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This paper describes DASH: Delegation to Autonomous Systems within Human-machine teams. The purpose of DASH is to combine ideas from recent progress in human machine interaction to confront current and prospected challenges of distributed delegation and meaningful human control. We describe the design considerations of DASH and illustrate how delegation using plays, a web of interaction modules, and interdependency graphs are used in the context of a military human-robot team.

Event-based sampling of sensor signals has become a mature alternative to time-periodic sampling completed with solutions for event-based estimation and control. Among those solutions there is a class of estimators exploiting the fact that an event was not triggered. Not receiving a new measurement is then interpreted as a sensor signal that has not violated the event criteria, which means that the signal is still within the triggering set defining the event. Such implied measurement information is exploited by the estimator, though it is only valid when no event occurred. The approach is thus sensitive to package loss and latency, as the estimator might be incorrect in assuming that no event took place. A solution to anticipate for package loss on the estimation error is studied in this article, and it is further turned into a first solution when the estimator is part of a feedback-control loop.

Intelligent robotic autonomous systems (unmanned aerial/ground/surface/underwater vehicles) are attractive for military application to relieve humans from tedious or dangerous tasks. These systems require awareness of the environment and their own performance to reach a mission goal. This awareness enables them to adapt their operations to handle unexpected changes in the environment and uncertainty in assessments. Components of the autonomous system cannot rely on perfect awareness or actuator execution, and mistakes of one component can affect the entire system. To obtain a robust system, a system-wide approach is needed and a realistic model of all aspects of the system and its environment. In this paper, we present our study on the design and development of a fully functional autonomous system, consisting of sensors, observation processing and behavior analysis, information database, knowledge base, communication, planning processes, and actuators. The system behaves as a teammate of a human operator and can perform tasks independently with minimal interaction. The system keeps the human informed about relevant developments that may require human assistance, and the human can always redirect the system with high-level instructions. The communication behavior is implemented as a Social AI Layer (SAIL). The autonomous system was tested in a simulation environment to support rapid prototyping and evaluation. The simulation is based on the Robotic Operating System (ROS) with fully modelled sensors and actuators and the 3D graphics-enabled physics simulation software Gazebo. In this simulation, various flying and driving autonomous systems can execute their tasks in a realistic 3D environment with scripted or user-controlled threats. The results show the performance of autonomous operation as well as interaction with humans

In the context of building heating systems control in office buildings, the current state-of-the-art applies either a deterministic Model Predictive Control (MPC) controller together with a nonlinear model, or a linearized model with a stochastic MPC controller. Deterministic MPC considers only one realization of the external disturbances, which can lead to a low performance solution if the forecasts of the disturbances are not accurate. Similarly, linear models are simplified representations of the building dynamics and might fail to capture some relevant behavior. In this paper, we improve upon the current literature by combining these two approaches, i.e. we adopt a nonlinear model together with a stochastic MPC controller. We consider a scenario-based MPC (SBMPC), where many realizations of the disturbances are considered, so as to include more possible future trajectories for the external disturbances. The adopted scenario generation method provides statistically significant scenarios, whereas so far in the current literature only approximate methods have been applied. Moreover, we use Modelica to obtain the model description, which allows to have a more accurate and nonlinear model. Lastly, we perform simulations comparing standard MPC vs SBMPC vs an optimal control approach with measurements of the external disturbances, and we show how our proposed scenario-based MPC controller can achieve a better performance compared to standard deterministic MPC. © 2019 IEEE.

A novel partitioning approach for linear switching large-scale systems is presented. We assume that the modes of the switching system are unknown a priori but can be detected. We propose an online partitioning scheme that can partition the system when the mode switches, thus adapting the partition to the mode. Moreover, after the system has been partitioned, we apply a decentralized state-feedback control scheme to stabilize the system. We also apply a dwell time stability scheme to prove that the closed-loop system remains stable even after both the mode and partition changes. The proposed approach is illustrated by means of an automatic generation control problem related to frequency deviation regulation in a large-scale power network., This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 675318 (INCITE). (Wicak Ananduta and Tomás Pippia are co-first authors.)

In this paper, Mine Counter-Measures (MCM) operations with multiple cooperative Autonomous Underwater Vehicles (AUVs) are examined within the Distributed Constraint optimization Problem (DCOP) framework. The goal of an MCM-operation is to search for mines and mine-like objects within a predetermined area so that ships can pass the area through a safe transit corridor. Performance metrics, such as the expected time of completion and the level of confidence that all mine-like objects within the area have been detected, are used to quantity the utility of the operation. The AUVs coordinate their individual search segments in a distributed manner in order to maximize the global utility. The segmentation is optimized by the Compression-DPOP (C-DPOP) algorithm, which allows explicit reasoning by the AUVs about their actions based on the performance metrics. After initial segmentation of the mine threat area, subsequent optimizations are triggered by the AUVs based on the variations in sonar performance. The performance of the C-DPOP algorithm is compared to a static segmentation approach and validated using the high-fidelity Unmanned Underwater Vehicle (UUV) simulation environment based on the Gazebo simulator. A© 2018 IEEE.

