ABSTRACT. A background on the Europan Spallation Source is given, and our work on three control problems important for the design of its linear accelerator are presented.

ABSTRACT. This work presents a robotic implementation of Christmas gift wrapping. Handling paper is a challenging task for an industrial robot as it easily tears and folds in unexpected ways. In this application, a dual-arm industrial robot with simple two-finger grippers was used, and the robot was programmed using a standard position-based approach. The wrapping was accomplished with the help of plastic spatulas, and although the speed of the final wrapping was slower than an average human, the gift wrapper application became a success on its tour around Sweden during the Christmas commerce 2015.

ABSTRACT. Simplifying the operation of forestry machines with the help of semi-automation is needed in the modern timber harvesting industry. The introduction of semi-automation functions can bring safety and reliability to most of the operations used for logging. In this paper we present two type of semi-automation scenarios for the rst two joints of a forwarder crane prototype keeping in mind the oscillation reduction of the crane structure.

ABSTRACT. In this paper we consider decentralized traffic signal control policies using the max-weight algorithm when the queue size measurement is noisy. We first show analytically that the standard max-weight algorithm is throughput optimal even under noisy queue measurements. However, the average steady-state queue lengths and subsequently the average delays are increased. In order to alleviate the effect of these noisy measurements we add filtering to the max-weight algorithm; more specifically, we propose the Filtered-max-weight algorithm, which is based on particle filtering. We demonstrate via simulations that the Filtered-max-weight algorithm performs better, in terms of average and standard deviation of the queue length, than the standard max-weight algorithm in the presence of noisy measurements.

ABSTRACT. A control policy for traffic lights in urban road networks is analyzed. The only measurements the controller needs are the densities on the incoming lanes for each junction. This makes the controller more robust compared to previously proposed back-pressure based strategies, since they also require information about the traffic flow through the junctions. Under the condition that the external inflows lies inside the largest possible stability region, it is shown that the policy manages to keep the queue lengths bounded.

ABSTRACT. We consider the fuel-optimal coordination of trucks into platoons. Truck platooning is a promising technology that enables trucks to save significant amounts of fuel by driving close together and thus reducing air drag. We study how fuel-optimal speed profiles for platooning can be computed. A first-order fuel model is considered and pairwise optimal plans are derived. We formulate an optimization problem that combines these pairwise plans into an overall plan for a large number of trucks. The problem resembles a medoids clustering problem. We propose an approximation algorithm similar to the partitioning around medoids algorithm and discuss its convergence. The method is evaluated with Monte Carlo simulations. We demonstrate that the proposed algorithm can compute a plan for thousands of trucks and that significant fuel savings can be achieved.

ABSTRACT. Model Predictive Control (MPC) schemes are commonly using reference-tracking cost functions, which have attractive properties in terms of stability and numerical im- plementation. However, many control applications have clear economic objectives that can be used directly as the MPC cost function. Such MPC schemes are labelled Economic MPC. The stability proof for economic MPC relies on strict dissipativity, which is in general difficult to establish. In contrast, tracking MPC has well-established and practically applicable stability guarantees, but can yield poor closed-loop performance in terms of the selected economic criterion. In this paper, we propose a strategy to tune tracking MPC schemes so as to locally approximate the behaviour of economic MPC while directly guaranteeing stability of the closed-loop system. We illustrate the theoretical developments in a simulated example.

ABSTRACT. EMPC will frequently operate with constraints active and is thus sensitive to disturbances. We here show how constraint adaptation can be used to improve closed loop performance for such cases. The adaptation introduces negligible overhead compared to standard MPC and is hence computationally attractive as compared to robust and stochastic EMPC. Since the adaptation is based on online measurements, it does not require \emph{a~priori} knowledge of the disturbances. We illustrate our method by means of an example.

ABSTRACT. This paper presents a model predictive controller (MPC) for smooth driving. We formulate an economic MPC (EMPC) where the second derivative of the curvature is minimized. We use the smoothness and comfort characteristics of clothoid-driving providing a very compact and intuitive controller formulation. We highlight the main modules of an autonomous vehicle architecture while explaining how the EMPC is integrated. In the end, we present experimental results where the controller is deployed on a Scania construction truck. Experimentally, we compare the EMPC with a pure-pursuit controller (PPC), both deployed in a Scania construction truck. We demonstrate the extreme accuracy of the EMPC both at high and low-speeds. We conclude that the EMPC outperforms the PPC.

