Isabel Jurado

Formation Control of Autonomous Underwater Vehicles Subject to Communication Delays

This paper addresses the formation control problem for fleets of autonomous underwater vehicles (AUVs). The solution is based on the virtual leader approach, with the main goal of designing a control system to cope with the inter-vehicle communication problems, especially significant in underwater environments. The use of kinematic relations allows the linearization of the AUV dynamics maintaining its turning capacities. The control strategy consists of a feedback H2/H∞ controller in combination with a feedforward controller, which makes it possible to deal with delays and packets dropouts while ensuring a good formation control performance.

Distributed estimation in networked systems under periodic and event-based communication policies

This papers aim is to present a novel design technique for distributed estimation in networked systems. The problem assumes a network of interconnected agents each one having partial access to measurements from a linear plant and broadcasting their estimations to their neighbours. The objective is to reach a reliable estimation of the plant state from every agent location. The observers structure implemented in each agent is based on local Luenberger-like observers in combination with consensus strategies. The paper focuses on the following network related issues: delays, packet dropouts and communication policy (time and event-driven). The design problem is solved via linear matrix inequalities and stability proofs are provided. The technique is of application for sensor networks and large scale systems where centralized estimation schemes are not advisable and energy-aware implementations are of interest. Simulation examples are provided to show the performance of the proposed methodologies.

Stability of Sequence-Based Control With Random Delays and Dropouts

We study networked control of nonlinear systems where system states and tentative plant input sequences are transmitted over unreliable communication channels. The sequences are calculated recursively by using a pre-designed nominally stabilizing state-feedback control mapping to plant state predictions. The controller does not require receipt acknowledgments or knowledge of delay or dropout distributions. For the i.i.d. case, in which case the numbers of consecutive dropouts are geometrically distributed, we show how the resulting closed loop system can be modeled as a Markov nonlinear jump system and establish sufficient conditions for stochastic stability.

Adaptive controller placement for wireless sensor-actuator networks with erasure channels

Wireless sensor-actuator networks offer flexibility for control design. One novel element which may arise in networks with multiple nodes is that the role of some nodes does not need to be fixed. In particular, there is no need to pre-allocate which nodes assume controller functions and which ones merely relay data. We present a flexible architecture for networked control using multiple nodes connected in series over analog erasure channels without acknowledgments. The control architecture proposed adapts to changes in network conditions, by allowing the role played by individual nodes to depend upon transmission outcomes. We adopt stochastic models for transmission outcomes and characterize the distribution of controller location and the covariance of system states. Simulation results illustrate that the proposed architecture has the potential to give better performance than limiting control calculations to be carried out at a fixed node.

Stock management in hospital pharmacy using chance-constrained model predictive control

One of the most important problems in the pharmacy department of a hospital is stock management. The clinical need for drugs must be satisfied with limited work labor while minimizing the use of economic resources. The complexity of the problem resides in the random nature of the drug demand and the multiple constraints that must be taken into account in every decision. In this article, chance-constrained model predictive control is proposed to deal with this problem. The flexibility of model predictive control allows taking into account explicitly the different objectives and constraints involved in the problem while the use of chance constraints provides a trade-off between conservativeness and efficiency. The solution proposed is assessed to study its implementation in two Spanish hospitals.

Networked predictive control of systems with data dropouts

This paper presents a practical algorithm to design networked control systems able to cope with high data dropout rates. The algorithm is intended for application in packet based networks protocols (Ethernet-like) where data packets typically content large data fields. The key concept is using such packets to transmit not only the current control signal, but predictions on a finite horizon without significantly increasing traffic load. Thus, predictive control is used together with buffered actuators and a state estimator to compensate for eventual packet dropouts. Additionally, some ideas are proposed to decrease traffic load, limiting packet size and media access frequency. Simulation results on the control of a three-tank system are given to illustrate the effectiveness of the method.

Stochastic MPC with applications to process control

This paper presents a model predictive control formulation for Networked Control Systems subject to independent and identically distributed delays and packet dropouts. The design takes into account the presence of a communication network in the control loop, resorting to a buffer at the actuator side to store and consistently apply delayed control sequences when fresh control inputs are not available. The proposed approach uses a statistical description of transmissions to optimise the expected future control performance conditioned upon the current system state, previously calculated control packets and transmission acknowledgements. Experimental studies using a quadruple tank process illustrate the applicability of the method to process control.

Dynamic Controller Allocation for Control over Erasure Channels

Wireless sensor-actuator networks offer flexibility in the design of networked control systems. One novel element when using networks with multiple nodes is that the role of individual nodes does not need to be fixed. In particular, there is no need to pre-allocate which nodes assume controller functions and which ones merely relay data. We present a flexible architecture for networked control using multiple nodes connected in series over analog erasure channels. The control architecture adapts to changes in network conditions, by allowing the role played by individual nodes to depend upon transmission outcomes.

An H∞ suboptimal robust control approach for systems with uncertainties and data dropouts

This paper studies the design of control systems subject to plant uncertainties and data losses in the channel connecting the plant sensor with the controller. The controller design has two main objectives. The first one is to robustify the control law against plant uncertainties. The other one is to achieve good performance by minimising the variance of the error signal. Data losses are modelled as an independent and identically distributed sequence of Bernoulli random variables. For analysis and design, this random variable is replaced by an additive noise plus gain channel model. To cope with structural uncertainties in the model of the plant, an H∞ control technique is employed. The controller is synthesised in order to make the closed-loop system robust against structural uncertainties of the nominal model, while achieving optimal performance of the system in the presence of dropouts.

Application of robust model predictive control to inventory management in hospitalary pharmacy

Inventory management is one of the main tasks that the pharmacy department has to carry out in a hospital. It is a complex problem that requires to establish a tradeoff between different and contradictory optimization criteria. The complexity of the problem is increased due to the constraints that naturally arise in this type of applications. In this paper, which corresponds to preliminary works performed to implement robust and advanced control techniques for pharmacy management in two Spanish hospitals, we propose, assess and compare three robust model predictive control(Chance-Constraints, Multi-Scenarios approach and Tree-Based Methods) as a mean to relieve this issue.