This paper presents a computationally efficient approach to designing Model Predictive Control (MPC) for dynamic positioning systems of marine vessels. In this approach, the MPC is approximated by a neural network equipped with a constraint-satisfaction strategy on the control actions to mitigate approximation errors. A capability factor is introduced during the training process to adjust the bounds of the control actions, ensuring that the neural network generates practically feasible solutions. Implementations of the proposed control scheme are computationally efficient on ships and effectively prevent actuator saturation in the presence of model mismatches and external disturbances. The usefulness and expected performance of this method is shown with a simulation example.

This paper addresses the challenge of actuator delays in industrial process control, focusing on scenarios where these delays are unknown but bounded. Unlike previous studies that assume constant and known delays, this work targets more realistic conditions with uncertain delays. We propose a novel method for training Reinforcement Learning (RL) agents that guarantees stability without requiring prior knowledge of delay values. To validate our approach, we apply it to a standard process control scenario: level regulation in a nonlinear spherical tank. This scenario is particularly challenging due to the impact of actuator delays on stability, especially when the tank's level set-point is near its upper or lower limits. Our results show that the proposed Rl method is robust in dynamic environments with uncertain delays, providing a promising solution for improving reliability and efficiency in industrial process control.

We introduce novel convergence results for asynchronous iterations that appear in the analysis of parallel and distributed optimization algorithms. The results are simple to apply and give explicit estimates for how the degree of asynchrony impacts the convergence rates of the iterates. Our results shorten, streamline and strengthen existing convergence proofs for several asynchronous optimization methods and allow us to establish convergence guarantees for popular algorithms that were thus far lacking a complete theoretical understanding. Specifically, we use our results to derive better iteration complexity bounds for proximal incremental aggregated gradient methods, to obtain tighter guarantees depending on the average rather than maximum delay for the asynchronous stochastic gradient descent method, to provide less conservative analyses of the speedup conditions for asynchronous block-coordinate implementations of Krasnoselskii–Mann iterations, and to quantify the convergence rates for totally asynchronous iterations under various assumptions on communication delays and update rates.

In scalable machine learning systems, model training is often parallelized over multiple nodes that run without tight synchronization. Most analysis results for the related asynchronous algorithms use an upper bound on the information delays in the system to determine learning rates. Not only are such bounds hard to obtain in advance, but they also result in unnecessarily slow convergence. In this paper, we show that it is possible to use learning rates that depend on the actual time-varying delays in the system. We develop general convergence results for delay-adaptive asynchronous iterations and specialize these to proximal incremental gradient descent and block coordinate descent algorithms. For each of these methods, we demonstrate how delays can be measured on-line, present delay-adaptive step-size policies, and illustrate their theoretical and practical advantages over the state-of-the-art.

Motivated by large-scale optimization problems arising in the context of machine learning, there have been several advances in the study of asynchronous parallel and distributed optimization methods during the past decade. Asynchronous methods do not require all processors to maintain a consistent view of the optimization variables. Consequently, they generally can make more efficient use of computational resources than synchronous methods, and they are not sensitive to issues like stragglers (i.e., slow nodes) and unreliable communication links. Mathematical modeling of asynchronous methods involves proper accounting of information delays, which makes their analysis challenging. This article reviews recent developments in the design and analysis of asynchronous optimization methods, covering both centralized methods, where all processors update a master copy of the optimization variables, and decentralized methods, where each processor maintains a local copy of the variables. The analysis provides insights into how the degree of asynchrony impacts convergence rates, especially in stochastic optimization methods.

The standard interference functions introduced by Yates have been very influential on the analysis and design of distributed power control laws. While powerful and versatile, the framework has some drawbacks: the existence of fixed-points has to be established separately, and no guarantees are given on the rate of convergence of the iterates. This paper introduces contractive interference functions, a slight reformulation of the standard interference functions that guarantees existence and uniqueness of fixed-points and geometric convergence rates. We show that many power control laws from the literature are contractive and derive, sometimes for the first time, convergence rate estimates for these algorithms. Finally, we show that although standard interference functions are not contractive, they are paracontractions with respect to a certain metric space. Extensions to two-sided scalable interference functions are also discussed.

The standard interference functions introduced by Yates have been very influential on the analysis and design of distributed power control laws. While powerful and versatile, the framework has some drawbacks: the existence of fixed-points has to be established separately, and no guarantees are given on the rate of convergence of the iterates. This paper introduces contractive interference functions, a slight reformulation of the standard interference functions that guarantees the existence and uniqueness of fixed-points along with linear convergence of iterates. We show that many power control laws from the literature are contractive and derive, sometimes for the first time, analytical convergence rate estimates for these algorithms. We also prove that contractive interference functions converge when executed totally asynchronously and, under the assumption that the communication delay is bounded, derive an explicit bound on the convergence time penalty due to increased delay. Finally, we demonstrate that although standard interference functions are, in general, not contractive, they are all para-contractions with respect to a certain metric. Similar results for two-sided scalable interference functions are also derived.