ABSTRACT. This paper investigates transient performance of inverter-based microgrids in terms of the resistive power losses incurred in regulating frequency under persistent stochastic disturbances. We model the inverters as second-order oscillators and compare two algorithms for frequency regulation: the standard frequency droop controller and a distributed proportional- integral (PI) controller. The transient power losses can be quantified using an input-output H2 norm. We show that the distributed PI-controller, which has previously been proposed for secondary frequency control (the elimination of static errors), also has the potential to significantly improve performance by reducing transient power losses. This loss reduction is shown to be larger in a loosely interconnected network than in a highly interconnected one, whereas losses do not depend on connectivity if standard droop control is employed. Moreover, our results indicate that there is an optimal tuning of the distributed PI-controller for loss reduction. Overall, our results provide an additional argument in favor of distributed algorithms for secondary frequency control in microgrids.

ABSTRACT. This work considers a coverage problem for a team of robotic agents. The agents are assigned a set of landmarks in the plane to keep under observation, and they can adjust both their positions and orientations in order to collectively improve the visibility of the landmarks. For this problem, a distributed algorithm is proposed, and its convergence properties are demonstrated analytically. The results are corroborated by numerical simulations and experiments with aerial robots.

ABSTRACT. Bayesian system identification is a theoretically well-founded and currently emerging area. We describe and evaluate two recent state-of-the-art sample-based methods for Bayesian parameter inference from the statistics literature, particle Metropolis-Hastings (PMH) and SMC^2, and apply them to a non-trivial real world system identification problem with large uncertainty present. We discuss their different properties from a user perspective, and conclude that they show similar performance in practice, while PMH is significantly easier to implement than SMC^2.

ABSTRACT. The speed of a wheeled vehicle is usually estimated from wheel speed sensors or GPS. For applications where these are not available, we propose a method to estimate the wheel speeds based on chassi vibrations measured by accelerometers. The accelerometer measures vibrations from various sources, but wheel vibrations dominate, while other sources are considered disturbances. A state-space structure for modeling the vibrations is proposed, along with a variation of the Rao-Blackwellized point mass filter (RBPMF). The variant has decreased asymptotic complexity as compared to the conventional formulation. Subsequently, the method is tested on experimental data and compared with alternative methods. The results indicate that it is feasible to estimate vehicle speed using vibrations, and that the RBPMF is an improvement over other tested algorithms.

ABSTRACT. En turbinanläggning på 640 [MW] drivs av att elda naturgas. Spillvärmen från turbinerna används för att avsalta havsvatten. Avsaltningsanläggningen har byggts ut i tre steg. Vid det senaste steget installerades två destillationsenheter som vardera förbrukar 25 [kg/s] ånga. APC och processgruppen på ÅF-Industry i Göteborg har fått uppdraget att undersöka driftbarheten på anläggningen efter den senaste utbyggnationen. Detta undersöks genom fem simuleringar på en dynamisk modell skriven i SystemModeler, (Modelica).

ABSTRACT. In this paper we study controllability of large scale networks with controls which can assume only positive or negative values, not both. In particular, the problem of finding a minimal set of driver nodes is studied. Previous results on controllability with arbitrary input constraints are reformulated and it is found that this problem is equivalent to standard optimization problems. Given an adjacency matrix A, an algorithm is developed that constructs an input matrix B with a minimal number of columns such that the resulting system (A, B) is positively controllable.

ABSTRACT. A hybrid observer structure for a linear time-invariant continuous plant under an intrinsic pulse-modulated feedback is considered. The firing times of the feedback representing the discrete state of the hybrid system to be observed are thus inaccessible for measurement. The observer possesses two feedback gains making use of the continuous output estimation error in order to correct the estimates of the continuous and discrete states, respectively. By reducing the hybrid state estimation error, the observer solves a synchronization problem between the firing times of the plant pulse-modulated feedback and that of the observer. The observer design degrees of freedom influencing the observer performance are investigated by extensive numerical experiments. The introduction of the discrete observer gain clearly improves the observer performance for low multiplicity periodic solutions in the plant but not for those with high multiplicity. In the latter case, fast observer convergence is achieved even for a zero value of the discrete gain.

