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Recurrent Neural Network Training with Convex Loss and Regularization Functions by Extended Kalman Filtering

This article investigates the use of extended Kalman filtering to train recurrent neural networks with rather general convex loss functions and regularization terms on the network parameters, including ℓ1-regularization. We show that the learning method is competitive with respect to stochastic gradient descent in a nonlinear system identification benchmark and in training a linear system with binary outputs. We also explore the use of the algorithm in data-driven nonlinear model predictive c...

Efficiently Solving the Harmonic Model Predictive Control Formulation

Harmonic model predictive control (HMPC) is a model predictive control (MPC) formulation that displays several benefits over other MPC formulations, especially when using a small prediction horizon. These benefits, however, come at the expense of an optimization problem that is no longer the typical quadratic programming problem derived from most linear MPC formulations due to the inclusion of a particular class of second-order cone constraints. This article presents a method for efficiently ...

Variance-Based Exploration for Learning Model Predictive Control

The combination of model predictive control (MPC) and learning methods has been gaining increasing attention as a tool to control systems that may be difficult to model. Using MPC as a function approximator in reinforcement learning (RL) is one approach to reduce the reliance on accurate models. RL is dependent on exploration to learn, and currently, simple heuristics based on random perturbations are most common. This paper considers variance-based exploration in RL geared towards using MPC ...

A Simple and Fast Coordinate-Descent Augmented-Lagrangian Solver for Model Predictive Control

This article proposes a novel coordinate-descent augmented-Lagrangian (CDAL) solver for linear, possibly parameter-varying, model predictive control (MPC) problems. At each iteration, an augmented Lagrangian (AL) subproblem is solved by coordinate descent (CD), exploiting the structure of the MPC problem. The CDAL solver enjoys three main properties: 1) it is construction-free, in that it avoids explicitly constructing the quadratic programming problem associated with MPC; 2) is matrix-free, ...

Active learning for regression by inverse distance weighting

This paper proposes an active learning (AL) algorithm to solve regression problems based on inverse-distance weighting functions for selecting the feature vectors to query. The algorithm has the following features: (i) supports both pool-based and population-based sampling; (ii) is not tailored to a particular class of predictors; (iii) can handle known and unknown constraints on the queryable feature vectors; and (iv) can run either sequentially, or in batch mode, depending on how often the ...

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