Sparse and Constrained Stochastic Predictive Control for Networked Systems

This article presents a novel class of control policies for networked control of Lyapunov-stable linear systems with bounded inputs. The control channel is assumed to have i.i.d. Bernoulli packet dropouts and the system is assumed to be affected by additive stochastic noise. Our proposed class of policies is affine in the past dropouts and saturated values of the past disturbances. We further consider a regularization term in a quadratic performance index to promote sparsity in control. We demonstrate how to augment the underlying optimization problem with a constant negative drift constraint to ensure mean-square boundedness of the closed-loop states, yielding a convex quadratic program to be solved periodically online. The states of the closed-loop plant under the receding horizon implementation of the proposed class of policies are mean square bounded for any positive bound on the control and any non-zero probability of successful transmission

Self-Triggered Stochastic MPC for Linear Systems With Disturbances

In this letter, we present a self-triggering mechanism for stochastic model predictive control (SMPC) of discrete-time linear systems subject to probabilistic constraints, where the controller and the plant are connected by a shared communication network. The proposed triggering mechanism requires that only one control input is allowed to be transmitted through the network at each triggering instant which is then applied to the plant for several steps afterward. By doing so, communication is effectively reduced both in terms of frequency and total amount. We establish the theoretical result for recursive feasibility in the light of proper reformulation of constraints on the nominal system trajectories, and also provide stability analysis for the proposed self-triggered SMPC. A numerical example illustrates the efficiency of the proposed scheme in reducing the communication as well as ensuring meeting the probabilistic constraints

Intelligent Operation System for the Autonomous Vehicle Fleet

Modular vehicles are vehicles with interchangeable substantial components also known as modules. Fleet modularity provides extra operational flexibility through on-field actions, in terms of vehicle assembly, disassembly, and reconfiguration (ADR). The ease of assembly and disassembly of modular vehicles enables them to achieve real-time fleet reconfiguration, which is proven as beneficial in promoting fleet adaptability and in saving ownership costs. The objective of military fleet operation is to satisfy uncertain demands on time while providing vehicle maintenance. To quantify the benefits and burdens from modularity in military operation, a decision support system is required to yield autonomously operation strategies for comparing the (near) optimal fleet performance for different vehicle architectures under diverse scenarios. The problem is challenging because: 1) fleet operation strategies are numerous, especially when modularity is considered; 2) operation actions are time-delayed and time-varying; 3) vehicle damages and demands are highly uncertain; 4) available capacity for ADR actions and vehicle repair is constrained. Finally, to explore advanced tactics enabled by fleet modularity, the competition between human-like and adversarial forces is required, where each force is capable to autonomously perceive and analyze field information, learn enemy's behavior, forecast enemy's actions, and prepare an operation plan accordingly. Currently, methodologies developed specifically for fleet competition are only valid for single type of resources and simple operation rules, which are impossible to implement in modular fleet operation. This dissertation focuses on a new general methodology to yield decisions in operating a fleet of autonomous military vehicles/robots in both conventional and modular architectures. First, a stochastic state space model is created to represent the changes in fleet dynamics caused by operation actions. Then, a stochastic model predictive control is customized to manage the system dynamics, which is capable of real-time decision making. Including modularity increases the complexity of fleet operation problem, a novel intelligent agent based model is proposed to ensure the computational efficiency and also imitate the collaborative decisions making process of human-like commanders. Operation decisions are distributed to several agents with distinct responsibility. Agents are designed in a specific way to collaboratively make and adjust decisions through selectively sharing information, reasoning the causality between events, and learning the other's behavior, which are achieved by real-time optimization and artificial intelligence techniques. To evaluate the impacts from fleet modularity, three operation problems are formulated: (i) simplified logistic mission scenario: operate a fleet to guarantee the readiness of vehicles at battlefields considering the stochasticity in inventory stocks and mission requirements; (ii) tactical mission scenario: deliver resources to battlefields with stochastic requirements of vehicle repairs and maintenance; (iii) attacker-defender game: satisfy the mission requirements with minimized losses caused by uncertain assaults from an enemy. The model is also implemented for a civilian application, namely the real-time management of reconfigurable manufacturing systems (RMSs). As the number of RMS configurations increases exponentially with the size of the line and demand changes frequently, two challenges emerge: how to efficiently select the optimal configuration given limited resources, and how to allocate resources among lines. According to the ideas in modular fleet operation, a new mathematical approach is presented for distributing the stochastic demands and exchanging machines or modules among lines (which are groups of machines) as a bidding process, and for adaptively configuring these lines and machines for the resulting shared demand under a limited inventory of configurable components.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147588/1/lixingyu_2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147588/2/lixingyu_1.pd

