the object is considered in the weight, length, and the position of centre of mass (COM). The environment is assumed to have an unknown but high stiffness. A hybrid force/position control algorithm is designed for the known system and environment case. The exponential convergence of the position and the interaction force with the environment is proved using the Lyapunov direct method. Similarly, in the unknown object and environment case, and in the presence of bounded disturbances on the robots and the object, an adaptive sliding mode hybrid force/position control scheme is designed. The asymptotic convergence of the object's position and the constraint force is guaranteed using the proposed control methodology. The internal forces and moments between the object and robots are controlled independently of the object's motion and environmental interaction forces. Simulation results confirm the performance and effectiveness of the suggested control methodologies.

Parameter optimization for noise and vibration reduction associated with position and speed sensorless control of permanent magnet machines at low speed

With the advantages and popularity of Permanent Magnet (PM) motors due to their high power density, there is an increasing incentive to use them in variety of applications including electric actuation. These applications have strict noise emission standards. The generation of audible noise and associated vibration modes are characteristics of all electric motors, it is especially problematic in low speed sensorless control rotary actuation applications using high frequency voltage injection technique. This dissertation is aimed at solving the problem of optimizing the sensorless control algorithm for low noise and vibration while achieving at least 12 bit absolute accuracy for speed and position control. The low speed sensorless algorithm is simulated using an improved Phase Variable Model, developed and implemented in a hardware-in-the-loop prototyping environment. Two experimental testbeds were developed and built to test and verify the algorithm in real time.^ A neural network based modeling approach was used to predict the audible noise due to the high frequency injected carrier signal. This model was created based on noise measurements in an especially built chamber. The developed noise model is then integrated into the high frequency based sensorless control scheme so that appropriate tradeoffs and mitigation techniques can be devised. This will improve the position estimation and control performance while keeping the noise below a certain level. Genetic algorithms were used for including the noise optimization parameters into the developed control algorithm.^ A novel wavelet based filtering approach was proposed in this dissertation for the sensorless control algorithm at low speed. This novel filter was capable of extracting the position information at low values of injection voltage where conventional filters fail. This filtering approach can be used in practice to reduce the injected voltage in sensorless control algorithm resulting in significant reduction of noise and vibration.^ Online optimization of sensorless position estimation algorithm was performed to reduce vibration and to improve the position estimation performance. The results obtained are important and represent original contributions that can be helpful in choosing optimal parameters for sensorless control algorithm in many practical applications.^

Visual control of robots : high-performance visual servoing

The application of high-speed machine vision for close-loop position control, or visual servoing, of a robot manipulator. It provides a comprehensive coverage of all aspects of the visual servoing problem: robotics, vision, control, technology and implementation issues. While much of the discussion is quite general the experimental work described is based on the use of a high-speed binary vision system with a monocular "eye-in-hand" camera.

Learning multidimensional joint control of a robot using receding horizon locally weighted regression

In this paper we explore the ability of a recent model-based learning technique Receding Horizon Locally Weighted Regression (RH-LWR) useful for learning temporally dependent systems. In particular this paper investigates the application of RH-LWR to learn control of Multiple-input Multiple-output robot systems. RH-LWR is demonstrated through learning joint velocity and position control of a three Degree of Freedom (DoF) rigid body robot.

Dragline modelling and control

Electric walking draglines are physically large and powerful machines used in the mining industry. However with the addition of suitable sensors and a controller a dragline can be considered as a numerically controlled machine or robot which can then perform parts of the operating cycle automatically. This paper presents an analysis of the electromechanical system necessary precursor to automatic control

Análisis de prestaciones de variador de frecuencia Sinamics para el control de velocidad y posición.

[ES]En este documento se exponen los resultados de las diferentes funcionalidades analizadas del variador de frecuencia Sinamics G120. El dispositivo se ha configurado mediante el software de control TIA Portal para la realización de tareas de control de velocidad de un motor de inducción, así como el control de posición del eje del motor y de un eje lineal acoplado al mismo.

Compliance and Force Control for Computer Controlled Manipulators

Compliant motion occurs when the manipulator position is constrained by the task geometry. Compliant motion may be produced either by a passive mechanical compliance built in to the manipulator, or by an active compliance implemented in the control servo loop. The second method, called force control, is the subject of this report. In particular, this report presents a theory of force control based on formal models of the manipulator, and the task geometry. The ideal effector is used to model the manipulator, and the task geometry is modeled by the ideal surface, which is the locus of all positions accessible to the ideal effector. Models are also defined for the goal trajectory, position control, and force control.