337 research outputs found
Distributed filtering of networked dynamic systems with non-gaussian noises over sensor networks: A survey
summary:Sensor networks are regarded as a promising technology in the field of information perception and processing owing to the ease of deployment, cost-effectiveness, flexibility, as well as reliability. The information exchange among sensors inevitably suffers from various network-induced phenomena caused by the limited resource utilization and complex application scenarios, and thus is required to be governed by suitable resource-saving communication mechanisms. It is also noteworthy that noises in system dynamics and sensor measurements are ubiquitous and in general unknown but can be bounded, rather than follow specific Gaussian distributions as assumed in Kalman-type filtering. Particular attention of this paper is paid to a survey of recent advances in distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks. First, two types of widely employed structures of distributed filters are reviewed, the corresponding analysis is systematically addressed, and some interesting results are provided. The inherent purpose of adding consensus terms into the distributed filters is profoundly disclosed. Then, some representative models characterizing various network-induced phenomena are reviewed and their corresponding analytical strategies are exhibited in detail. Furthermore, recent results on distributed filtering with non-Gaussian noises are sorted out in accordance with different network-induced phenomena and system models. Another emphasis is laid on recent developments of distributed filtering with various communication scheduling, which are summarized based on the inherent characteristics of their dynamic behavior associated with mathematical models. Finally, the state-of-the-art of distributed filtering and challenging issues, ranging from scalability, security to applications, are raised to guide possible future research
Estimation and stability of nonlinear control systems under intermittent information with applications to multi-agent robotics
This dissertation investigates the role of intermittent information in estimation and control problems and applies the obtained results to multi-agent tasks in robotics. First, we develop a stochastic hybrid model of mobile networks able to capture a large variety of heterogeneous multi-agent problems and phenomena. This model is applied to a case study where a heterogeneous mobile sensor network cooperatively detects and tracks mobile targets based on intermittent observations. When these observations form a satisfactory target trajectory, a mobile sensor is switched to the pursuit mode and deployed to capture the target. The cost of operating the sensors is determined from the geometric properties of the network, environment and probability of target detection. The above case study is motivated by the Marco Polo game played by children in swimming pools. Second, we develop adaptive sampling of targets positions in order to minimize energy consumption, while satisfying performance guarantees such as increased probability of detection over time, and no-escape conditions. A parsimonious predictor-corrector tracking filter, that uses geometrical properties of targets\u27 tracks to estimate their positions using imperfect and intermittent measurements, is presented. It is shown that this filter requires substantially less information and processing power than the Unscented Kalman Filter and Sampling Importance Resampling Particle Filter, while providing comparable estimation performance in the presence of intermittent information. Third, we investigate stability of nonlinear control systems under intermittent information. We replace the traditional periodic paradigm, where the up-to-date information is transmitted and control laws are executed in a periodic fashion, with the event-triggered paradigm. Building on the small gain theorem, we develop input-output triggered control algorithms yielding stable closed-loop systems. In other words, based on the currently available (but outdated) measurements of the outputs and external inputs of a plant, a mechanism triggering when to obtain new measurements and update the control inputs is provided. Depending on the noise environment, the developed algorithm yields stable, asymptotically stable, and Lp-stable (with bias) closed-loop systems. Control loops are modeled as interconnections of hybrid systems for which novel results on Lp-stability are presented. Prediction of a triggering event is achieved by employing Lp-gains over a finite horizon in the small gain theorem. By resorting to convex programming, a method to compute Lp-gains over a finite horizon is devised. Next, we investigate optimal intermittent feedback for nonlinear control systems. Using the currently available measurements from a plant, we develop a methodology that outputs when to update the control law with new measurements such that a given cost function is minimized. Our cost function captures trade-offs between the performance and energy consumption of the control system. The optimization problem is formulated as a Dynamic Programming problem, and Approximate Dynamic Programming is employed to solve it. Instead of advocating a particular approximation architecture for Approximate Dynamic Programming, we formulate properties that successful approximation architectures satisfy. In addition, we consider problems with partially observable states, and propose Particle Filtering to deal with partially observable states and intermittent feedback. Finally, we investigate a decentralized output synchronization problem of heterogeneous linear systems. We develop a self-triggered output broadcasting policy for the interconnected systems. Broadcasting time instants adapt to the current communication topology. For a fixed topology, our broadcasting policy yields global exponential output synchronization, and Lp-stable output synchronization in the presence of disturbances. Employing a converse Lyapunov theorem for impulsive systems, we provide an average dwell time condition that yields disturbance-to-state stable output synchronization in case of switching topology. Our approach is applicable to directed and unbalanced communication topologies.\u2
Multi-agent estimation and control of cyber-physical systems
Doctor of PhilosophyElectrical and Computer EngineeringBalasubramaniam NatarajanA cyber-physical system (CPS) typically consists of networked computational elements
that control physical processes. As an integral part of CPS, the widespread deployment of
communicable sensors makes the task of monitoring and control quite challenging especially from the viewpoint of scalability and complexity. This research investigates two unique aspects of overcoming such barriers, making a CPS more robust against data explosion and network vulnerabilities. First, the correlated characteristics of high-resolution sensor data are exploited to significantly reduce the fused data volume. Specifically, spatial, temporal and spatiotemporal compressed sensing approaches are applied to sample the measurements in compressed form. Such aggregation can directly be used in centralized static state estimation even for a nonlinear system. This approach results in a remarkable reduction in communication overhead as well as memory/storage requirement. Secondly, an agent based architecture is proposed, where the communicable sensors (identified as agents) also perform local information processing. Based on the local and underdetermined observation space, each agent can monitor only a specific subset of global CPS states, necessitating neighborhood information exchange. In this framework, we propose an agent based static state estimation encompassing local consensus and least square solution. Necessary bounds
