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In this paper we propose a fast optimization algorithm for approximately minimizing convex quadratic functions over the intersection of affine and separable constraints (i.e., the Cartesian product of possibly nonconvex real sets). This problem class contains many NP-hard problems such as mixed-integer quadratic programming. Our heuristic is based on a… (More)

- Nicholas Moehle, Stephen P. Boyd
- Int. J. Control
- 2015

In this paper we give energy-optimal excitation current waveforms for a permanent magnet synchronous motor that result in a desired average torque. Our formulation generalizes previous work by including a general back-EMF waveform, voltage and current limits, an arbitrary phase winding connection, a simple eddy current loss model, and a trade-off between… (More)

- Nicholas Moehle, Stephen P. Boyd
- 2016 IEEE Conference on Control Applications (CCA…
- 2016

In this paper, we address the problem of finding current waveforms for a switched reluctance motor that minimize a user-defined combination of torque ripple and RMS current. The motor model we use is fairly general, and includes magnetic saturation, voltage and current limits, and highly coupled magnetics (and therefore, unconventional geometries and… (More)

- Nicholas Moehle, Stephen P. Boyd
- Systems & Control Letters
- 2015

We consider the switched-affine optimal control problem, i.e., the problem of selecting a sequence of affine dynamics from a finite set in order to minimize a sum of convex functions of the system state. We develop a new reduction of this problem to a mixed-integer convex program (MICP), based on perspective functions. Relaxing the integer constraints of… (More)

- Nicholas Moehle, Stephen P. Boyd
- CDC
- 2016

—We present a method for finding current waveforms for induction motors that minimize resistive loss while achieving a desired average torque output. Our method is not based on reference-frame theory for electric machines, and therefore directly handles induction motors with asymmetric winding patterns, nonsinusoidally distributed windings, and a general… (More)

- Nicholas Moehle, Xinyue Shen, Zhi-Quan Luo, Stephen P. Boyd
- ArXiv
- 2017

We consider the problem of reserving link capacity in a network in such a way that any of a given set of flow scenarios can be supported. In the optimal capacity reservation problem, we choose the reserved link capacities to minimize the reservation cost. This problem reduces to a large linear program, with the number of variables and constraints on the… (More)

— This paper considers estimation of covariance matrices in multivariate linear regression models for two-level data produced by a population of similar units (individuals). The proposed Bayesian formulation assumes that the covariances for different units are sampled from a common distribution. Assuming that this common distribution is Wishart, the optimal… (More)

- Matt Wytock, Nicholas Moehle, Stephen P. Boyd
- ACC
- 2017

We present a simple, practical method for managing the energy produced and consumed by a network of devices. Our method is based on (convex) model predictive control. We handle uncertainty using a robust model predictive control formulation that considers a finite number of possible scenarios. A key attribute of our formulation is the encapsulation of… (More)

- N Moehle
- 2016

This is a 24 hour take-home final. Please turn it in at Bytes Cafe in the Packard building, 24 hours after you pick it up. You may use any books, notes, or computer programs, but you may not discuss the exam with anyone until August 16, after everyone has taken the exam. The only exception is that you can ask us for clarification, via the course staff email… (More)

- Nicholas Moehle
- ArXiv
- 2016

We consider the problem of controlling switched-mode power converters using model predictive control. Model predictive control requires solving optimization problems in real time, limiting its application to systems with small numbers of switches and a short horizon. We propose a technique for using off-line computation to approximate the model predictive… (More)

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