On‐off iterative adaptive controller for low‐power micro‐robotic step regulation

Abstract

A novel model-free iterative adaptive controller is presented for lowpower control of piezoelectric actuators. The controller uses simple adaptation rules based on known general behavior of piezoelectric actuators to adjust onoff switching times to drive piezoelectric actuators through a desired transient step motion. Adaptation rules are based on small numbers of measurements taken during each iteration of the actuator movement. Combined with the use of only on-off control inputs, controller implementation can be possible at much lower overall power levels than would be needed to implement a conventional control strategy such as through pulse-width-modulation (PWM) with real-time feedback. Such power savings are particularly important for the intended controller application to piezoelectric microactuators driving autonomous terrestrial micro-robots. A method for predicting convergence of systems with nominally linear dynamics and unknown, bounded nonlinearities is described, and applied to a sample target piezoelectric actuator. The controller is tested in simulation and experimentally on a piezoelectric cantilever actuator, and shows predicted convergence to the desired response.

Sensing parameter selection for ultra-low-power system identification

“…Here, v 1 and v 2 are constants of noise generation and p 1 , p 2 , and p 3 are constants of power consumption dictated by resistors, capacitors, and other circuit components, and V s is the supply voltage of the op-amp. For common commercially available op-amps, e n is roughly inversely proportional to the supply current of the op-amp, I s , as shown in Figure 3 [7].…”

“…These phenomena can be observed in the comparison of actuator and sensor power loads for examples of piezoelectric [1] or electrostatic [2] actuation. Among other possible situations, sensing power often outweighs actuation power when the actuator is a capacitive load with low frequency inputs, but high resolution capacitive position sensing is desired.…”

Sensing Parameter Selection Strategy for Ultra-low-power Micro-servosystem Identification

“…These phenomena can be observed in the comparison of actuator and sensor power loads for examples of piezoelectric [1] or electrostatic [2] actuation. Nonetheless, despite the relatively high cost of sensing, feedback in some form is often necessary because of extremely precise movements may be desired, and because micro-systems themselves may contain significant variation from nominal models due to atmospheric effects, large microfabrication variations, or modeling error.…”

Iterative Learning Control for Nonlinear Systems: A Bounded‐Error Algorithm

“…Iterative learning control (ILC) improves the tracking accuracy of repetitive processes. The ILC is based on the idea that information from the previous trial is used to update the control input in order to obtain better performance of the assigned task on the next trial [1][2][3][4][5][6][7][8][9][10]. Some other definitions of ILC are quoted by Ahn et al in [2].…”