An hybrid methodology for RL-based behavior coordination in a target following mission with an AUV

Proposes a behavior-based scheme for high-level control of autonomous underwater vehicles (AUVs). Two main characteristics can be highlighted in the control scheme. Behavior coordination is done through a hybrid methodology, which takes in advantages of the robustness and modularity in competitive approaches, as well as optimized trajectories

Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

Dynamic management and restoration of virtual paths in broadband networks based on distributed software agents

La gestió de xarxes és un camp molt ampli i inclou molts aspectes diferents. Aquesta tesi doctoral està centrada en la gestió dels recursos en les xarxes de banda ampla que disposin de mecanismes per fer reserves de recursos, com per exemple Asynchronous Transfer Mode (ATM) o Multi-Protocol Label Switching (MPLS). Es poden establir xarxes lògiques utilitzant els Virtual Paths (VP) d'ATM o els Label Switched Paths (LSP) de MPLS, als que anomenem genèricament camins lògics. Els usuaris de la xarxa utilitzen doncs aquests camins lògics, que poden tenir recursos assignats, per establir les seves comunicacions. A més, els camins lògics són molt flexibles i les seves característiques es poden canviar dinàmicament. Aquest treball, se centra, en particular, en la gestió dinàmica d'aquesta xarxa lògica per tal de maximitzar-ne el rendiment i adaptar-la a les connexions ofertes. En aquest escenari, hi ha diversos mecanismes que poden afectar i modificar les característiques dels camins lògics (ample de banda, ruta, etc.). Aquests mecanismes inclouen els de balanceig de la càrrega (reassignació d'ample de banda i reencaminament) i els de restauració de fallades (ús de camins lògics de backup). Aquests dos mecanismes poden modificar la xarxa lògica i gestionar els recursos (ample de banda) dels enllaços físics. Per tant, existeix la necessitat de coordinar aquests mecanismes per evitar possibles interferències. La gestió de recursos convencional que fa ús de la xarxa lògica, recalcula periòdicament (per exemple cada hora o cada dia) tota la xarxa lògica d'una forma centralitzada. Això introdueix el problema que els reajustaments de la xarxa lògica no es realitzen en el moment en què realment hi ha problemes. D'altra banda també introdueix la necessitat de mantenir una visió centralitzada de tota la xarxa. En aquesta tesi, es proposa una arquitectura distribuïda basada en un sistema multi agent. L'objectiu principal d'aquesta arquitectura és realitzar de forma conjunta i coordinada la gestió de recursos a nivell de xarxa lògica, integrant els mecanismes de reajustament d'ample de banda amb els mecanismes de restauració preplanejada, inclosa la gestió de l'ample de banda reservada per a la restauració. Es proposa que aquesta gestió es porti a terme d'una forma contínua, no periòdica, actuant quan es detecta el problema (quan un camí lògic està congestionat, o sigui, quan està rebutjant peticions de connexió dels usuaris perquè està saturat) i d'una forma completament distribuïda, o sigui, sense mantenir una visió global de la xarxa. Així doncs, l'arquitectura proposada realitza petits rearranjaments a la xarxa lògica adaptant-la d'una forma contínua a la demanda dels usuaris. L'arquitectura proposada també té en consideració altres objectius com l'escalabilitat, la modularitat, la robustesa, la flexibilitat i la simplicitat. El sistema multi agent proposat està estructurat en dues capes d'agents: els agents de monitorització (M) i els de rendiment (P). Aquests agents estan situats en els diferents nodes de la xarxa: hi ha un agent P i diversos agents M a cada node; aquests últims subordinats als P. Per tant l'arquitectura proposada es pot veure com una jerarquia d'agents. Cada agent és responsable de monitoritzar i controlar els recursos als que està assignat. S'han realitzat diferents experiments utilitzant un simulador distribuït a nivell de connexió proposat per nosaltres mateixos. Els resultats mostren que l'arquitectura proposada és capaç de realitzar les tasques assignades de detecció de la congestió, reassignació dinàmica d'ample de banda i reencaminament d'una forma coordinada amb els mecanismes de restauració preplanejada i gestió de l'ample de banda reservat per la restauració. L'arquitectura distribuïda ofereix una escalabilitat i robustesa acceptables gràcies a la seva flexibilitat i modularitat.

Integration of knowledge-based, qualitative and numeric tools for real time dynamic systems supervision

The proposal presented in this thesis is to provide designers of knowledge based supervisory systems of dynamic systems with a framework to facilitate their tasks avoiding interface problems among tools, data flow and management. The approach is thought to be useful to both control and process engineers in assisting their tasks. The use of AI technologies to diagnose and perform control loops and, of course, assist process supervisory tasks such as fault detection and diagnose, are in the scope of this work. Special effort has been put in integration of tools for assisting expert supervisory systems design. With this aim the experience of Computer Aided Control Systems Design (CACSD) frameworks have been analysed and used to design a Computer Aided Supervisory Systems (CASSD) framework. In this sense, some basic facilities are required to be available in this proposed framework: ·

