L. Giovanini

Autonomous and decentralized mission planning for clusters of UUVs

This paper proposes an algorithm for autonomous strategic mission planning of missions where multiple unhabitated underwater vehicles (UUVs) cooperate in order to solve one or more mission tasks. Missions of this type include multi-agent reconnaissance missions and multi-agent mine sweeping missions. The mission planning problem is posed as a receding horizon mixed–integer constrained quadratic optimal control problem. This problem is subsequently partitioned into smaller subproblems and solved in a parallel and decentralized manner using a distributed Nash-based game approach. The paper presents the development of the proposed algorithm and discusses its properties. An application example is used to further demonstrate the main characteristics of the proposed method.

Low-level flexible-structure control applied to heat exchanger networks

A low-level flexible-structure control is proposed for designing control systems capable of efficiently handling constraints on the manipulated variables of heat exchanger networks (HENs). Flexible-structure refers to the capability of the resulting control system to switch from one closed-loop structure to another in order to keep regulation, and low-level means that it can be configured in most distributed control systems. This control approach is useful to hold the operating point close to an optimum when optimal conditions are located on the constraints. The application example compares the approach with the use of a more involved strategy.

A fault detection and isolation filter for discrete linear systems

The problem of fault and/or abrupt disturbances detection and isolation for discrete linear systems is analyzed in this work. A strategy for detecting and isolating faults and/or abrupt disturbances is presented. The strategy is an extension of an already existing result in the continuous time domain to the discrete domain. The resulting detection algorithm is a Kalman filter with a special structure. The filter generates a residuals vector in such a way that each element of this vector is related with one fault or disturbance. Therefore the effects of the other faults, disturbances, and measurement noises in this element are minimized. The necessary stability and convergence conditions are briefly exposed. A numerical example is also presented.

Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach

BackgroundThe spread of an infectious disease is determined by biological and social factors. Models based on cellular automata are adequate to describe such natural systems consisting of a massive collection of simple interacting objects. They characterize the time evolution of the global system as the emergent behaviour resulting from the interaction of the objects, whose behaviour is defined through a set of simple rules that encode the individual behaviour and the transmission dynamic.MethodsAn epidemic is characterized trough an individual–based–model built upon cellular automata. In the proposed model, each individual of the population is represented by a cell of the automata. This way of modeling an epidemic situation allows to individually define the characteristic of each individual, establish different scenarios and implement control strategies.ResultsA cellular automata model to study the time evolution of a heterogeneous populations through the various stages of disease was proposed, allowing the inclusion of individual heterogeneity, geographical characteristics and social factors that determine the dynamic of the desease. Different assumptions made to built the classical model were evaluated, leading to following results: i) for low contact rate (like in quarantine process or low density population areas) the number of infective individuals is lower than other areas where the contact rate is higher, and ii) for different initial spacial distributions of infected individuals different epidemic dynamics are obtained due to its influence on the transition rate and the reproductive ratio of disease.ConclusionsThe contact rate and spatial distributions have a central role in the spread of a disease. For low density populations the spread is very low and the number of infected individuals is lower than in highly populated areas. The spacial distribution of the population and the disease focus as well as the geographical characteristic of the area play a central role in the dynamics of the desease.

Distributed Model Predictive Control Based on Dynamic Games

Model predictive control (MPC) is widely recognized as a high performance, yet practical, control technology. This model-based control strategy solves at each sample a discrete-time optimal control problem over a finite horizon, producing a control input sequence. An attractive attribute of MPC technology is its ability to systematically account for system constraints. The theory of MPC for linear systems is well developed; all aspects such as stability, robustness,feasibility and optimality have been extensively discussed in the literature (see, e.g., (Bemporad & Morari, 1999; Kouvaritakis & Cannon, 2001; Maciejowski, 2002; Mayne et al., 2000)). The effectiveness of MPC depends on model accuracy and the availability of fast computational resources. These requirements limit the application base for MPC. Even though, applications abound in process industries (Camacho & Bordons, 2004), manufacturing (Braun et al., 2003), supply chains (Perea-Lopez et al., 2003), among others, are becoming more widespread. Two common paradigms for solving system-wide MPC calculations are centralised and decentralised strategies. Centralised strategies may arise from the desire to operate the system in an optimal fashion, whereas decentralised MPC control structures can result from the incremental roll-out of the system development. An effective centralised MPC can be difficult, if not impossible to implement in large-scale systems (Kumar & Daoutidis, 2002; Lu, 2003). In decentralised strategies, the system-wide MPC problem is decomposed into subproblems by taking advantage of the system structure, and then, these subproblems are solved independently. In general, decentralised schemes approximate the interactions between subsystems and treat inputs in other subsystems as external disturbances. This assumption leads to a poor systemperformance (Sandell Jr et al., 1978; Siljak, 1996). Therefore, there is a need for a cross-functional integration between the decentralised controllers, in which a coordination level performs steady-state target calculation for decentralised controller (Aguilera & Marchetti, 1998; Aske et al., 2008; Cheng et al., 2007; 2008; Zhu & Henson, 2002). Several distributed MPC formulations are available in the literature. A distributed MPC framework was proposed by Dumbar and Murray (Dunbar & Murray, 2006) for the class 4