DCOP (Distributed Constraint optimization Problem) is a framework for representing distributed multi- agent problems. However, it only allows discrete values for the decision variables, which limits its application for real-world problems. In this paper, an extension of DCOP is investigated to handle variables with continuous domains. Additionally, an iterative any-time algorithm Compression-DPOP (C-DPOP) is presented that is based on the Distributed Pseudo-tree Opti- mization Procedure (DPOP). C-DPOP iteratively samples the search space in order to handle problems that are restricted by time and memory limitations. The performance of the algorithm is examined through a mobile sensor coordination problem. The proposed algorithm outperforms DPOP with uniform sampling regarding both resource requirement and performance. © 2018 European Control Association (EUCA).

We propose a parametrized Model Predictive Control (MPC) approach for optimal operation of microgrids. The parametrization expresses the control input as a function of the states, variables, and parameters. In this way, it is possible to apply an MPC approach by optimizing only the parameters and not the inputs. Moreover, the value of the binary control variables in the model is assigned according to parametrized heuristic rules, thus obtaining a formulation for the optimization problem that is more scalable compared to standard approaches in the literature. Furthermore, we propose a control scheme based on one single controller that uses two different sampling times and prediction models. By doing so, we can include both fast and slow dynamics of the system at the same level. This control approach is applied to an operational control problem of a microgrid, which includes local loads, local production units, and local energy storage systems and results show the effectiveness of the proposed approach.

Distributed state estimation refers to a method for computing a local estimate of the global state in each node of a networked systems. Typically, current methods take a homogenous approach, where the same estimator is performed in each node for approximating a centralised estimation result. Yet, many practical examples indicate that the capabilities of a networked systems rise when its nodes do not only have similarities, but, to some extent, differ from each other. Inspired by this idea, this study extends current studies on homogeneous estimation and turns it into a heterogeneous solution for distributed state estimation focusing on three contributions. Firstly, the estimation problem in networked systems is reformulated so that a particular interdependency of the estimation results between any two nodes is pursued rather than aiming for similar estimation results across all nodes. Secondly, a node setup is adopted suiting heterogeneous estimation on system level, yet, still allows for nodes to employ solutions based on existing (homogenous) distributed estimators. Thirdly, the pursued interdependency of local estimation results is proven for the adopted setup, along with practical conditions that will guarantee stable estimation results in each node locally. Extended designs of this heterogeneous estimator are studied for observing the spread of a chemical compound (linear) and for detecting shockwaves on a highway (non-linear).

Distributed and decentralized processing and fusion of sensor data are becoming increasingly important. In view of the Internet of Things and the vision of ubiquitous sensing, designing and implementing multisensor state estimation algorithm have already become a key issue. A network of interconnected sensor devices is usually characterized by the idea to process and collect data locally and independently on the sensor nodes. However, this does not imply that the data are independent of each other, and the state estimation algorithms have to address possible interdependencies so as to avoid erroneous data fusion results. © 2016 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business.

Decentralized data fusion is a challenging task. Either it is too difficult to maintain and track the information required to perform fusion optimally, or too much information is discarded to obtain informative fusion results. A well-known solution is Covariance Intersection, which may provide too conservative fusion results. A less conservative alternative is discussed in this paper, and generalizations are proposed in order to apply it to a wide class of fusion problems. The Inverse Covariance Intersection algorithm is about finding the maximum possible common information shared by the estimates to be fused. A bound on the possibly shared common information is derived and removed from the fusion result in order to guarantee consistency. It is shown that the conditions required for consistency can be significantly relaxed, and also other causes of correlations, such as common process noise, can be treated. © 2017 International Society of Information Fusion (ISIF). China Gezhouba Group No.3 Engineering Co., Ltd (CGGC); Energy China; et al.; Hangzhou Dianzi Univeristy; LIFT; SATPRO

For decentralized fusion problems, ellipsoidal intersection has been proposed as an efficient fusion rule that provides less conservative results as compared to the well-know covariance intersection method. Ellipsoidal intersection relies on the computation of a common estimate that is shared by the estimates to be fused. In this paper, an algebraic reformulation of ellipsoidal intersection is discussed that circumvents the computation of the common estimate. It is shown that ellipsoidal intersection corresponds to an internal ellipsoidal approximation of the intersection of covariance ellipsoids. An interesting result is that ellipsoidal intersection can be computed with the aid of the Bar-Shalom/Campo fusion formulae. This is achieved by assuming a specific correlation structure between the estimates to be fused.

This article focuses on the problem of fusing two prior Gaussian estimates into a single estimate, when the correlation is unknown. Existing solutions either lead to a conservative fusion result, as the chosen parametrization focuses on the fusion formulas instead of correlations, or they are computationally expensive. The contribution of this article is a novel parametrization, in which the correlation is explicitly characterized a priori to deriving the fusion formulas. Then, maximizing the correlation ensures that the fusion result is based on independent parts of the prior estimates and, simultaneously, addresses the fact that the correlation is unknown. In addition, a guaranteed improvement of the accuracy after fusion is attained. An illustrative example demonstrates the benefits of the proposed method compared to an existing fusion method.

