Optimal Sequence-Based Control of Networked Linear Systems

In Networked Control Systems (NCS), components of a control loop are connected by data networks that may introduce time-varying delays and packet losses into the system, which can severly degrade control performance. Hence, this book presents the newly developed S-LQG (Sequence-Based Linear Quadratic Gaussian) controller that combines the sequence-based control method with the well-known LQG approach to stochastic optimal control in order to compensate for the network-induced effects

Optimal Sequence-Based Control of Networked Linear Systems

In Networked Control Systems (NCS), components of a control loop are connected by data networks that may introduce time-varying delays and packet losses into the system, which can severly degrade control performance. Hence, this book presents the newly developed S-LQG (Sequence-Based Linear Quadratic Gaussian) controller that combines the sequence-based control method with the well-known LQG approach to stochastic optimal control in order to compensate for the network-induced effects

Design methods for networked control systems with unreliable channels focusing on packet dropouts

Texto completo descargado en TeseoLos sistemas de control a través de redes se han convertido en un área importante dentro de la comunidad de control, lo cual es debido a su bajo coste y a la flexibilidad de sus aplicaciones. Los sistemas de control a través de redes (NCSs) se componen de sensores, actuadores y controladores; las operaciones entre ellos se coordinan a través de una red de comunicación. Típicamente, estos sistemas están espacialmente distribuidos, y pueden funcionar de manera asíncrona, pero sus operaciones han de estar coordinadas para conseguir los objetivos deseados. En este resumen se presenta una perspectiva general de los NCSs, y en particular, los casos específicos en los que se ha basado esta tesis, abordando los temas principales relacionados con NCS, con todos los problemas y ventajas asociados, se describen en este resumen. Por último, se presenta un índice de la tesis con sus contribuciones más relevantes. - Introducción a los Sistemas de Control a través de Red Los Sistemas de Control a través de Red (NCSs) son sistemas espacialmente distribuidos donde la comunicación entre plantas, sensores, actuadores y controladores se realiza a través de una red de comunicación. La complejidad en el diseño y la realización, el coste del cableado, la instalación y el mantenimiento pueden ser reducidos drásticamente incluyendo una red de comunicación. Sin embargo, las redes de comunicación en los sistemas también traen algunos incovenientes como los retrasos y la pérdida de datos, los errores de codificación, etc. Estos incovenientes pueden ser la causa de la de la degradación del comportamiento del sistema e incluso causar su desestabilización. Hoy en día, hay un gran número de situaciones prácticas en las que el uso de redes de comunicación para el control son necesarias para aplicaciones o procesos de control en ingeniería. Algunos ejemplos son: Situaciones en las que el espacio y el peso están limitados. Situaciones en las que las distancias a considerar son grandes. Aplicaciones de control donde el cableado no es posible. El uso de redes de comunicación digitales proporciona también algunas ventajas: La complejidad en el cableado en conexiones punto a punto se reduce mucho, así como el coste. Además, los costes de instalación pueden reducirse también drásticamente. La reducción en la complejidad del cableado hace mucho más fácil el diagnóstico y el mantenimiento del sistema, dando lugar a un ahorro en el coste debido a que la instalación y el funcionamiento tienen una eficiencia mayor. Los NCSs son flexibles y reconfigurables. Fiabilidad, redundancia y robustez ante los fallos. Los NCSs proporcionan modularidad, control descentralizado y diagnósticos integrados. Todas estas ventajas sugieren que los NCSs jugarán un papel principal en un futuro cercano, siendo un área de investigación muy prometedora. - Objetivos de la tesis La idea general de esta tesis es proponer algunas soluciones novedosas a diferentes problemas relacionados con NCSs. Todos los problemas considerados son típicos dentro del marco del control a través de redes, considerándose principalmente el de las pérdidas de paquetes en la transmisión de datos. Dentro del contexto de sistemas con pérdida de paquetes, se han estudiado diferentes problemas. Para obtener soluciones diferentes para este tipo de sistemas, se han considerado los siguientes objetivos: Diseño de controladores. Controladores Hinf, que consigan la robustificación de sistemas con incertidumbres. Controladores MPC, combinados con estrategias de buffer. Diseño de filtros. Filtros Hinf para sistemas con incertidumbres, usando técnicas frecuenciales y cadenas de Markov. Diseño de algoritmos. Localización dinámica de un control distribuido en una red formada por una estructura matricial de nodos. Localización dinámica del estimador de la salida del sistema, en una red formada por una estructura lineal de nodos. Estimación distribuida cooperativa. Basada en observadores locales de Luenberger. - Conclusiones Uno de los objetivos de esta tesis ha sido el análisis de la estabilidad y comportamiento de sistemas bajo control. En algunos casos, el diseño se ha realizado imponiendo restricciones en cuanto a la estabilidad. La robustificación de sistemas, en particular la de aquellos con incertidumbres, ha sido también tenida en cuenta. Las técnicas de control Hinf se han usado en los casos de análisis y diseño de sistemas de control. Otro objetivo importante de esta tesis ha sido el diseño de algoritmos para una red dinámica, la cual está compuesta por cierta estructura de nodos. El algoritmo es capaz de decidir qué nodo será el controlador o el estimador de la salida del sistema en la red. La estabilidad y el comportamiento del sistema de control ha sido analizado. También se ha abordado el diseño de estimación y esquemas distribuidos. Se han considerado redes que introducen retrasos temporales, junto con pérdidas aleatorias. La reducción en el consumo de energía ha sido un objetivo importante en esta parte de la tesis. En este caso, se ha examinado una política de comunicación entre agentes basada en eventos, la cual da lugar a un compromiso entre el comportamiento del sistema y los ahorros en la comunicación

