Position-dependent Load Research Articles

Online tracking of dynamically time-varying inertia using an enhanced SDFT-based estimation methodology

The overall motion performance of mechatronic machines depends heavily on proper knowledge of the machine’s characteristics, among which the inertia. Moreover, the inertia of most multi-body mechanisms, for example, reciprocating slider-crank mechanisms, varies as a function of the machine position, thereby challenging optimal control. Nevertheless, the inertia’s variation is sometimes not known accurately enough or changes over time due to, e.g. a production process or premature wear, forcing an online control/correction of the inertia profile. Therefore, earlier, the author developed a new computational-friendly online method based on a frequency-specific magnitude response to estimate the machine’s position-dependent load side inertia, assuming good knowledge of all other machine properties. Yet, to guarantee accuracy, the maximum machine speed during estimation had to be strongly reduced, resulting in an undesirable longer motion time. Therefore, in this work, the failure mechanism of the earlier proposed estimator is analysed and overcome by intelligently separating estimation and motion signals. As a result, the estimator is no longer limited by the machine’s motion speed but rather by a maximum in the inertia variation per signal excitation period and, thus, time. Moreover, given this limiting variation, a guideline was established to evaluate the new method’s estimation quality. Finally, both in simulation and on a physical machine, by taking advantage of the newly proposed method, the minimal required estimation time is reduced significantly (up to 50% on the machine) without diminishing the estimation accuracy.

Product Flexible Car Body Fixtures with Position-dependent Load Balancing Based on Finite Element Method in Combination with Methods of Artificial Intelligence

In car-body production, the fixtures used for fixing the car-body panels during the joining processes predominantly consist of rigid constructions that are precise and specific for one car-model. In addition to the already presented automatic adaptation of such fixtures, and specific for the production of many different car-models using same fixture, different clamping robots have been developed. In order to meet the high quality requirements, the positional deviations of these clamping robots are adjusted due to different external loads. This control loop is carried out using a combination of finite element method, mathematical algorithms and modern methods of artificial intelligence.

Repetitive learning position control for full order model permanent magnet step motors

We provide a novel theoretical solution to the yet unsolved problem of tracking, via state feedback, periodic reference signals (with known period) for the rotor position of full order model uncertain permanent magnet step motors with non-sinusoidal flux distribution and uncertain position-dependent load torque. The resulting control is of simple structure and incorporates three repetitive learning estimation schemes generalizing the classical integral actions. Realistic simulation results illustrate the effectiveness of the proposed approach.

Robust adaptive learning control for nonlinear systems with extended matching unstructured uncertainties

SUMMARYThe exponential output tracking problem for a class of single‐input, single‐output uncertain nonlinear systems, including systems with extended matching unstructured uncertainties and without a well‐defined global relative degree, is addressed. Conditions on the uncertain system dynamics are derived, which allow us to design a state‐feedback learning control achieving semi‐global exponential output tracking of sufficiently smooth and periodic reference signals of known period, while guaranteeing ℒ︁2 and ℒ︁∞ transient performances during the learning phase. The application of the proposed learning approach to the position tracking control problem for uncertain permanent magnet step motors with non‐sinusoidal flux distribution and uncertain position‐dependent load torque allows us to provide a solution to a yet unsolved problem. Copyright © 2011 John Wiley & Sons, Ltd.

A global robust iterative learning position control for current-fed permanent magnet step motors

The tracking control problem via state feedback for uncertain current-fed permanent magnet step motors with non-sinusoidal flux distribution and uncertain position-dependent load torque is addressed: a periodic reference signal (of known period) for the rotor position is required to be tracked. A robust iterative learning control algorithm is designed which, for any motor initial condition and without requiring any resetting procedure, guarantees, despite system uncertainties: exponential convergence of the rotor position tracking error to a residual ball (centered at the origin) whose radius can be made arbitrarily small by properly setting the learning gain; asymptotic convergence of the rotor position tracking error to zero. A sufficient condition for the asymptotic estimation of the uncertain reference input achieving, for compatible initial conditions, perfect tracking is derived. Robustness with respect to a finite memory implementation of the control algorithm based on the piecewise linear approximation theory is shown to be guaranteed; satisfactory performances of a discrete-time implementation of the control algorithm are obtained in realistic simulations for the full-order voltage-fed motor.