Strategies for controlling the indoor climate in greenhouses are based on a few sensors and actuators in combination with an assumption that climate variables, such as temperature, are uniform throughout the greenhouse. While this is already an improper assumption for conventional greenhouses, it especially does not hold for the new trend of growing crops at multiple layers. In addition, different temperature values are desired at the different layers, which turns out to give an uncontrollability issue with the current set of actuators. To solve this issue, fans are placed at each of the layers and an MPC strategy is employed for controlling this nonlinear MIMO system. A case study further shows that such a control strategy reduces the consumed energy of the greenhouse, while maintaining the desired temperature values.

Distributed implementations of state estimation algorithms generally have in common that each node in a networked system computes an estimate on the entire global state. Accordingly, each node has to store and compute an estimate of the same state vector irrespective of whether its sensors can only observe a small part of it. In particular, the task of monitoring large-scale phenomena renders such distributed estimation approaches impractical due to the sheer size of the corresponding state vector. In order to reduce the workload of the nodes, the state vector to be estimated is subdivided into smaller, possibly overlapping parts. In this situation, fusion does not only refer to the computation of an improved estimate but also to the task of reassembling an estimate for the entire state from the locally computed estimates of unequal state vectors. However, existing fusion methods require equal state representations and, hence, cannot be employed. For that reason, a fusion strategy for estimates of unequal and possibly overlapping state vectors is derived that minimizes the mean squared estimation error. For the situation of unknown cross-correlations between local estimation errors, also a conservative fusion strategy is proposed. © IFAC.

Distributed solutions for signal processing techniques are important for establishing large-scale monitoring and control applications. They enable the deployment of scalable sensor networks for particular application areas. Typically, such networks consists of a large number of vulnerable components connected via unreliable communication links and are sometimes deployed in harsh environment. Therefore, dependability of sensor network is a challenging problem. An efficient and cost effective answer to this challenge is provided by employing runtime reconfiguration techniques that assure the integrity of the desired signal processing functionalities. Runtime reconfigurability has thorough impact both on system design, implementation, testing/validation and deployment. The presented research focuses on the widespreaded signal processing method known as state estimation with Kalman filtering in particular. To that extent, a number of distributed state estimation solutions that are suitable for networked systems in general are overviewed, after which robustness of the system is improved according to various runtime reconfiguration techniques

Centralized state-estimation algorithms, such as the original Kalman filter, are no longer feasible in large scale sensor networks, due to practical limitations on communication bandwidth and spatial distribution of resources. To cope with these limitations, various distributed estimation algorithms have been proposed that estimate the state of a process in each sensor node using local measurements. State fusion of this local estimate with the estimates obtained in neighboring nodes ensures that the difference between local estimates is reduced. A common perspective in distributed state-estimation is that each individual node performs the same algorithm locally. This paper investigates whether it is beneficial to have some nodes that can perform a different, more accurate estimation method, i.e., heterogenous. To that extent, a networked system where each node employs the same local state-estimator is compared to a similar system where different nodes can perform different types of local estimation algorithms. Their performances are assessed on a Van-der-Pol oscillator and on a benchmark application to estimate speed profiles in traffic shockwaves. The results of these examples encourage further investigation of heterogeneous, distributed state-estimation. © 2011 IEEE.

Some crucial challenges of estimation over sensor networks are reaching consensus on the estimates of different systems in the network and separating the mutual information of two estimates from their exclusive information. Current fusion methods of two estimates tend to bypass the mutual information and directly optimize the fused estimate. Moreover, both the mean and covariance of the fused estimate are fully determined by optimizing the covariance only. In contrast to that, this paper proposes a novel fusion method in which the mutual information results in an additional estimate, which defines a mutual mean and covariance. Both variables are derived from the two initial estimates. The mutual covariance is used to optimize the fused covariance, while the mutual mean optimizes the fused mean. An example of decentralized state estimation, where the proposed fusion method is applied, shows a reduction in estimation error compared to the existing alternatives. © 2010 AACC.

To reduce the amount of data transfer in net- worked control systems and wireless sensor networks, mea- surements are usually sampled only when an event occurs, rather than synchronous in time. Today’s event sampling methodologies are triggered by the current value of the sensor. State-estimators are designed to cope with such methods. In this paper we propose a sampling method in which an event is triggered depending on the reduction of the estimator’s uncertainty and estimation-error. As such, communication requirements are minimized while attaining a certain error- covariance matrix and estimation error at the state-estimator. Furthermore, it is proven that the error-covariance matrix is asymptotically bounded in case the designed sampling protocol is combined with an event-based state-estimator. An illustrative example shows that the developed protocol provides an improved state estimation, while minimizing communication between sensor and state-estimator.