Neural Networks: Training and Application to Nonlinear System Identification and Control

This dissertation investigates training neural networks for system identification and classification. The research contains two main contributions as follow:1. Reducing number of hidden layer nodes using a feedforward componentThis research reduces the number of hidden layer nodes and training time of neural networks to make them more suited to online identification and control applications by adding a parallel feedforward component. Implementing the feedforward component with a wavelet neural network and an echo state network provides good models for nonlinear systems.The wavelet neural network with feedforward component along with model predictive controller can reliably identify and control a seismically isolated structure during earthquake. The network model provides the predictions for model predictive control. Simulations of a 5-story seismically isolated structure with conventional lead-rubber bearings showed significant reductions of all response amplitudes for both near-field (pulse) and far-field ground motions, including reduced deformations along with corresponding reduction in acceleration response. The controller effectively regulated the apparent stiffness at the isolation level. The approach is also applied to the online identification and control of an unmanned vehicle. Lyapunov theory is used to prove the stability of the wavelet neural network and the model predictive controller. 2. Training neural networks using trajectory based optimization approachesTraining neural networks is a nonlinear non-convex optimization problem to determine the weights of the neural network. Traditional training algorithms can be inefficient and can get trapped in local minima. Two global optimization approaches are adapted to train neural networks and avoid the local minima problem. Lyapunov theory is used to prove the stability of the proposed methodology and its convergence in the presence of measurement errors. The first approach transforms the constraint satisfaction problem into unconstrained optimization. The constraints define a quotient gradient system (QGS) whose stable equilibrium points are local minima of the unconstrained optimization. The QGS is integrated to determine local minima and the local minimum with the best generalization performance is chosen as the optimal solution. The second approach uses the QGS together with a projected gradient system (PGS). The PGS is a nonlinear dynamical system, defined based on the optimization problem that searches the components of the feasible region for solutions. Lyapunov theory is used to prove the stability of PGS and QGS and their stability under presence of measurement noise

Oceanographic pursuit: Networked control of multiple vehicles tracking dynamic ocean features

We present an integrated framework for joint estimation and pursuit of dynamic features in the ocean, over large spatial scales and with multiple collaborating vehicles relying on limited communications. Our approach uses ocean model predictions to design closed-loop networked control at short time scales, and the primary innovation is to represent model uncertainty via a projection of ensemble forecasts into local linearized vehicle coordinates. Based on this projection, we identify a stochastic linear time-invariant model for estimation and control design. The methodology accurately decomposes spatial and temporal variations, exploits coupling between sites along the feature, and allows for advanced methods in communication-constrained control. Simulations with three example datasets successfully demonstrate the proof-of-concept.United States. Office of Naval Research (Grant N00014-09-1-0700)National Science Foundation (U.S.) (Contract CNS-1212597

Robust nonlinear receding horizon control with constraint tightening: off line approximation and application to networked control system