ABSTRACT. We consider the problem of finding exact statistical characterizations of the Bernoulli trials network size estimator, a simple algorithm for distributedly counting the number of agents in an anonymous communication network for which the probability of committing estimation errors scales down exponentially with the amount of information exchanged by the agents. The estimator works by cascading a local, randomized, voting step (i.e., the i.i.d. generation of some Bernoulli trials) with an average consensus on these votes. We derive a tight upper bound on the probability that this strategy leads to an incorrect estimate, and refine the offline procedure for selecting the Bernoulli trials success rate.

ABSTRACT. The paper proposes a control design method in order to gate input flow to a protected urban vehicular network such that travel time Quality of Service (QoS) constraints are preserved within the network. The proposed single region admission control policy hence guarantees that trips are completed within a predefined travel time constraint subject to the vehicular conservation law and the Network Fundamental Diagram (NFD). In view of the network to be protected, two types of queues are distinguished: external and internal. While external queues represent vehicles waiting to enter the protected network, an internal queue can be used to describe the network aggregated behavior. By controlling the number of vehicles in the internal queue, the travel time within the network can be subsequently controlled as well. The problem targeted in this paper is to find a balanced coordination of both external and internal queues to guarantee that vehicles will reach their destination within a guaranteed travel time within the network. The input flow to the network via the external queue(s) is gated. The admission controller can thus be interpreted as a mechanism which transforms the unknown arrival process governing the number of vehicles entering the network to a regulated process, such that prescribed QoS requirements on travel time in the network and upper bound on the external queue are satisfied. The admission control problem is formulated as a constrained optimization problem and it is solved via Quadratic Programming (QP) and Model Predictive Control (MPC). A case study demonstrates the benefits of the admission control mechanisms proposed.

ABSTRACT. A rigid-body model of a human finger interacting with a trackball is considered. The mathematical model consists of a ball with a spherical joint constraint, a finger with three degrees of freedom (DOF) and the Coulomb friction model. We derive a hybrid, high-index differential-algebraic equation for modeling the system dynamics which is used for both simulation and finding optimal trajectories.

ABSTRACT. Prognostics and health management is a useful tool for more flexible maintenance planning and increased system reliability. The application in this study is lead-acid battery failure prognosis for heavy-duty trucks which is important to avoid unplanned stops by the road. There are large amounts of data available, logged from trucks in operation, however, data is not closely related to battery health which makes battery prognostic challenging. Two features of the dataset has been identified, 1) few informative variables, and 2) highly correlated variables in the dataset. The main contribution is a novel method for identifying important variables, taking these two properties into account, using Random Survival Forests to estimate prognostics models. The result of the proposed method is compared to existing variable selection methods, and applied to a real-world automotive dataset. Prognostic models with all and reduced set of variables are generated and differences between the model predictions are discussed, and favorable properties of the proposed approach is highlighted.

ABSTRACT. Maximum likelihood (ML) estimation in nonlinear state space models (SSMs) is a challenging problem due to the analytical intractability of the log-likelihood. We explore the use of Newton optimisation to solve the ML estimation problem. Two different approximation methods are explored to estimate the intractable gradient and Hessian of the log-likelihood. Firstly, we make use of an approximation based on linearisation, which has a small computational cost but problem-specific accuracy. Secondly, we make use of a sampling approximation, which has a large computational cost but is consistent for and SSM. We benchmark both approaches in the different problems and obtain encouraging results.

ABSTRACT. We propose a novel approach to input design for identification of nonlinear state space models. The optimal input sequence is obtained by maximizing a scalar cost function of the Fisher information matrix. Since the Fisher information matrix is unavailable in closed form, it is estimated using particle methods. In addition, we make use of Gaussian process optimization to find the optimal input and to mitigate the problem of a large computational cost incurred by the particle filter, as the method reduces the number of functional evaluations. Numerical examples are provided to illustrate the performance of the resulting algorithm.

ABSTRACT. In this work we present an approach to use time of arrival measurements from an ultrawideband system. We model the ultrawideband measurements using a tailored heavy-tailed asymmetric distribution to account for measurement outliers. We use this distribution for estimating the position of a mobile transmitter using a multilateration approach. The resulting position estimates are shown to be accurate when compared to reference data from an independent optical tracking system.