Optimization based energy-efficient control inmobile communication networks

In this work we consider how best to control mobility and transmission for the purpose of datatransfer and aggregation in a network of mobile autonomous agents. In particular we considernetworks containing unmanned aerial vehicles (UAVs). We first consider a single link betweena mobile transmitter-receiver pair, and show that the total amount of transmittable data isbounded. For certain special, but not overly restrictive cases, we can determine closed-formexpressions for this bound, as a function of relevant mobility and communication parameters.We then use nonlinear model predictive control (NMPC) to jointly optimize mobility and trans-mission schemes of all networked nodes for the purpose of minimizing the energy expenditureof the network. This yields a novel nonlinear optimal control problem for arbitrary networksof autonomous agents, which we solve with state-of-the-art nonlinear solvers. Numerical re-sults demonstrate increased network capacity and significant communication energy savingscompared to more na ̈ıve policies. All energy expenditure of an autonomous agent is due tocommunication, computation, or mobility and the actual computation of the NMPC solutionmay be a significant cost in both time and computational resources. Furthermore, frequentbroadcasting of control policies throughout the network can require significant transmit andreceive energies. Motivated by this, we develop an event-triggering scheme which accounts forthe accuracy of the optimal control solution, and provides guarantees of the minimum timebetween successive control updates. Solution accuracy should be accounted for in any triggeredNMPC scheme where the system may be run in open loop for extended times based on pos-sibly inaccurate state predictions. We use this analysis to trade-off the cost of updating ourtransmission and locomotion policies, with the frequency by which they must be updated. Thisgives a method to trade-off the computation, communication and mobility related energies ofthe mobile autonomous network.Open Acces

Stochastic Model Predictive Control and Machine Learning for the Participation of Virtual Power Plants in Simultaneous Energy Markets

