Iterative Machine Learning for Precision Trajectory Tracking with Series Elastic Actuators

When robots operate in unknown environments small errors in postions can lead to large variations in the contact forces, especially with typical high-impedance designs. This can potentially damage the surroundings and/or the robot. Series elastic actuators (SEAs) are a popular way to reduce the output impedance of a robotic arm to improve control authority over the force exerted on the environment. However this increased control over forces with lower impedance comes at the cost of lower positioning precision and bandwidth. This article examines the use of an iteratively-learned feedforward command to improve position tracking when using SEAs. Over each iteration, the output responses of the system to the quantized inputs are used to estimate a linearized local system models. These estimated models are obtained using a complex-valued Gaussian Process Regression (cGPR) technique and then, used to generate a new feedforward input command based on the previous iteration's error. This article illustrates this iterative machine learning (IML) technique for a two degree of freedom (2-DOF) robotic arm, and demonstrates successful convergence of the IML approach to reduce the tracking error.Comment: 9 pages, 16 figure. Submitted to AMC Worksho

Digital control of force microscope cantilevers using a field programmable gate array

This report describes a cantilever controller for magnetic resonance force microscopy (MRFM) based on a field programmable gate array (FPGA), along with the hardware and software used to integrate the controller into an experiment. The controller is assembled from a low-cost commercially available software defined radio (SDR) device and libraries of open-source software. The controller includes a digital filter comprising two cascaded second-order sections ("biquads"), which together can implement transfer functions for optimal cantilever controllers. An appendix in this report shows how to calculate filter coefficients for an optimal controller from measured cantilever characteristics. The controller also includes an input multiplexer and adder used in calibration protocols. Filter coefficients and multiplexer settings can be set and adjusted by control software while an experiment is running. The input is sampled at 64 MHz; the sampling frequency in the filters can be divided down under software control to achieve a good match with filter characterisics. Data reported here were sampled at 500 kHz, chosen for acoustic cantilevers with resonant frequencies near 8 kHz. Inputs are digitized with 12 bits resolution, outputs with 14 bits. The experiment software is organized as a client and server to make it easy to adapt the controller to different experiments. The server encapusulates the details of controller hardware organization, connection technology, filter architecture, and number representation. The same server could be used in any experiment, while a different client encodes the particulars of each experiment.Comment: submitted to Review of Scientific Instrument

Practical recipes for the model order reduction, dynamical simulation, and compressive sampling of large-scale open quantum systems

This article presents numerical recipes for simulating high-temperature and non-equilibrium quantum spin systems that are continuously measured and controlled. The notion of a spin system is broadly conceived, in order to encompass macroscopic test masses as the limiting case of large-j spins. The simulation technique has three stages: first the deliberate introduction of noise into the simulation, then the conversion of that noise into an equivalent continuous measurement and control process, and finally, projection of the trajectory onto a state-space manifold having reduced dimensionality and possessing a Kahler potential of multi-linear form. The resulting simulation formalism is used to construct a positive P-representation for the thermal density matrix. Single-spin detection by magnetic resonance force microscopy (MRFM) is simulated, and the data statistics are shown to be those of a random telegraph signal with additive white noise. Larger-scale spin-dust models are simulated, having no spatial symmetry and no spatial ordering; the high-fidelity projection of numerically computed quantum trajectories onto low-dimensionality Kahler state-space manifolds is demonstrated. The reconstruction of quantum trajectories from sparse random projections is demonstrated, the onset of Donoho-Stodden breakdown at the Candes-Tao sparsity limit is observed, a deterministic construction for sampling matrices is given, and methods for quantum state optimization by Dantzig selection are given.Comment: 104 pages, 13 figures, 2 table