ABSTRACT. En berättelse som beskriver nyttan av forskning på automatiska inställningsmetoder för parametrarna i PID-regulatorer.

ABSTRACT. In this paper, strong AC-grid connected VSC-HVDC systems are studied. Under specific conditions, such systems can suffer from both stability and poor damping related issues, which warrants a stability study. Analytical eigenvalue expressions can directly demonstrate the impact of physical or control parameters on the system stability. However, especially in case of high-order systems, such expressions are challenging to obtain. This paper suggests a method to symbolically represent approximative eigenvalues of two-terminal VSC-HVDC systems, which could also be used to analyze the system dynamics. In addition, by applying symbolic-isolation method, the order of a multi-terminal VSC-HVDC system can be reduced to an equivalent two-terminal VSC-HVDC system, which enables the proposed method to provide symbolic pole expressions. Numerical studies based on Matlab simulations are presented, showing the accuracy of the analytical eigenvalue expressions and providing useful hints on the impact of physical or control parameters.

ABSTRACT. The concept of string stability provides a notion of stability and performance of platoons of vehicles by characterizing the attenuation of disturbances as they propagate through a string of vehicles. However, existing notions of string stability do not allow for the practically relevant case of external disturbances on vehicles. In this extended abstract, we propose a notion of string stability that includes disturbances on all vehicles in the platoon and present a distributed controller that achieves disturbance string stability.

ABSTRACT. The platoon control problem is considered with a leader and predecessor following scheme. A novel velocity-dependent spacing policy is integrated into this scheme in order to improve robustness against the communication delay with the leader. The velocity dependence is adjusted in this scheme with a scalar parameter that appears as the constant time headway in the standard predecessor following scheme. The design problem is considered with a first-order vehicle model and a simple PD-type controller. It is shown that formation stability can always be ensured with sufficient emphasis on the predecessor information. On the other hand, string stability can be achieved only when the headway parameter is chosen larger than the communication delay. It is also outlined how the feedback gains and the emphasis on the leader can be adjusted for desirable performance.

ABSTRACT. The optimal speed trajectory for a heavy duty truck is calculated by using the Pontryagin's maximum principle. The truck motion depends on controllable tractive and braking forces and external forces such as air and rolling resistance and road slope. The solution is subject to restrictions such as maximum power and position dependent speed restrictions. The intended application is driving in environments with varying requirements on the velocity due to e.g. legal limits and traffic.

ABSTRACT. Reversing with general 2-trailer system is known to be a hard task for a driver without lots of training. In this paper we present a control and motion planning framework that can be used to automatically plan and execute complex maneuvers in a complex environment. A cascade control structure is used to stabilize the general 2-trailer system around piecewise linear reference paths. The controller consists of a gain-scheduled Linear Quadratic controller together with a high level pure pursuit path tracker. This controller together with a kinematic model of the general 2-trailer system is used to perform forward simulations within a Closed-Loop Rapidly Exploring Random Tree framework to generate feasible trajectories in forward and backward motion. The approach is evaluated in both simulations and on a small scale test platform in a lab environment.

ABSTRACT. This paper presents an approach to trajectory planning for automated lane change maneuvers based on model predictive control theory. The proposed lane change maneuver algorithm determines whether there exists a longitudinal trajectory which allows the vehicle to safely position itself in an inter-vehicle traffic gap in the target lane. If such a longitudinal trajectory exists, the algorithm plans the corresponding lateral trajectory. The trajectory planning problem can thereby be formulated as two loosely coupled convex quadratic programs for longitudinal and lateral control. The real-time ability of the proposed lane change maneuver algorithm to generate smooth collision-free trajectories is demonstrated in a Volvo V60 performing lane change maneuvers on a test track.

ABSTRACT. When computing the solution to Model Predictive Control (MPC) and Moving Horizon Estimation (MHE) problems, most computational effort is spent when solving a sequence of unconstrained finite-time optimal control (UFTOC) problems. Hence, methods for solving MPC and MHE problems rely on efficient algorithms for solving the UFTOC problems. Here, two recently proposed parallel algorithms are applied to solve the UFTOC problem arising in MHE problems. Numerical results from both a MATLAB implementation and a fully parallel ANSI-C implementation show promising results for problems with long prediction horizons.