Model predictive control with amplitude and rate actuator saturation

In this work we show that the anti-wind-up-bumpless-transfer controller emerges from the structure of model predictive control (MPC) with quadratic objective and input constrains. The key to establish that relationship is the application of optimality conditions to the equivalent optimal control problem. The proposed framework employs a model of physical constraints as part of the controller architecture to ensure that the commands sent to the actuator do not exceed their specific limits and the internal states of the controller are well updated. Numerical examples are presented for illustrating the proposed control design methodology.

Distributed control of underwater vehicles

Despite the closed-loop performance of centralized multi-variable controllers, the vast majority of control applications are still based on decentralized controllers. Because of their single-loop structure, decentralized controllers cannot suppress interactions of the system, which are only taken into account in the controller tuning phase. Therefore, it would be useful in many cases to tackle the undesirable effects of the interactions on the closed-loop system. A novel model predictive control scheme based on Nash optimality is presented to achieve this goal. In this algorithm the centralized optimization is decomposed into that of several small coupled optimization problems. The relevant computational convergence, the closed-loop performance, and the effect of communication failures on the closed-loop behaviour are analysed. The control problem is illustrated to verify the effectiveness and practicality of the proposed control algorithm.

Adaptive control using multiple models switching and tuning

The purpose of this paper is to marry the two concepts of multiple model adaptive control and safe adaptive control. In its simplest form, multiple model adaptive control involves a supervisory switching among one of a finite number of controllers as more is learnt about the plant, until one of the controllers is finally selected and remains unchanged. Safe adaptive control is concerned with ensuring that when the controller is changed the closed-loop is never unstable. This paper introduces a receding horizon multiple model, switching and tuning control scheme based on an on-line redesign of the controller. This control scheme has a natural two-stage adaptive control algorithm: identification of the closest model and design of the control law. The computational complexity aspects of this approach to adaptive control are discussed briefly. A nonlinear system is used to illustrate the ideas

A real-time algorithm for acoustic monitoring of ingestive behavior of grazing cattle

A novel algorithm for monitoring the livestock grazing behavior is proposed.The three basic grazing events are detected and classified using acoustic signals.The algorithm shows robustness to different operational conditions.It has linear computational complexity and works fully automatically in real-time. Assessment of both grazing behavior and herbage intake are two very difficult tasks that can be concurrently accomplished by means of accurate detection, classification and measurement of grazing events such as chews, bites and chew-bites. It is well known that acoustic monitoring is among the best methods to automatically quantify and classify ingestive and rumination events in grazing animals. However, most existing methods of signal analysis appear to be computationally complex and costly, and are therefore difficult to implement. In this work, we present and test a novel analysis system called Chew-Bite Real-Time Algorithm (CBRTA) that works fully automatically in real-time to detect and classify ingestive events of grazing cattle. The system employs a directional wide-frequency microphone facing inwards on the forehead of animals, and a coupled signal analysis and decision logic algorithm that measures shape, amplitude, duration and energy of sound signals to iteratively detect and classify ingestive events. Performance and validation of the CBRTA was determined using two databases of grazing signals. Signals were recorded on dairy cows offered either, natural pasture ( N = 25 ), or experimental micro-swards in indoor controlled environment ( N = 50 ). The CBRTA exhibited a simple linear complexity capable to execute 50 times faster than real-time and without undermining overall recognition rate and accuracy when signals were processed at 4kHz sampling frequency and 8bits quantization. Furthermore, CBRTA was capable to detect ingestive events with a 97.4% success rate, while achieving up to 84.0% success for their classification as exclusive chews, bites or composite chew-bites. The methodology proposed with CBRTA has promising application in embedded microcomputer systems that necessarily depend on fast real-time execution to minimize computational load, power source and storage memory. Such a system can readily facilitate the transmission of processed data through wireless network or the storage in an onboard device.

Flexible-structure control: a strategy for releasing input constraints.

In this paper output unreachability under input saturation phenomenon is studied: under a large disturbance or setpoint change, the process output may never reach the set point even when the manipulated variable has driven to saturation. The process output can be brought back to the set point only by activating an auxiliary manipulated variable. A new control structure for designing and implementing a control system capable of solving this problem is proposed by transferring the control from one variable to another and taking into account the different dynamics involved in the system. The control structure, called flexible-structure control due to its ability to adapt the control structure to the operating conditons, is a generalization of the split-range control. It can be summarized as two controllers connected through a piecewise linear function. This function decides, based on the value of one manipulated variable, when and how the control structure changes. Its parameters control the interaction between both manipulated variables and leave the capability for handling the balance between control quality and other goals to the operator.