Virtual Power Plants (VPPs) have emerged as a modern real-time energy management architecture that seeks to synergistically coordinate an aggregation of renewable and non-renewable generation systems overcome some the fundamental limitations traditional power grids dominated by synchronous machines. In this survey paper, we review different existing emerging feedback control mechanisms architectures used for operation VPPs. contrast other works mostly focused on optimal dispatch economical...

We introduce a class of concurrent learning (CL) algorithms designed to solve parameter estimation problems with convergence rates ranging from hyperexponential prescribed-time while utilizing alternating datasets during the process. The proposed algorithm employs broad dynamic gains, exponentially growing finite-time blow-up enabling either enhanced or user-prescribed time independent dataset's richness. CL can handle applications involving switching between multiple that may have varying...

This paper investigates the combination of reinforcement learning and neural networks applied to data-driven control dynamical systems. In particular, we propose a multi-critic actor-critic architecture that eases value function task by distributing it into multiple networks. We also filtered approach offers further performance improvements as training process policy. All studied methods are evaluated with several numerical experiments on multi-tank water systems nonlinear coupled dynamics,...

A hierarchical control strategy is proposed to solve the optimal drainage problem in sewer systems by combining an optimization technique known as minimum scaled consensus (MSCC) with deep deterministic policy gradient (DDPG) algorithm. The MSCC operates at global level, and used determine flows of hydraulic structures system, such that water optimally distributed, i.e., wastewater are controlled minimize saturation levels and/or flooding events, filling each system components (e.g., pipes,...

We present a new class of accelerated distributed algorithms for the robust solution convex optimization problems over networks. The novelty approach lies in introduction restarting mechanisms that coordinate evolution dynamics with individual asynchronous and periodic time-varying momentum coefficients. model as set-valued hybrid dynamical systems since method combines continuous-time acceleration discrete-time updates. For these dynamics, we derive graph-dependent conditions guarantee...

Multi-time scale techniques, such as singular perturbations and averaging theory, have played an important role in the development of distributed Nash equilibrium seeking algorithms for network systems. Such techniques rely on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">uniform asymptotic stability</i> properties dynamics that evolve each time scales closed-loop system. When are absent, synthesis multi-time equilibrium-seeking is more...

We introduce a new closed-loop architecture for the online solution of approximate optimal control problems in context continuous-time systems. Specifically, we first algorithm that incorporates dynamic momentum actor-critic structures to plants with an affine structure input. By incorporating our algorithm, are able accelerate convergence properties system, achieving superior transient performance compared traditional gradient-descent based techniques. In addition, by leveraging existence...

We study the problem of robust resource allocation with momentum following a dynamical systems point view. Motivated by class existing optimization dynamics no defined on general m-simplex, we propose time-varying differential equations that achieve acceleration and preserve most asymptotic properties its time-invariant counterpart. Since in continuous-time usually lack structural robustness properties, present hybrid regularization induces property uniform stability system. show this using...

The metabolic clearance of prolactin (PRL) is partially executed by the kidney. Here, we investigate urine excretion PRL in patients with Diabetes Mellitus and renal impairment.Serum samples were collected from male, mestizo central Mexico employing a cross-sectional study design. Ninety-eight individuals had either no diabetes normal function (control), function, or impaired function. was determined chemiluminescent immunometric assay; protein, albumin, creatinine evaluated using...

This paper studies the stability and convergence properties of a class multi-agent concurrent learning (CL) algorithms with momentum restart. Such can be integrated as part estimation pipelines data-enabled control systems to enhance transient performance while maintaining guarantees. However, characterizing restarting policies that yield stable behaviors in decentralized CL systems, especially when network topology communication graph is directed, has remained an open problem. In this...

Multi-time scale techniques, such as singular perturbations and averaging theory, have played an essential role in the development of distributed Nash equilibrium-seeking algorithms for network systems. Such techniques intrinsically rely on uniform asymptotic stability properties dynamics that evolve each time closed-loop system. When are absent, synthesizing multi-time is more challenging requires additional regularization mechanisms. In this paper, we investigate synthesis analysis these...

Traffic congestion has dire economic and social impacts in modern metropolitan areas. To address this problem, paper we introduce a novel type of model-free transactive controllers to manage vehicle traffic highway networks for which precise mathematical models are not available. Specifically, consider system with managed lanes on dynamic tolling mechanisms can be implemented real-time using measurements from the roads. We present three incentive-seeking feedback able find optimal incentives...

The area of Cyber-Physical Systems (CPS) has emerged as a general discipline that studies complex dynamical systems incorporate computation, control, and communication technologies [10]. application CPS spans several domains, from autonomous driving intelligent transportation to deep-space exploration quantum computing. However, traditionally, the algorithmic design implementation algorithms have been studied separately. While this approach facilitates theoretical analysis system, it...

To be cost-effective, robot-based undersea mining must comply several operational constraints. Among the main constraints are time and energy required to extract mineral from seabed. It is also important reduce wear of joints that connect ship on surface with robot crawler does seabed, since this not only reduces operating costs, but lengthens useful life these parts which increases system security. For reason, least amount twisting in pieces preferable, so it advisable number turns or...