2,652 research outputs found
Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Online Hybrid Model Predictive Control
Today's fast linear algebra and numerical optimization tools have pushed the
frontier of model predictive control (MPC) forward, to the efficient control of
highly nonlinear and hybrid systems. The field of hybrid MPC has demonstrated
that exact optimal control law can be computed, e.g., by mixed-integer
programming (MIP) under piecewise-affine (PWA) system models. Despite the
elegant theory, online solving hybrid MPC is still out of reach for many
applications. We aim to speed up MIP by combining geometric insights from
hybrid MPC, a simple-yet-effective learning algorithm, and MIP warm start
techniques. Following a line of work in approximate explicit MPC, the proposed
learning-control algorithm, LNMS, gains computational advantage over MIP at
little cost and is straightforward for practitioners to implement
Deep Reinforcement Learning for Event-Triggered Control
Event-triggered control (ETC) methods can achieve high-performance control
with a significantly lower number of samples compared to usual, time-triggered
methods. These frameworks are often based on a mathematical model of the system
and specific designs of controller and event trigger. In this paper, we show
how deep reinforcement learning (DRL) algorithms can be leveraged to
simultaneously learn control and communication behavior from scratch, and
present a DRL approach that is particularly suitable for ETC. To our knowledge,
this is the first work to apply DRL to ETC. We validate the approach on
multiple control tasks and compare it to model-based event-triggering
frameworks. In particular, we demonstrate that it can, other than many
model-based ETC designs, be straightforwardly applied to nonlinear systems
A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming
This work presents the concept of kernel mean embedding and kernel
probabilistic programming in the context of stochastic systems. We propose
formulations to represent, compare, and propagate uncertainties for fairly
general stochastic dynamics in a distribution-free manner. The new tools enjoy
sound theory rooted in functional analysis and wide applicability as
demonstrated in distinct numerical examples. The implication of this new
concept is a new mode of thinking about the statistical nature of uncertainty
in dynamical systems
Nonlinear Wasserstein distributionally robust optimal control
This paper presents a novel approach to addressing the distributionally robust nonlinear model predictive control (DRNMPC) problem. Current literature primarily focuses on the static Wasserstein distributionally robust optimal control problem with a prespecified ambiguity set of uncertain system states. Although a few studies have tackled the dynamic setting, a practical algorithm remains elusive. To bridge this gap, we introduce an DRNMPC scheme that dynamically controls the propagation of ambiguity, based on the constrained iterative linear quadratic regulator. The theoretical results are also provided to characterize the stochastic error reachable sets under ambiguity. We evaluate the effectiveness of our proposed iterative DRMPC algorithm by comparing the closed-loop performance of feedback and open-loop on a mass-spring system. Finally, we demonstrate in numerical experiments that our algorithm controls the propagated Wasserstein ambiguity
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control
We apply kernel mean embedding methods to sample-based stochastic
optimization and control. Specifically, we use the reduced-set expansion method
as a way to discard sampled scenarios. The effect of such constraint removal is
improved optimality and decreased conservativeness. This is achieved by solving
a distributional-distance-regularized optimization problem. We demonstrated
this optimization formulation is well-motivated in theory, computationally
tractable and effective in numerical algorithms
Propagating Kernel ambiguity sets in nonlinear data-driven dynamics models
This paper provides answers to an open problem: given a nonlinear data-driven dynamical system model, e.g., kernel conditional mean embedding (CME) and Koopman operator, how can one propagate the ambiguity sets forward for multiple steps? This problem is the key to solving distributionally robust control and learning-based control of such learned system models under a data-distribution shift. Different from previous works that use either static ambiguity sets, e.g., fixed Wasserstein balls, or dynamic ambiguity sets under known piece-wise linear (or affine) dynamics, we propose an algorithm that exactly propagates ambiguity sets through nonlinear data-driven models using the Koopman operator and CME, via the kernel maximum mean discrepancy geometry. Through both theoretical and numerical analysis, we show that our kernel ambiguity sets are the natural geometric structure for the learned data-driven dynamical system models
Augmented Reality Meets Tangibility: A New Approach for Early Childhood Education
Augmented Reality (AR) has been recognised as one of the promising technologies for the gaming industry. In this study, the authors intend to apply AR technology to develop an interactive educational game. This paper presents an AR featured educational game specifically designed for 4-7 years old pre-school children. The principal objective of this game is to enable children to learn various abstract concepts, such as colour mixing, mathematics and 2D-3D geometrical shape recognition. This game allows users to interact with both onscreen (intangible) and physical objects (tangible) at the same time; different interaction forms including the touch screen (click) and AR game (rotate) are designed for better interaction with the real world and learning. This paper focuses on the details of the design and interactive behaviour. Furthermore, beyond the needs of children, this game also serves for parents through the Token Economy method; parents can control the kids’ contacting time with portable devices, and track and modify their everyday learning patterns. A pilot study implementing mix method was used to gather user’s feedback is also described in this paper
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