<p>All mechanical systems with moving parts are affected by friction, including industrial robots. Being able to design an accurate friction model would further increase the performance of todays robots. Friction is a complex dynamic phenomena that is constantly changing depending on the state and environment of the robot. It is therefore beneficial to update the parameters of the friction model online. An estimate of the friction will be made using the feedback control signal with the help of a feedforward control scheme in a two axis simulation setup. The friction estimate is then used for an offline identification of three friction model parameters in a static Lugre friction model. Improvements on the identification will be done by introducing some shut-off rules that will improve the estimate. The normalized least mean square method (NLMS) will then be used to update the parameters online. A simulation of friction compensation with a fixed friction model, and with an adaptive friction model will be studied. The method will also be simulated using experimental data taken from a real industrial robot.</p>

A new inverse analysis method is presented to identify parameters of heat convection in microelectronic packages. This approach adopts a modified Micro Genetic Algorithm (µGA) in finding the global optimum of parameters. A reduced-basis approach is introduced in the forward heat transfer analysis so as to significantly improve the efficiency in the calculation. Different identification procedures are employed to identify heat convection coefficients of a typical microelectronic package. Comparisons between different algorithms are performed. Results show that the use of the reduced-basis method together with the modified µGA outperforms the conventional GAs significantly. The presented method of coefficient identification is ideal for practical applications. It is efficient enough even for online analysis of both forward and inverse problem. / Singapore-MIT Alliance (SMA)

The simplest form of induction motors, known as AC squirrel cage motor, is the universal workhorse of industrial and commercial premises. For many years it was restricted to constant speed applications while DC motors were preferred for high-performance variable speed and servo drives. With modern advances in semiconductor and digital signal processing technologies, it is now possible to operate induction motors in high-performance drives at a reasonable cost with Field Oriented Control methods. The latter have made induction motor drives equivalent to DC drives in terms of independent control of flux and torque; and superior to them in terms of dynamic performance. In developing Field Oriented Control for induction motors engineers are faced with two major challenges: (1) the estimation of rotor data to compute for the slip gain, and (2) the compensation of changes in drive operating conditions and parameters in order to maintain the drive performance high at all time. This thesis addresses these issues by introducing two independent control systems. The first system is designed to estimate online the value of the slip gain in the entire torque-speed plane in order to maintain decoupled control of torque and flux despite the so-called detuning effects. It is based on evaluating the operating condition of the drive in terms frequency and load torque, and selecting the appropriate estimation method accordingly. A fuzzy controller is used to generate the distribution factor for the methods. The second system is a fuzzy self-tuning speed controller, with reduced sensitivity to motor parameters and operating condition changes. It has the ability to adjust its gains in real time according to the current trend of the drive system. It is designed to maintain tight control of speed and torque for high-performance applications. The performances of the two controllers are validated through a series of simulation and experimental tests using a 2HP 3-phase induction motor with an ADMC21992 160-MHz DSP microprocessor. / Power Engineering and Power Electronics

No description available.

The field of inverse problems is an area in applied mathematics that is of great importance in several scientific and industrial applications. Since an inverse problem is typically founded on non-linear and ill-posed models it is a very difficult problem to solve. To find a regularized solution it is crucial to have a priori information about the solution. Therefore, general theories are not sufficient considering new applications. In this thesis we consider the inverse problem to determine the beam bending stiffness from measurements of the transverse dynamic displacement. Of special interest is to localize parts with reduced bending stiffness. Driven by requirements in the wood-industry it is not enough considering time-efficient algorithms, the models must also be adapted to manage extremely short calculation times. For the developing of efficient methods inverse problems based on the fourth order Euler-Bernoulli beam equation and the second order string equation are studied. Important results are the transformation of a nonlinear regularization problem to a linear one and a convex procedure for finding parts with reduced bending stiffness.

No description available.

All mechanical systems with moving parts are affected by friction, including industrial robots. Being able to design an accurate friction model would further increase the performance of todays robots. Friction is a complex dynamic phenomena that is constantly changing depending on the state and environment of the robot. It is therefore beneficial to update the parameters of the friction model online. An estimate of the friction will be made using the feedback control signal with the help of a feedforward control scheme in a two axis simulation setup. The friction estimate is then used for an offline identification of three friction model parameters in a static Lugre friction model. Improvements on the identification will be done by introducing some shut-off rules that will improve the estimate. The normalized least mean square method (NLMS) will then be used to update the parameters online. A simulation of friction compensation with a fixed friction model, and with an adaptive friction model will be studied. The method will also be simulated using experimental data taken from a real industrial robot.