Low-speed performance comparisons of back-EMF detection circuits with position-dependent load torque

Because of the widely used brushless DC motors (BDCMs) in high-efficiency variable-speed compressors, many sensorless controls with square-wave operation had been developed in the literatures. Recently, more and more low-production-cost BDCM-type compressors with position-dependent load torque, such as rotary compressors, are applied to the residential products, which lead to the importance of low-speed performance of sensorless control. The authors address the low-speed performance comparisons between two common back-EMF detecting circuits (BEDCs). Without using the actual refrigerant system, a Motor-Generator set coupled with a disk and copper cylinder are also built up to serve as the experimental position-dependent load torque. From the simulated and experimental results, one of the BEDCs is better than the other one for the low-speed application of position-dependent load torque.

Threshold control of arm posture and movement adaptation to load

We addressed the fundamental questions of which variables underlie the control of arm movement and how they are stored in motor memory, reproduced and modified in the process of adaptation to changing load conditions. Such variables are defined differently in two major theories of motor control (internal models and threshold control). To resolve the controversy, these theories were tested (experiment 1) based on their ability to explain why active movement away from a stable posture is not opposed by stabilizing mechanisms (the posture-movement problem). The internal model theory suggests that the system counteracts the opposing forces by increasing the muscle activity in proportion to the distance from the initial posture (position-dependent EMG control). In contrast, threshold control fully excludes these opposing forces by shifting muscle activation thresholds and thus resetting the stabilizing mechanisms to a new posture. Subjects were sitting, holding the vertical handle of a double-joint manipulandum with their right hand and were facing a computer screen on which the handle and target to be reached were displayed. In response to an auditory signal, subjects quickly moved the handle from an initial position to one of two (frontal and sagittal) targets. No load was applied during the movement but in separate trials, a brief perturbation was applied to the handle by torque motors controlling the manipulandum. Perturbations were applied prior to or 3 s after movement offset, in the latter case in one of eight directions. The EMG activity of the majority of the seven recorded muscles was at zero level before movement onset and returned to zero level after movement offset. Those muscles that remained active before or after the movement could be made silent whereas previously silent muscles could be activated after a small passive displacement (several millimeters) elicited by perturbations in appropriate directions. Results showed that the activation thresholds of motoneurons of arm muscles were reset from the initial to a final position and that EMG activity was not position-dependent. These results were inconsistent with the internal model theory but confirmed the threshold control theory. Then the ability of threshold control theory to explain rapid movement adaptation to a position-dependent load was investigated (experiment 2 and 3). Subjects produced fast movement to the frontal target with and without a position-dependent load applied to the handle. Trials were organized in blocks alternating between the load and no-load condition (20 blocks in total, with randomly chosen number of five to ten trials in each). Subjects were instructed "do not correct" in experiment 2 and "correct" movement errors during the trial in experiment 3. Five threshold arm configurations underlying the movement production and adaptation were identified. When instructed "do not correct", movement precision was fully restored on average after two trials. No significant improvement was observed as the experiment progressed despite the fact that the same load condition was repeated after one block of trials. Thus, in each block, the adaptation was made anew, implying that subjects relied on short-term memory and could not recall the threshold arm configurations they specified to accurately reach the same target in the same load condition in previous blocks. When instructed to "correct" within each trial, precision was restored faster, on average after one trial. Major aspects of the production and adaptation of arm movement (including the kinematics, movement errors, instruction-dependent behavior, and absence of position-related EMG activity) are explained in terms of threshold control.