Performance-driven control of nano-motion systems

The performance of high-precision mechatronic systems is subject to ever increasing demands regarding speed and accuracy. To meet these demands, new actuator drivers, sensor signal processing and control algorithms have to be derived. The state-of-the-art scientific developments in these research directions can significantly improve the performance of high-precision systems. However, translation of the scientific developments to usable technology is often non-trivial. To improve the performance of high-precision systems and to bridge the gap between science and technology, a performance-driven control approach has been developed. First, the main performance limiting factor (PLF) is identified. Then, a model-based compensation method is developed for the identified PLF. Experimental validation shows the performance improvement and reveals the next PLF to which the same procedure is applied. The compensation method can relate to the actuator driver, the sensor system or the control algorithm. In this thesis, the focus is on nano-motion systems that are driven by piezo actuators and/or use encoder sensors. Nano-motion systems are defined as the class of systems that require velocities ranging from nanometers per second to millimeters per second with a (sub)nanometer resolution. The main PLFs of such systems are the actuator driver, hysteresis, stick-slip effects, repetitive disturbances, coupling between degrees-of-freedom (DOFs), geometric nonlinearities and quantization errors. The developed approach is applied to three illustrative experimental cases that exhibit the above mentioned PLFs. The cases include a nano-motion stage driven by a walking piezo actuator, a metrological AFM and an encoder system. The contributions of this thesis relate to modeling, actuation driver development, control synthesis and encoder sensor signal processing. In particular, dynamic models are derived of the bimorph piezo legs of the walking piezo actuator and of the nano-motion stage with the walking piezo actuator containing the switching actuation principle, stick-slip effects and contact dynamics. Subsequently, a model-based optimization is performed to obtain optimal drive waveforms for a constant stage velocity. Both the walking piezo actuator and the AFM case exhibit repetitive disturbances with a non-constant period-time, for which dedicated repetitive control methods are developed. Furthermore, control algorithms have been developed to cope with the present coupling between and hysteresis in the different axes of the AFM. Finally, sensor signal processing algorithms have been developed to cope with the quantization effects and encoder imperfections in optical incremental encoders. The application of the performance-driven control approach to the different cases shows that the different identified PLFs can be successfully modeled and compensated for. The experiments show that the performance-driven control approach can largely improve the performance of nano-motion systems with piezo actuators and/or encoder sensors

Iterative learning control with wavelet filtering

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Startup of a CD/DVD module under vibration

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Iterative learning control with wavelet filtering

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Alternative Frequency-Domain Stability Criteria for Discrete-Time Networked Systems with Multiple Delays

In this paper alternative frequency-domain criteria areprovided for the stability of discrete-time networkedcontrol systems with time-varying delays. These criteria are in various situations less conservative than the existing frequency-domain conditions as is demonstrated by means of an example. In addition, new stability conditions are presented that allow for multiple sensor-to-controller and controller-to-actuator channels exhibiting different delay characteristics. The stability conditions are formulated in terms of the H_infinity norm and the structured singular value. As a result, the obtainedresults can be used directly for controller synthesis via standard robust control techniques

Iterative learning control with wavelet filtering

The tracking performance of systems that perform repetitive tasks can be significantly improved usingiterative learning control (ILC). During successive iterations, ILC learns a high performance feedforwardsignal from the measured tracking error. In practical applications, the tracking errors of successiveexperiments contain a repetitive part and a non-repetitive part. ILC only compensates for the repetitive part, while the non-repetitive part also enters the learning scheme and deteriorates the performance of ILC. In this paper, analysis of the tracking error of ILC shows the influence of non-repetitive disturbances. The disturbances of the last two iterations appear to have the largest influence on the tracking error. In order to remove the non-repetitive disturbances from the tracking error, a wavelet filtering method is proposed, which identifies and removes the non-repetitive disturbances by a comparison of the time–frequency content of two error realizations for each iteration of ILC. The wavelet filtered error signal contains only the repetitive disturbances and is used as input for ILC. Both simulations and experiments show that with wavelet filtering, a better tracking performance is obtained together with a feedforward signal that containssignificantly less disturbances

Velocity and acceleration estimation for optical incremental encoders

Optical incremental encoders are extensively used for position measurements in motion systems. The position measurements suffer from quantization errors. Velocity and acceleration estimations obtained by numerical differentiation largely amplify the quantization errors. In this paper, the time stamping concept is used to obtain more accurate position, velocity and acceleration estimations. Time stamping makes use of stored events, consisting of the encoder counts and their time instants, captured at a high resolution clock. Encoder imperfections and the limited resolution of the capturing rate of the encoder events result in errors in the estimations. In this paper, we propose a method to extend the observation interval of the stored encoder events using a skip operation. Experiments on a motion system show that the velocity estimation is improved by 54% and the acceleration estimation by 92%