ABSTRACT. Multiple-shooting and Direct collocation are two highly successful yet competing simultaneous approaches to transform continuous optimal control problems into Nonlinear Programs. Because of their respective specificities, they are often used in different contexts. Practitioners often favour multiple-shooting for real-time optimal control, as the structure of the underlying linear algebra can be easily exploited and parallelized. In contrast, they often prefer direct collocation for off-line optimal control, as it allows for a trivial deployment of an exact Hessian, and because of the nonlinear problem dynamics are very highly lifted, therefore providing strong convergence properties. However, the sparsity of the linear algebra underlying a transcription based on direct collocation is in general not straightforward to exploit. NLP solvers often rely on off-the-shelf sparse linear solvers, which do not always deliver optimal performances. In this paper, we show that multiple-shooting is in fact equivalent to direct collocation, provided that the underlying integrators are lifted. This observation allows one to deploy the very mature structure-exploiting linear algebra developed for multiple shooting in the context of direct collocation.

ABSTRACT. With the increasing use of wireless communication in networked control systems, the issue of control over unreliable channels has risen into prominence. As controllers, sensors and actuators often are positioned on different locations, it can be difficult or expensive to create reliable communication links between the components. Therefore, the question of control over lossy networks is one of increasing importance.

One solution to the issue of lossy channels is to use tree codes to characterize the submission of the data from the controller to the actuators, and from the sensors to the controller. By utilizing tree codes one can turn a lossy channel into a channel with a random delay. This delay will not be bounded, but follows a probability function that depends on the lossyness of the network. The problem that will be examined here has lossy channels between the controller and the actuators that are modified using tree codes into channels with a random delay. An optimal solution is derived for finite horizon discrete hold-input LQG control for this case. This solution is compared with standard LQG control in simulations, which demonstrate that a significant improvement in the cost can be achieved when the probability of delay is high.

ABSTRACT. We address H-infinity state feedback and give a simple form for an optimal control law applicable to linear time-invariant systems with symmetric and Hurwitz state matrix. More specifically, the control law can be expressed in the matrices of the system's state space representation, given separate cost on state and control input. The derived optimal control law is transparent, easy to synthesize and scalable. Examples demonstrate the scalability of the optimal control law and temperature control in buildings as a possible application area.

ABSTRACT. Deep Brain Stimulation (DBS) is a neurosurgical treatment in e.g. Parkinson’s Disease. DBS consists of delivering electrical pulses to a certain target in the brain through implanted electrodes. The goal is to stimulate a certain target while keeping the stimulation outside as small as possible to avoid possible side effects. Due to the difficulties of undergoing in vivo studies, mathematical models aiming at a better understanding of DBS have been developed in the past years. This work covers optimization of the electrical stimuli using clinical data and Finite Element Methods approach with two areas of interest: lead design and fault alleviation. Different lead designs were considered: row segmentation, i.e., dividing a cylindrical contact into several independent ones and the separation between rows. Due to the increased number of contacts and the complexities of hardware, faults are more likely to happen and their impact in the stimulation should be quantified and, if needed, alleviated. Results show that segmentation helps achieve a better target selectivity although higher amplitude is needed. Better results were achieved while keeping the contacts closer together. Two approaches were analyzed regarding fault alleviation: increasing amplitude and using a neighbouring contact. Both yielded satisfactory results, though care should be exercised since a high amplitude will reduce the battery life of the device and could be hazardous for the patient.

ABSTRACT. The method presented in this paper fuses information from a monocular camera, an inertial sensor and ultra wideband (UWB) sensors. It is shown that the complementary characteristics of these sensors enables improved pose estimates, without requiring the introduction of any visible infrastructure such as for example fiducial markers. A simultaneous localization and mapping (SLAM) framework is used to simultaneously estimate the pose of naturally occurring visual landmarks and the pose of a moving sensor platform. A real data experiment in a controlled indoor environment is conducted. The data come with high accurate ground truth and are used to show the benefit of the suggested sensor combination.

ABSTRACT. In this contribution we investigate the possibility of using magnetic field measurements for train localization. A signature, or fingerprint, is created using the magnetic field measurements and a speed estimate. This signature is then used for localization by employing different subsequence matching methods.