The emergence of distributed energy resources in the electricity system involves new scenarios in which domestic consumers (end-users) can be aggregated to participate in energy markets, acting as prosumers. Every prosumer is considered to work as an individual energy node, which has its own renewable generation source, its controllable and non-controllable energy loads, or even its own individual tariffs to trade. The nodes can build aggregations which are managed by a system operator. The participation in energy markets is not trivial for individual prosumers due to different aspects such as the technical requirements which must be satisfied, or the need to trade with a minimum volume of energy. These requirements can be solved by the definition of aggregated participations. In this context, the aggregators handle the difficult task of coordinating and stabilizing the prosumers' operations, not only at an individual level, but also at a system level, so that the set of energy nodes behaves as a single entity with respect to the market. The system operators can act as a trading-distributing company, or only as a trading one. For this reason, the optimization model must consider not only aggregated tariffs, but also individual tariffs to allow individual billing for each energy node. The energy node must have the required technical and legal competences, as well as the necessary equipment to manage their participation in energy markets or to delegate it to the system operator. This aggregation, according to business rules and not only to physical locations, is known as virtual power plant. The optimization of the aggregated participation in the different energy markets requires the introduction of the concept of dynamic storage virtualization. Therefore, every energy node in the system under study will have a battery installed to store excess energy. This dynamic virtualization defines logical partitions in the storage system to allow its use for different purposes. As an example, two different partitions can be defined: one for the aggregated participation in the day-ahead market, and the other one for the demand-response program. There are several criteria which must be considered when defining the participation strategy. A risky strategy will report more benefits in terms of trading; however, this strategy will also be more likely to get penalties for not meeting the contract due to uncertainties or operation errors. On the other hand, a conservative strategy would result worse economically in terms of trading, but it will reduce these potential penalties. The inclusion of dynamic intent profiles allows to set risky bids when there exist a potential low error of forecast in terms of generation, load or failures; and conservative bids otherwise. The system operator is the agent who decides how much energy will be reserved to trade, how much to energy node self consumption, how much to demand-response program participation etc. The large number of variables and states makes this problem too complex to be solved by classical methods, especially considering the fact that slight differences in wrong decisions would imply important economic issues in the short term. The concept of dynamic storage virtualization has been studied and implemented to allow the simultaneous participation in multiple energy markets. The simultaneous participations can be optimized considering the objective of potential profits, potential risks or even a combination of both considering more advanced criteria related to the system operator's know-how. Day-ahead bidding algorithms, demand-response program participation optimization and a penalty-reduction operation control algorithm have been developed. A stochastic layer has been defined and implemented to improve the robustness inherent to forecast-dependent systems. This layer has been developed with chance-constraints, which includes the possibility of combining an intelligent agent based on a encoder-decoder arquitecture built with neural networks composed of gated recurrent units. The formulation and the implementation allow a total decouplement among all the algorithms without any dependency among them. Nevertheless, they are completely engaged because the individual execution of each one considers both the current scenario and the selected strategy. This makes possible a wider and better context definition and a more real and accurate situation awareness. In addition to the relevant simulation runs, the platform has also been tested on a real system composed of 40 energy nodes during one year in the German island of Borkum. This experience allowed the extraction of very satisfactory conclusions about the deployment of the platform in real environments.La irrupción de los sistemas de generación distribuidos en los sistemas eléctricos dan lugar a nuevos escenarios donde los consumidores domésticos (usuarios finales) pueden participar en los mercados de energía actuando como prosumidores. Cada prosumidor es considerado como un nodo de energía con su propia fuente de generación de energía renovable, sus cargas controlables y no controlables e incluso sus propias tarifas. Los nodos pueden formar agregaciones que serán gestionadas por un agente denominado operador del sistema. La participación en los mercados energéticos no es trivial, bien sea por requerimientos técnicos de instalación o debido a la necesidad de cubrir un volumen mínimo de energía por transacción, que cada nodo debe cumplir individualmente. Estas limitaciones hacen casi imposible la participación individual, pero pueden ser salvadas mediante participaciones agregadas. El agregador llevará a cabo la ardua tarea de coordinar y estabilizar las operaciones de los nodos de energía, tanto individualmente como a nivel de sistema, para que todo el conjunto se comporte como una unidad con respecto al mercado. Las entidades que gestionan el sistema pueden ser meras comercializadoras, o distribuidoras y comercializadoras simultáneamente. Por este motivo, el modelo de optimización sobre el que basarán sus decisiones deberá considerar, además de las tarifas agregadas, otras individuales para permitir facturaciones independientes. Los nodos deberán tener autonomía legal y técnica, así como el equipamiento necesario y suficiente para poder gestionar, o delegar en el operador del sistema, su participación en los mercados de energía. Esta agregación atendiendo a reglas de negocio y no solamente a restricciones de localización física es lo que se conoce como Virtual Power Plant. La optimización de la participación agregada en los mercados, desde el punto de vista técnico y económico, requiere de la introducción del concepto de virtualización dinámica del almacenamiento, para lo que será indispensable que los nodos pertenecientes al sistema bajo estudio consten de una batería para almacenar la energía sobrante. Esta virtualización dinámica definirá particiones lógicas en el sistema de almacenamiento para dedicar diferentes porcentajes de la energía almacenada para propósitos distintos. Como ejemplo, se podría hacer una virtualización en dos particiones lógicas diferentes: una de demand-response. Así, el sistema podría operar y satisfacer ambos mercados de manera simultánea con el mismo grid y el mismo almacenamiento. El potencial de estas particiones lógicas es que se pueden definir de manera dinámica, dependiendo del contexto de ejecución y del estado, tanto de la red, como de cada uno de los nodos a nivel individual. Para establecer una estrategia de participación se pueden considerar apuestas arriesgadas que reportarán más beneficios en términos de compra-venta, pero también posibles penalizaciones por no poder cumplir con el contrato. Por el contrario, una estrategia conservadora podría resultar menos beneficiosa económicamente en dichos términos de compra-venta, pero reducirá las penalizaciones. La inclusión del concepto de perfiles de intención dinámicos permitirá hacer pujas que sean arriesgadas, cuando existan errores de predicción potencialmente pequeños en términos de generación, consumo o fallos; y pujas más conservadoras en caso contrario. El operador del sistema es el agente que definirá cuánta energía utiliza para comercializar, cuánta para asegurar autoconsumo, cuánta desea tener disponible para participar en el programa de demand-response etc. El gran número de variables y de situaciones posibles hacen que este problema sea muy costoso y complejo de resolver mediante métodos clásicos, sobre todo teniendo en cuenta que pequeñas variaciones en la toma de decisiones pueden tener grandes implicaciones económicas incluso a corto plazo. En esta tesis se ha investigado en el concepto de virtualización dinámica del almacenamiento para permitir una participación simultánea en múltiples mercados. La estrategia de optimización definida permite participaciones simultáneas en diferentes mercados que pueden ser controladas con el objetivo de optimizar el beneficio potencial, el riesgo potencial, o incluso una combinación mixta de ambas en base a otros criterios más avanzados marcados por el know-how del operador del sistema. Se han desarrollado algoritmos de optimización para el mercado del day-ahead, para la participación en el programa de demand-response y un algoritmo de control para reducir las penalizaciones durante la operación mediante modelos de control predictivo. Se ha realizado la definición e implementación de un componente estocástico para hacer el sistema más robusto frente a la incertidumbre inherente a estos sistemas en los que hay tanto peso de una componente de tipo forecasing. La formulación de esta capa se ha realizado mediante chance-constraints, que incluye la posibilidad de combinar diferentes componentes para mejorar la precisión de la optimización. Para el caso de uso presentado se ha elegido la combinación de métodos estadísticos por probabilidad junto a un agente inteligente basado en una arquitectura de codificador-decodificador construida con redes neuronales compuestas de Gated Recurrent Units. La formulación y la implementación utilizada permiten que, aunque todos los algoritmos estén completamente desacoplados y no presenten dependencias entre ellos, todos se actual como la estrategia seleccionada. Esto permite la definición de un contexto mucho más amplio en la ejecución de las optimizaciones y una toma de decisiones más consciente, real y ajustada a la situación que condiciona al proceso. Además de las pertinentes pruebas de simulación, parte de la herramienta ha sido probada en un sistema real compuesto por 40 nodos domésticos, convenientemente equipados, durante un año en una infraestructura implantada en la isla alemana de Borkum. Esta experiencia ha permitido extraer conclusiones muy interesantes sobre la implantación de la plataforma en entornos reales