Parameter identification of an induction machine is of great importance in numerous industrial applications, including the assessment of machine performance and design of control schemes. Parameter identification is based on the input-output signals and the model used. Many researches have applied the inverter drive to control the exciting signal of the induction machine in the identifying process. This study proposed a method to identify all parameter of the induction machine with a no-load low-voltage starting test. The method has a simple structure without needing extra hardware, which could significantly simplify the procedures and save cost. Based on the curves of resistance and reactance, the user can obtain the machine¡¦s equivalent circuit parameters. With the identified parameters of the equivalent circuit, input voltage, and rotor speed, the user can find the torque. From the torque and rotor speed, the user can find the mechanical parameters. A least mean square (LMS) method was used with a particle swarm optimization (PSO) method to solve the aforementioned problem. From various tests, the practicability and accuracy of this method can been proven. This study also proposes a method to rapidly analyze power parameters. This method uses two adjacent data to compute the fundamental frequency component of voltage or current. The parameters of fundamental frequency component include frequency, amplitude, and phase. Under the condition of varied parameters, the frequency and phase are dependent. This method fixes the frequency and computes the amplitude and phase, and then stable results will be obtained.

Metal mesh foil bearings (MMFBs) are a promising low cost gas bearing technology for support of high speed oil-free microturbomachinery. Elimination of complex oil lubrication and sealing system by installing MMFBs in oil free rotating machinery offer distinctive advantages such as reduced system overall weight, enhanced reliability at high rotational speeds and extreme temperatures, and extended maintenance intervals compared to conventional turbo machines. MMFBs for oil-free turbomachinery must demonstrate adequate load capacity, reliable rotordynamic performance, and low frictional losses in a high temperature environment. The thesis presents the measurements of MMFB break-away torque, rotor lift off and touchdown speeds, temperature at increasing static load conditions, and identified stiffness and equivalent viscous damping coefficients. The experiments, conducted in a test rig driven by an automotive turbocharger turbine, demonstrate the airborne operation (hydrodynamic gas film) of the floating test MMFB with little frictional loses at increasing loads. The measured drag torque peaks when the rotor starts and stops, and drops significantly once the bearing is airborne. The estimated rotor speed for lift-off increases linearly with increasing applied loads. During continuous operation, the MMFB temperature measured at one end of the back surface of the top foil increases both with rotor speed and static load. Nonetheless, the temperature rise is only nominal ensuring reliable bearing performance. Application of a sacrificial layer of solid lubricant on the top foil surface aids to reduce the rotor break-away torque. The measurements give confidence on this simple bearing technology for ready application into oil-free turbomachinery. Impact loads delivered (with a soft tip) to the test bearing, while resting on the (stationary) drive shaft, evidence a system with large damping and a structural stiffness that increases with frequency (max. 200 Hz). The system equivalent viscous damping ratio decreases from ~ 0.7 to 0.2 as the frequency increases. In general, the viscous damping in a metal mesh structure is of structural type and inversely proportional to the frequency and amplitude of bearing motion relative to the shaft. Impact load tests, conducted while the shaft rotates at 50 krpm, show that the bearing direct stiffness is lower (~25% at 200 Hz) than the bearing structural stiffness identified from impact load tests without shaft rotation. However, the identified equivalent viscous damping coefficients from tests with and without shaft rotation are nearly identical. The orbits of bearing motion relative to the rotating shaft show subsynchronous motion amplitudes and also backward synchronous whirl. The subsynchronous vibration amplitudes are locked at a frequency, nearly identical to a rotor natural frequency. A backward synchronous whirl occurs while the rotor speed is between any two natural frequencies, arising due to bearing stiffness asymmetry.

This study focuses on kinematic calibration of industrial robots. Kinematic modeling, parameter identification and optimal pose selection methods are presented. A computer simulation of the kinematic calibration is performed using generated measurements with normally distributed noise. Furthermore, kinematic calibration experiments are performed on an ABB IRB 6600 industrial robot using full pose measurements taken by a laser tracking system. The kinematic model of the robot is developed using the modified Denavit - Hartenberg convention. A nonlinear least-squares method is employed during the parameter identification stage. According to the experiment results, the initial robot positioning errors are reduced by more than 80%.