ABSTRACT. We aim at developing statistical tools that improve the accuracy and precision of the measurements returned by triangulation Light Detection and Rangings (Lidars). To this aim we: i) propose and validate a novel model that describes the statistics of the measurements of these Lidars, and that is built starting from mechanical considerations on the geometry and properties of their pinhole lens - CCD camera systems; ii) build, starting from this novel statistical model, a Maximum Likelihood (ML) / Akaike Information Criterion (AIC) - based sensor calibration algorithm that exploits training information collected in a controlled environment; iii) develop ML and Least Squares (LS) strategies that use the calibration results to statistically process the raw sensor measurements in non controlled environments. The overall technique allowed us to obtain empirical empirical improvements of the normalized Mean Squared Error (MSE) from 0:0789 to 0:0046.

ABSTRACT. In most target tracking problems, the solutions rely heavily on the assumption that the timestamps for all measurements are known. However, in addition to traditional measurements, sometimes there are observations available that lack timestamps but are very accurate otherwise. The observations could, \eg, be tracks left by a target and found at a later time or statements from an eyewitness having observed some event, which can in conjunction with traditional measurements be used to improve the accuracy of the estimated trajectory. In this paper we examine the implications of having a stochastic timestamp for one observation and derive a Bayesian smoother for a linear Gaussian model, where a prior is put on the timestamp to capture any information known about the time. The marginal and joint posteriors for the states and timestamp are derived and the maximum a posteriori (MAP) and minimum mean squared error (MMSE) estimators are applied. The aim of the paper is to provide an intuition for the problem by considering a very simple one-dimensional random walk model. A discussion on the effects of the stochastic timestamp and a comparison of the posteriors and estimators rounds up the paper.

ABSTRACT. We propose nested sequential Monte Carlo (NSMC), a methodology to sample from sequences of probability distributions, even where the random variables are high-dimensional. NSMC generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm. Furthermore, NSMC can in itself be used to produce such properly weighted samples. Consequently, one NSMC sampler can be used to construct an efficient high-dimensional proposal distribution for another NSMC sampler, and this nesting of the algorithm can be done to an arbitrary degree. This allows us to consider complex and high-dimensional models using SMC. We show results that motivate the efficacy of our approach on several filtering problems with dimensions in the order of 100 to 1 000.

ABSTRACT. In this paper, we study the problem of tracking and encircling a moving target by agents in 3D. Specifically, a group of agents are driven to some desired formation on a spherical surface and simultaneously keep the center of this spherical formation coinciding with the target to be tracked. In our control design, the desired formation is not used as a reference signal for tracking. Rather by designing communication topology for the agents we can achieve the desired formation using relative positions only. We can also place the desired cyclic formation on the equator if the north pole is specified.

ABSTRACT. In the application of the Expectation Maximization algorithm to identification of dynamical systems, internal states are typically chosen as latent variables, for simplicity. In this work, we propose a different choice of latent variables, namely, system disturbances. Such a formulation elegantly handles the problematic case of singular state space models, and is shown, under certain circumstances, to improve the fidelity of bounds on the likelihood, leading to convergence in fewer iterations. To access these benefits we develop a Lagrangian relaxation of the nonconvex optimization problems that arise in the latent disturbances formulation, and proceed via semidefinite programming.

ABSTRACT. Aquaculture, the farming of aquatic species, is classically done in natural bodies of water. Land-based recirculating aquaculture systems for fish farming give benefits over traditional methods such as reduced nutrient emissions, but are difficult to develop and design, much due to the very large time constants in the system which makes experimentation impractical and costly. Realistic and reliable simulation is therefore very useful in the planning and operation of such systems.

A simulator for RAS is being developed in the modeling language Modelica. A user-friendly drag and drop plant building tool together with a robust back-end is being built using the same underlying equations as a previously implemented RAS simulator. Extensions include energy balances and optimization capabilities, which will allow optimal design of (in particular) plants for cold-water species. Wolffish and lobster are two species of interest for the simulator, and experiments to obtain model parameters are being planned at a pilot-scale salmon plant in Kungshamn.

ABSTRACT. Cloud computing technology provides the means to share physical resources among multiple users and data center tenants by exposing them as virtual resources. There is a strong industrial drive to use similar technology and concepts to provide timing sensitive services. One such is virtual networking services, so called services chains, which consist of several interconnected virtual network functions. This allows for the capacity to be scaled up and down by adding or removing virtual resources. In this work, we develop a model of a service chain and pose the allocation of resources as an optimisation problem.