2007/2008Nonlinear Receding Horizon (RH) control, also known as moving horizon control or nonlinear Model Predictive Control (MPC), refers to a class of algorithms that make explicit use of a nonlinear process model to optimize the plant behavior, by computing a sequence of future ma- nipulated variable adjustments. Usually the optimal control sequence is obtained by minimizing a multi-stage cost functional on the basis of open-loop predictions. The presence of uncertainty in the model used for the optimization raises the question of robustness, i.e., the maintenance of certain properties such as stability and performance in the presence of uncertainty. The need for guaranteeing the closed-loop stability in presence of uncertainties motivates the conception of robust nonlinear MPC, in which the perturbations are explicitly taken in account in the design of the controller. When the nature of the uncertainty is know, and it is assumed to be bounded in some compact set, the robust RH control can be determined, in a natural way, by solving a min–max optimal control problem, that is, the performance objective is optimized for the worst-case scenario. However, the use of min-max techniques is limited by the high computational burden required to solve the optimization problem. In the case of constrained system, a possibility to ensure the robust constraint satisfaction and the closed-loop stability without resorting to min-max optimization consists in imposing restricted (tightened) constraints on the the predicted trajectories during the optimization. In this framework, an MPC scheme with constraint tightening for discrete-time nonlinear systems affected by state-dependent and norm bounded uncertainties is proposed and discussed. A novel method to tighten the constraints relying on the nominal state prediction is described, leading to less conservative set contractions than in the existing approaches. Moreover, by imposing a stabilizing state constraint at the end of the control horizon (in place of the usual terminal one placed at the end of the prediction horizon), less stringent assumptions can be posed on the terminal region, while improving the robust stability properties of the MPC closed-loop system. The robust nonlinear MPC formulation with tightened constraints is then used to design off- line approximate feedback laws able to guarantee the practical stability of the closed-loop system. By using off-line approximations, the computational burden due to the on-line optimization is removed, thus allowing for the application of the MPC to systems with fast dynamics. In this framework, we will also address the problem of approximating possibly discontinuous feedback functions, thus overcoming the limitation of existent approximation scheme which assume the continuity of the RH control law (whereas this condition is not always verified in practice, due to both nonlinearities and constraints). Finally, the problem of stabilizing constrained systems with networked unreliable (and de- layed) feedback and command channels is also considered. In order to satisfy the control ob- jectives for this class of systems, also referenced to as Networked Control Systems (NCS’s), a control scheme based on the combined use of constraint tightening MPC with a delay compen- sation strategy will be proposed and analyzed. The stability properties of all the aforementioned MPC schemes are characterized by using the regional Input-to-State Stability (ISS) tool. The ISS approach allows to analyze the depen- dence of state trajectories of nonlinear systems on the magnitude of inputs, which can represent control variables or disturbances. Typically, in MPC the ISS property is characterized in terms of Lyapunov functions, both for historical and practical reasons, since the optimal finite horizon cost of the optimization problem can be easily used for this task. Note that, in order to study the ISS property of MPC closed-loop systems, global results are in general not useful because, due to the presence of state and input constraints, it is impossible to establish global bounds for the multi-stage cost used as Lyapunov function. On the other hand local results do not allow to analyze the properties of the predictive control law in terms of its region of attraction. There- fore, regional ISS results have to employed for MPC controlled systems. Moreover, in the case of NCS, the resulting control strategy yields to a time-varying closed-loop system, whose stability properties can be analyzed using a novel regional ISS characterization in terms of time-varying Lyapunov functions.XXI Ciclo198

Programming a paintable computer

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002.Includes bibliographical references (p. 163-169).A paintable computer is defined as an agglomerate of numerous, finely dispersed, ultra-miniaturized computing particles; each positioned randomly, running asynchronously and communicating locally. Individual particles are tightly resource bound, and processing is necessarily distributed. Yet computing elements are vanishingly cheap and are regarded as freely expendable. In this regime, a limiting problem is the distribution of processing over a particle ensemble whose topology can vary unexpectedly. The principles of material self-assembly are employed to guide the positioning of "process fragments" - autonomous, mobile pieces of a larger process. These fragments spatially position themselves and reaggregate into a running process. We present the results of simulations to show that "process self-assembly" is viable, robust and supports a variety of useful applications on a paintable computer. We describe a hardware reference platform as an initial guide to the application domain. We describe a programming model which normatively defines the term process fragment and which provides environmental support for the fragment's mobility, scheduling and data exchange. The programming model is embodied in a simulator that supports development, test and visualization on a 2D particle ensemble. Experiments on simple combinations of fragments demonstrate robustness and explore the limits of scale invariance. Process fragments are shown interacting to approximate conservative fields, and using these fields to implement scaffolded and thermodynamic self-assembly.(cont.) Four applications demonstrate practical relevance, delineate the application domain and collectively illustrate the paintable's capacity for storage, communication and signal processing. These four applications are Audio Streaming, Holistic Data Storage, Surface Bus and Image Segmentation.by William Joseph Butera.Ph.D

