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This letter presents and analyzes an adaptive data-driven controller that learns the optimal processing rate in a multi-unit processing network in the presence of disturbances. We formulate an optimization problem of linear cost, linear dynamics for the processing network model and an affine constraint on the dispatcher policy. A data-driven linear equation is constructed, based on which the onlin
We present a method for optimal control with respect to a linear cost function for positive linear systems with coupled input constraints. We show that the Bellman equation giving the optimal cost function and resulting sparse state feedback for these systems can be stated explicitly, with the solution given by a linear program. Our framework admits a range of network routing problems with underly
We study dynamical optimal transport of discrete time systems (dDOT) with Lagrangian cost. The problem is approached by combining optimal control and Kantorovich duality theory. Based on the derived solution, a first order splitting algorithm is proposed for numerical implementation. While solving partial differential equations is often required in the continuous time case, a salient feature of ou
We investigate optimal mass transport problem of affine-nonlinear dynamical systems with input and density constraints. Three algorithms are proposed to tackle this problem, including two Uzawa-type methods and a splitting algorithm based on the Douglas-Rachford algorithm. Some preliminary simulation results are presented to demonstrate the effectiveness of our approaches.
In this paper, we provide the following simple equivalent condition for a nonsymmetric Algebraic Riccati Equation to admit a stabilizing cone-preserving solution: an associated coefficient matrix must be stable. The result holds under the assumption that said matrix be cross-positive on a proper cone, and it both extends and completes a corresponding sufficient condition for nonnegative matrices i
Research efforts to push the integration density of circuits with technologies that transcend Moore's law have gained significant attention in recent years. This study investigates the silicon area gains of Sequential 3D technology, utilizing the third dimension of integrated circuits by accommodating nMOS and pMOS transistors in two stacked tiers with high-density and low-pitch 3D vias. The effic
Network coherence generally refers to the emergence of simple aggregated dynamical behaviors, despite heterogeneity in the dynamics of the subsystems that constitute the network. In this paper, we develop a general frequency domain framework to analyze and quantify the level of network coherence that a system exhibits by relating coherence with a low-rank property of the system's input–output resp
This paper presents a reconfigurable radio frequency front-end (RFFE) tailored for direct RF sampling receivers operating within Frequency Range 1 (FR-1) of the 5G spectrum. It consists of a balun-LNA, a noise-cancelling, current-reuse, Q-enhanced filter, and a programmable gain amplifier (PGA). Fabricated in 22-nm FD-SOI technology, the RFFE covers the entire frequency range from 1.7 to 6.4 GHz w
The need for autonomous robot systems in both the service and the industrial domain is larger than ever. In the latter, the transition to small batches or even “batch size 1” in production created a need for robot control system architectures that can provide the required flexibility. Such architectures must not only have a sufficient knowledge integration framework. It must also support autonomou
With the growing demand for efficient, safe and sustainable transportation systems, the imperative to design intelligent routing and traffic management solutions within urban settings, requiring minimal data exchange and ensuring scalability, becomes evident. This paper introduces an innovative paradigm for traffic management. By seamlessly integrating machine learning and Autonomous Intersection
We present an online learning analysis of minimax adaptive control for the case where the uncertainty includes a finite set of linear dynamical systems. Precisely, for each system inside the uncertainty set, we define the model-based regret by comparing the state and input trajectories from the minimax adaptive controller against that of an optimal controller in hindsight that knows the true dynam
Control of network systems with uncertain local dynamics has remained an open problem for a long time. In this paper, a distributed minimax adaptive control algorithm is proposed for such networks whose local dynamics has an uncertain parameter possibly taking finite number of values. To hedge against this uncertainty, each node in the network collects the historical data of its neighbouring nodes
This work concerns reduction of the peak flow rate of a district heating grid,a key system property which is bounded by pipe dimensions and pumpingcapacity. The peak flow rate constrains the number of additional consumersthat can be connected, and may be a limiting factor in reducing supplytemperatures when transitioning to the 4th generation of district heating.We evaluate a full year of operatio
We present a tool for modeling conflict situations that enables simulation and testing of situation awareness in shared autonomy, in this case in an autonomous driving scenario. The flexibility of the tool allows definition of new conflict situations, integration with various control and conflict detection systems, as well as customization of Takeover Request (TOR) signals and different means of c
Traditional control systems face challenges in managing high data loads and computing power, prompting the evolution of Cloud Control Systems (CCS)-a fusion of Networked Control Systems (NCS) and cloud computing. Despite offering manifold advantages, CCS encounters hurdles in navigating the dynamic cloud environment characterized by fluctuating workloads, rendering static frequency settings ineffi
This paper examines the local exponential stability (LES) of trajectories for nonlinear systems on Riemannian manifolds. We present necessary and sufficient conditions for LES of a trajectory on a Riemannian manifold by analyzing the complete lift of the system along the given trajectory. These conditions are coordinate-free which reveal fundamental relationships between exponential stability and
Using hydraulic models in control design in district heating networks can increase pumping efficiency and reduce sensitivity to hydraulic bottlenecks. These models are usually white-box, as they are obtained based on full knowledge of the district heating network and its parameters. This type of model is time-consuming to obtain, and might differ from the actual behavior of the system. In this pap
The ability to learn new tasks and quickly adapt to different variations or dimensions is an important attribute in agile robotics. In our previous work, we have explored Behavior Trees and Motion Generators (BTMGs) as a robot arm policy representation to facilitate the learning and execution of assembly tasks. The current implementation of the BTMGs for a specific task may not be robust to the ch