ABSTRACT. The use of high-order polynomial models that are linear in the parameters is common in system identification to avoid the non-convexity of the prediction error method when applied to other model structures. A common and fairly general case is to use high-order ARX models to approximate Box-Jenkins structures. Then, a well known correspondence is made between the ARX polynomials and the plant and noise models in the Box-Jenkins structure. However, this commonly used result is only valid when the Box-Jenkins predictor is stable. In this contribution, we generalize these results to allow for unstable predictors due to an unstable plant. We show that high-order ARX models are appropriate for this situation as well. However, corrections must be made to correctly retrieve the noise model and noise variance.

ABSTRACT. We consider the problem of low-rank approximation with convex constraints. Given a certain data matrix, this problem consists of finding a low-rank approximation of desired rank, which fulfills the convex constraints and minimizes the distance to the data matrix in the Frobenius norm. Such problems appear manifold in the areas of data analysis, image compression and many more. Moreover, the problem of matrix completion can be considered as a particular instance.

ABSTRACT. This paper considers a class of structured separable convex optimization problems, motivated by deploying Model Predictive Control (MPC) on multi-agent systems that are interacting via non-delayed couplings. For this class of problems, we show that the Hessian of the dual function is numerically structured. The numerical structure allows for deploying a quasi-Newton method in the dual space. For large problems, this approach yield a large reduction of the computational complexity of solving the problem, and for geographically distributed problems a reduction in the communication burden.

ABSTRACT. Hidden Markov Models (HMMs) are widely used in applications and theoretical frameworks as models for stochastic time series. Most of the current methods employed for identification of the parameters needed to specify an HMM utilize iterative likelihood maximization schemes. Such methods may experience problems with slow and/or only local convergence. We explore an alternative approach employing a method of moments. The special case of known sensor uncertainties is considered which allows the problem to be cast as a convex optimization problem. We provide theoretical guarantees for consistency, as well as numerical simulations indicating that the method can be orders of magnitude faster than a standard maximum likelihood procedure. The conclusion is that the method of moments can be a viable option in applications where run-time is a critical variable.

ABSTRACT. This work presents a fully automated procedure for controller synthesis for multi-agent systems under coupled constraints. Each agent has dynamics consisting of two terms: the first one models the coupled constraints and the other one is an additional control input. We aim to design the inputs so that each agent meets a high-level task specification given in Metric Interval Temporal Logic (MITL). First, we design a decentralized abstraction that provides a time and space discretization of the multi-agent system. Second, by utilizing this abstraction and techniques from formal verification, we provide an algorithm that computes the individual runs provably satisfying the high-level tasks.

ABSTRACT. Event-based sensing and communication holds the promise of lower resource utilization and/or better performance in remote state estimation applications such as wireless sensor networks. Recently, stochastic event-triggering rules in an integrated event-generator in the sensor have been proposed as a means to avoid the complexity of the problem that normally arises in event-based observer design. Applying a scaled Gaussian function in the event generator, the optimal remote state estimator becomes a linear, time-varying Kalman filter. We propose a new stochastic triggering rule, namely stochastic send-on-delta sampling with time-lag dependence. The idea is to use a very simple predictor in the sensor, which allows the communication rate to be reduced with preserved estimation performance compared to regular stochastic send-on-delta sampling. The proposed scheme also compares favorably to two other previously proposed stochastic sampling approaches in numerical examples.

ABSTRACT. This contrinution introduces a simulation-based method for maximum likelihood estimation of stochastic Wiener systems. It is well known that the likelihood function of the observed outputs for the general class of stochastic Wiener systems is analytically intractable. However, when the distributions of the process disturbance and the measurement noise are available, the likelihood can be approximated by running a Monte-Carlo simulation on the model. We suggest the use of Laplace importance sampling techniques for the likelihood approximation. The algorithm is tested on a simple first order linear example which is excited only by the process disturbance. Further, we demonstrate the algorithm on an FIR system with cubic nonlinearity. The performance of the algorithm is compared to the maximum likelihood method and other recent techniques

ABSTRACT. The aim of this article is to present a survey on inspection applications of Pneumatic Wall-Climbing Robots (PWCR). In general, a PWCR utilizes negative pressure as its adhesion method, through mainly suction cups or negative pressure thrust-based mechanisms. Their main advantage being their ability to climb non-ferromagnetic surfaces, such as glass and composite materials, in comparison with climbing robots based on magnetic adhesion methods. A growing application area is the utilization of PWCRs for inspection purposes for accelerating the otherwise time consuming procedures of manual inspection, while offering the important advantage of protecting human workers from hazardous and/or unreachable environments.