Task-oriented cross-system design for Metaverse in 6G era

As an emerging concept, the Metaverse has the potential to revolutionize social interaction in the post-pandemic era by establishing a digital world for online education, remote healthcare, immersive business, intelligent transportation, and advanced manufacturing. The goal is ambitious, yet the methodologies and technologies to achieve the full vision of the Metaverse remain unclear. In this thesis, we first introduce the three pillars of infrastructure that lay the foundation of the Metaverse, i.e., Human-Computer Interfaces (HCIs), sensing and communication systems, and network architectures. Then, we depict the roadmap towards the Metaverse that consists of four stages with different applications. As one of the essential building blocks for the Metaverse, we also review the state-of-the-art Computer Vision for the Metaverse as well as the future scope. To support diverse applications in the Metaverse, we put forward a novel design methodology: task-oriented cross-system design, and further review the potential solutions and future challenges. Specifically, we establish a task-oriented cross-system design for a simple case, where sampling, communications, and prediction modules are jointly optimized for the synchronization of the real-world devices and digital model in the Metaverse. We use domain knowledge to design a deep reinforcement learning (DRL) algorithm to minimize the communication load subject to an average tracking error constraint. We validate our framework on a prototype composed of a real-world robotic arm and its digital model. The results show that our framework achieves a better trade-off between the average tracking error and the average communication load compared to a communication system without sampling and prediction. For example, the average communication load can be reduced to 87% when the average track error constraint is 0.002◦ . In addition, our policy outperforms the benchmark with the static sampling rate and prediction horizon optimized by exhaustive search, in terms of the tail probability of the tracking error. Furthermore, with the assistance of expert knowledge, the proposed algorithm achieves a better convergence time, stability, communication load, and average tracking error. Furthermore, we establish a task-oriented cross-system design framework for a general case, where the goal is to minimize the required packet rate for timely and accurate modeling of a real-world robotic arm in the Metaverse. Specifically, different modules including sensing, communications, prediction, control, and rendering are considered. To optimize a scheduling policy and prediction horizons, we design a Constraint Proximal Policy Optimization (CPPO) algorithm by integrating domain knowledge from relevant systems into the advanced reinforcement learning algorithm, Proximal Policy Optimization (PPO). Specifically, the Jacobian matrix for analyzing the motion of the robotic arm is included in the state of the CPPO algorithm, and the Conditional Value-at-Risk (CVaR) of the state-value function characterizing the long-term modeling error is adopted in the constraint. Besides, the policy is represented by a two-branch neural network determining the scheduling policy and the prediction horizons, respectively. To evaluate our algorithm, we build a prototype including a real-world robotic arm and its digital model in the Metaverse. The experimental results indicate that domain knowledge helps to reduce the convergence time and the required packet rate by up to 50%, and the cross-system design framework outperforms a baseline framework in terms of the required packet rate and the tail distribution of the modeling error

Wirelessly Enabled Control of Cyber-Physical Infrastructure with Applications to Hydronic Systems.

Civil infrastructure systems, such as transportation networks, pipe networks, electrical grids, and building environments, are typically managed and controlled with outdated, inefficient, and minimally automated legacy controllers. This is apparent from documented oil pipeline leaks, broad electrical outages, and power plant failures. The relatively recent advents of small inexpensive microcontrollers and low-power wireless networking technologies has revealed opportunities for better managing the operational effectiveness of civil infrastructure systems. Academic research in this field is maturing, yet the field remains in its nascent years of commercial viability, focusing mainly on low data-rate sensing with centralized processing. Little focus has been on distributed wireless control systems for civil infrastructure. This dissertation follows the development and utilization of a new cyber-physical system (CPS) architecture for civil infrastructure. Embedded computing power is distributed throughout the physical systems and global objectives are met with the aid of wireless information exchange. The Martlet wireless controller node was conceived during the first part of this thesis to enable this objective of wirelessly distributed CPS. Once produced, the Martlet was used to realize such a controller, motivated by an application in hydronic cooling systems. The design of the proposed controller began with a study concerning models and objective functions for the control of bilinear systems, like those found in hydronics, when constrained by the resources of a wireless control node. The results showed that previous work with linear quadratic controllers could be improved by using nonlinear models and explicit objective functions. An agent-based controller utilizing the proposed bilinear model-predictive control algorithm, was then developed accounting for the limitation of, and leveraging the advantages of, wireless control nodes in order to regulate a hydronic system with hybrid dynamics. The resulting Martlet based control system was compared to traditional benchmark controllers and shown to achieve adequate performance, with the added benefits of a wireless CPS. These developments in wirelessly distributed control of complex systems are presented not only with the tested hydronic systems in mind, but with the goal of extending this technology to improve the performance and reliability of a wide variety of controlled cyber-physical civil infrastructure systems.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107310/1/mbkane_1.pd