for the consensus weights are obtained through the maximum eigenvalue based convergence analysis and are verified for a radial power distribution network. The agent based formulation is also applied for a linear dynamical system and the consensus approach is found to exhibit better and more robust performance compared to a diffusion filter. The agent based Kalman consensus filter (AKCF) is further investigated, when the agents can choose between measurements and/or consensus, allowing the economic allocation of sensing and communication tasks as well as the temporary omission of faulty agents. The filter stability is guaranteed by deriving necessary consensus bounds through Lyapunov stability analysis. The states dynamically estimated from AKCF can be used for state-feedback control in a model predictive fashion. The effect of lossy communication is investigated and critical bounds on the link failure rate and the degree of consensus that ensure stability of the agent based control are derived and verified via simulations
FeedNetBack - D05.04 - Design methodologies for event-based control systems
This is a Deliverable Report for the FeedNetBack project (www.feednetback.eu). Networked Control Systems (NCS) are systems in which the sensors or/and the actuators communicate with the controller through a network. Energy saving and robustness to unreliable channels are major challenges in networked control, notably in wireless scenarios. Energy efficiency and in particular asynchronous design methodologies are studied in this deliverable. The presence of a channel between the sensors measuring the plant and the controller generating the control inputs implies that the measurements should be quantized. As a preliminary step, the problem of finding a stabilizing policy with quantized measurements and bounded control inputs is considered. It is common to assume that the different nodes of a Network Control System use a periodic synchronized clock, this simplifies the model which may take into account some transmission delays. However, this assumption is strong and energy consuming. Indeed, the periodic sampling time is often chosen to ensure given performance in the worst case scenario, wasting energy when the system is running around its working point. To relax the assumption of synchronized nodes, the rest of the deliverable introduces two asynchronous design methodologies, event-based and self-triggered methodologies. The former consists in limiting the transmissions between the nodes when a given condition holds, or, in other words, when an event occurs. Not only this approach relaxes the assumption of synchronized nodes, but it also limits the transmissions which save energy. In the following, event-based approach is applied to a feedback control case and an estimation case. However, by its nature, event-based approach forces the communicating node to watch for the occurrence of the triggering event. This is relaxed in self-triggered approach where each node decides, at the end of an action (e.g. measuring, transmitting, controlling), when the next action will take place. In between these times, the node usually goes to down mode to save energy. In the last part of this deliverable, this approach is applied to a variable sample rate control and to the case of IEEE 802.15.4 protocol
State Estimation for Distributed and Hybrid Systems
This thesis deals with two aspects of recursive state estimation: distributed estimation and estimation for hybrid systems. In the first part, an approximate distributed Kalman filter is developed. Nodes update their state estimates by linearly combining local measurements and estimates from their neighbors. This scheme allows nodes to save energy, thus prolonging their lifetime, compared to centralized information processing. The algorithm is evaluated experimentally as part of an ultrasound based positioning system. The first part also contains an example of a sensor-actuator network, where a mobile robot navigates using both local sensors and information from a sensor network. This system was implemented using a component-based framework. The second part develops, a recursive joint maximum a posteriori state estimation scheme for Markov jump linear systems. The estimation problem is reformulated as dynamic programming and then approximated using so called relaxed dynamic programming. This allows the otherwise exponential complexity to be kept at manageable levels. Approximate dynamic programming is also used to develop a sensor scheduling algorithm for linear systems. The algorithm produces an offline schedule that when used together with a Kalman filter minimizes the estimation error covariance
D04.05 - Feasibility mock-ups of feedback schedulers
Control and computation co-design deals with the interaction between feedback control laws design and their implementation on a real execution resource. Control design is often carried out in the framework of continuous time, or under the assumption of ideal sampling with equidistant intervals and known delays. Implementation on a real-time execution platform introduces many timing uncertainties and distortions to the ideal timing scheme, e.g. due to variable computation durations, complex preemption patterns between concurrent activities, uncertain network induced communication delays or occasional data loss. Analyzing, prototyping, simulating and guaranteeing the safety of complex control systems are very challenging topics. Models are needed for the mechatronic continuous system, for the discrete controllers and diagnosers, and for network behavior. Real-time properties (task response times) and the network Quality of Service (QoS) influence the controlled system properties (Quality of Control, QoC). To reach effective and safe systems it is not enough to provide theoretic control laws and leave programmers and real-time systems engineers just do their best to implement the controllers. This report first describes, through the detailed design of a quadrotor drone controller, the main features of {\sc Orccad}, an integrated development environment aimed to bridge the gap between advanced control design and real-time implementation. Besides control design and implementation, a real-time (hardware-in-the-loop) simulation has been designed to assess the control design with a simulated target rather than with the real plant. Using this HIL structure, several experiments using flexible real-time control features are reported, namely Kalman filters subject to data loss, control under (m,k)-firm constraints, control with varying sampling rates and feedback scheduling using the MPC approach
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