ABSTRACT. This note presents a secure scheme for the stabilisation of continuous-time linear time-invariant systems under sensor and actuator attacks.

ABSTRACT. In this paper a cooperative fuel and comfort efficient method featuring a collision avoidance scheme is presented to control multiple autonomous vehicles in different traffic scenarios. It is shown that the strategy can be applied to control different groups of vehicles with different dynamics. Simulation results are used to illustrate performance and generality properties of the proposed approach.

ABSTRACT. In this article a distributed model predictive control scheme, for the cooperative motion control of multi agent system is being presented. Two different control architectures: a centralized and a distributed MPC, are studied and evaluated in simulation experiments. The efficiency of the overall suggested distributed MPC scheme, as well as it comparison with the centralized approach, is being evaluated through the utilization of multiple simulation scenarios.

ABSTRACT. Being able to estimate parameters or to detect and isolate faults are critical issues to improve the performance of an Unmanned Aerial Vehicle (UAV). In this paper, a recently proposed method is applied to a simplified simulated UAV system and recursive parameter estimation as well as sensor fault detection are done via a bank of Extended Kalman Filters (EKFs). Besides, some problems that concern model errors, varying disturbance levels and closed-loop data are also discussed. The results indicate that the method is able to monitor several types of faults as well as the parameter changes of the system.

ABSTRACT. We consider the influences of uncertainty on the application-oriented input design problem. In the input design problem, the objective is to design an input signal to be used in an identification experiment which provides a model with acceptable control performance. The problem is usually formulated as a constrained optimization problem, where the constraint relies on the parameters to be estimated. Based on a risk theoretical approach, the uncertainty on the parameters is measured and a robust application-oriented input design problem is presented.

ABSTRACT. Dynamical networks, composed of dynamical subsystems that interact according to a given interconnection topology, can model several complex systems, ranging from natural systems to engineering applications. Dynamical networks admit a graph representation, where nodes represent subsystems and arcs represent interconnections, and have a peculiar feature: the global behaviour is the outcome of an ensemble of local interactions. A structural analysis of dynamical networks aims at providing parameter-independent results, based on the topology of the interconnection graph (i.e., the system structure); a structural approach is particularly well suited to explain the extraordinary robustness of natural systems in spite of intrinsic uncertainties and variability. We present the BDC-decomposition, which describes the structure of a wide class of dynamical networks: we show that it provides not only a local, but also a global description of the system, and that it can help structurally assess many relevant properties. In particular, we focus on a BDC-based vertex algorithm that allows us to analyse the system steady-state input-output behaviour and to compute the system influence matrix, representing the structural steady-state effect on each of the variables of a persistent input applied to each of the system equations.

ABSTRACT. In this work we present currently ongoing work about robust calibration for triaxial MEMS accelerometers. A rough outlier rejection is performed initially by fitting an ellipsoid to the calibration data and removing measurements that are too far from the surface of this ellipsoid. This is combined with using a robust extended Kalman filter and a gyroscope to estimate the orientation of the sensor when solving the calibration problem. Results from simulations and an experiment using real sensors show that the robust method performs better than our previous method, when outliers are present in the calibration data.

ABSTRACT. This is a tutorial for introducing the particle Metropolis-Hastings (PMH) algorithm for Bayesian parameter inference in non-linear state space models (SSMs). The aim is to provide the participants with the necessary knowledge to implement the PMH algorithm in MATLAB. Throughout the tutorial, we discuss the theory underpinning the algorithm and provide some intuition for each of the steps in the algorithm. The PMH implementation is applied for parameter inference in a real-world data set of the number of annual major Earthquakes. We conclude the tutorial by discussing possible improvements to the algorithm and by giving a list of suitable references for further study.