This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

Piezo-actuated stages have become more and more promising in nanopositioning applications due to the excellent advantages of the fast response time, large mechanical force, and extremely fine resolution. Modeling and control are critical to achieve objectives for high-precision motion. However, piezo-actuated stages themselves suffer from the inherent drawbacks produced by the inherent creep and hysteresis nonlinearities and vibration caused by the lightly damped resonant dynamics, which make modeling and control of such systems challenging. To address these challenges, various techniques have been reported in the literature. This paper surveys and discusses the progresses of different modeling and control approaches for piezo-actuated nanopositioning stages and highlights new opportunities for the extended studies.

Modeling and Control of Piezo-Actuated Nanopositioning Stages: A Survey

Recent interest in high-speed scanning probe microscopy for high-throughput applications including video-rate atomic force microscopy and probe-based nanofabrication has sparked attention on the development of high-bandwidth flexure-guided nanopositioning systems (nanopositioners). Such nanopositioners are designed to move samples with sub-nanometer resolution with positioning bandwidth in the kilohertz range. State-of-the-art designs incorporate uniquely designed flexure mechanisms driven by compact and stiff piezoelectric actuators. This paper surveys key advances in mechanical design and control of dynamic effects and nonlinearities, in the context of high-speed nanopositioning. Future challenges and research topics are also discussed.

/pdf/invited-review-article-high-speed-flexure-guided-4qdbjhhogg.pdf

Invited review article: high-speed flexure-guided nanopositioning: mechanical design and control issues.

Lecture notes in control and information sciences

Control of chaos: Methods and applications in engineering

This paper presents a frequency domain identification of dynamic model parameters for frictional presliding behavior. The identification procedure for the dynamic model parameters, i.e., (1) the stiffness and (2) the damping of the presliding phenomenon, is reduced from performing several dedicated experiments to one experiment where the system is excited with random noise and the frequency response function (FRF) of the phenomenon is measured. Time domain validation experiments on a servomechanism show accurate estimates of the dynamic model parameters for the linearized presliding behavior.

Frequency domain identification of dynamic friction model parameters

Brief Friction induced hunting limit cycles: A comparison between the LuGre and switch friction model

http://www.mate.tue.nl/mate/pdfs/10617.pdf

Velocity and acceleration estimation for optical incremental encoders

Piezoelectric actuators are commonly used for micropositioning systems at nanometer resolution. Increasing demands regarding the speed and accuracy are inducing the need for new actuators and new drive principles. A nonresonant piezoelectric actuator is used to drive a stage with 1-DOF through four piezoelectric drive legs. In order to improve the positioning accuracy of the stage, a new drive principle and control strategy for the walking piezomotor are proposed in this paper. The proposed drive principle results in overlapping tip trajectories of the drive legs, resulting in a continuous and smooth drive movement. Gain scheduling feedback in combination with feedforward control further improves the performance of the stage. With the developed drive principle and control strategy, the piezomotor is able to drive the stage at constant velocities between 100 nm/s and 1 mum/s with a tracking error below the encoder resolution of 5 nm. Constant velocities up to 2 mm/s are performed with tracking errors below 400 nm. Point-to-point movements between 5 nm and the complete stroke of the stage are performed with a final static error below the encoder resolution.

http://www.mate.tue.nl/mate/pdfs/9634.pdf

Using a Walking Piezo Actuator to Drive and Control a High-Precision Stage

https://pure.tue.nl/ws/files/3390005/22076505621656.pdf

M.J.G. van de Molengraft

Papers

Frequency domain identification of dynamic friction model parameters

Brief Friction induced hunting limit cycles: A comparison between the LuGre and switch friction model

Velocity and acceleration estimation for optical incremental encoders

Using a Walking Piezo Actuator to Drive and Control a High-Precision Stage

Semantic world modeling using probabilistic multiple hypothesis anchoring