Modelling, control and supervision for a class of hybrid systems

The aim of this thesis is to narrow the gap between two different control techniques: the continuous control and the discrete event control techniques DES. This gap can be reduced by the study of Hybrid systems, and by interpreting as Hybrid systems the majority of large-scale systems. In particular, when looking deeply into a process, it is often possible to identify interaction between discrete and continuous signals. Hybrid systems are systems that have both continuous, and discrete signals. Continuous signals are generally supposed continuous and differentiable in time, since discrete signals are neither continuous nor differentiable in time due to their abrupt changes in time. Continuous signals often represent the measure of natural physical magnitudes such as temperature, pressure etc. The discrete signals are normally artificial signals, operated by human artefacts as current, voltage, light etc. Typical processes modelled as Hybrid systems are production systems, chemical process, or continuos production when time and continuous measures interacts with the transport, and stock inventory system. Complex systems as manufacturing lines are hybrid in a global sense. They can be decomposed into several subsystems, and their links. Another motivation for the study of Hybrid systems is the tools developed by other research domains. These tools benefit from the use of temporal logic for the analysis of several properties of Hybrid systems model, and use it to design systems and controllers, which satisfies physical or imposed restrictions. This thesis is focused in particular types of systems with discrete and continuous signals in interaction. That can be modelled hard non-linealities, such as hysteresis, jumps in the state, limit cycles, etc. and their possible non-deterministic future behaviour expressed by an interpretable model description. The Hybrid systems treated in this work are systems with several discrete states, always less than thirty states (it can arrive to NP hard problem), and continuous dynamics evolving with expression: with Ki ¡ Rn constant vectors or matrices for X components vector. In several states the continuous evolution can be several of them Ki = 0. In this formulation, the mathematics can express Time invariant linear system. By the use of this expression for a local part, the combination of several local linear models is possible to represent non-linear systems. And with the interaction with discrete events of the system the model can compose non-linear Hybrid systems. Especially multistage processes with high continuous dynamics are well represented by the proposed methodology. Sate vectors with more than two components, as third order models or higher is well approximated by the proposed approximation. Flexible belt transmission, chemical reactions with initial start-up and mobile robots with important friction are several physical systems, which profits from the benefits of proposed methodology (accuracy). The motivation of this thesis is to obtain a solution that can control and drive the Hybrid systems from the origin or starting point to the goal. How to obtain this solution, and which is the best solution in terms of one cost function subject to the physical restrictions and control actions is analysed. Hybrid systems that have several possible states, different ways to drive the system to the goal and different continuous control signals are problems that motivate this research. The requirements of the system on which we work is: a model that can represent the behaviour of the non-linear systems, and that possibilities the prediction of possible future behaviour for the model, in order to apply an supervisor which decides the optimal and secure action to drive the system toward the goal. Specific problems can be determined by the use of this kind of hybrid models are: - The unity of order. - Control the system along a reachable path. - Control the system in a safe path. - Optimise the cost function. - Modularity of control The proposed model solves the specified problems in the switching models problem, the initial condition calculus and the unity of the order models. Continuous and discrete phenomena are represented in Linear hybrid models, defined with defined eighth-tuple parameters to model different types of hybrid phenomena. Applying a transformation over the state vector : for LTI system we obtain from a two-dimensional SS a single parameter, alpha, which still maintains the dynamical information. Combining this parameter with the system output, a complete description of the system is obtained in a form of a graph in polar representation. Using Tagaki-Sugeno type III is a fuzzy model which include linear time invariant LTI models for each local model, the fuzzyfication of different LTI local model gives as a result a non-linear time invariant model. In our case the output and the alpha measure govern the membership function. Hybrid systems control is a huge task, the processes need to be guided from the Starting point to the desired End point, passing a through of different specific states and points in the trajectory. The system can be structured in different levels of abstraction and the control in three layers for the Hybrid systems from planning the process to produce the actions, these are the planning, the process and control layer. In this case the algorithms will be applied to robotics ¡V a domain where improvements are well accepted ¡V it is expected to find a simple repetitive processes for which the extra effort in complexity can be compensated by some cost reductions. It may be also interesting to implement some control optimisation to processes such as fuel injection, DC-DC converters etc. In order to apply the RW theory of discrete event systems on a Hybrid system, we must abstract the continuous signals and to project the events generated for these signals, to obtain new sets of observable and controllable events. Ramadge & Wonham¡¦s theory along with the TCT software give a Controllable Sublanguage of the legal language generated for a Discrete Event System (DES). Continuous abstraction transforms predicates over continuous variables into controllable or uncontrollable events, and modifies the set of uncontrollable, controllable observable and unobservable events. Continuous signals produce into the system virtual events, when this crosses the bound limits. If this event is deterministic, they can be projected. It is necessary to determine the controllability of this event, in order to assign this to the corresponding set, , controllable, uncontrollable, observable and unobservable set of events. Find optimal trajectories in order to minimise some cost function is the goal of the modelling procedure. Mathematical model for the system allows the user to apply mathematical techniques over this expression. These possibilities are, to minimise a specific cost function, to obtain optimal controllers and to approximate a specific trajectory. The combination of the Dynamic Programming with Bellman Principle of optimality, give us the procedure to solve the minimum time trajectory for Hybrid systems. The problem is greater when there exists interaction between adjacent states. In Hybrid systems the problem is to determine the partial set points to be applied at the local models. Optimal controller can be implemented in each local model in order to assure the minimisation of the local costs. The solution of this problem needs to give us the trajectory to follow the system. Trajectory marked by a set of set points to force the system to passing over them. Several ways are possible to drive the system from the Starting point Xi to the End point Xf. Different ways are interesting in: dynamic sense, minimum states, approximation at set points, etc. These ways need to be safe and viable and RchW. And only one of them must to be applied, normally the best, which minimises the proposed cost function. A Reachable Way, this means the controllable way and safe, will be evaluated in order to obtain which one minimises the cost function. Contribution of this work is a complete framework to work with the majority Hybrid systems, the procedures to model, control and supervise are defined and explained and its use is demonstrated. Also explained is the procedure to model the systems to be analysed for automatic verification. Great improvements were obtained by using this methodology in comparison to using other piecewise linear approximations. It is demonstrated in particular cases this methodology can provide best approximation. The most important contribution of this work, is the Alpha approximation for non-linear systems with high dynamics While this kind of process is not typical, but in this case the Alpha approximation is the best linear approximation to use, and give a compact representation.