INTELLIGENT CONTROLLER: AN ALTERNATIVE APPROACH FOR NONLINEAR SYSTEM CONTROL

Control of non liner systems is difficult in the absence of a systematic procedure as available for linear systems. Mathematical model that we use for nonlinear system control needs very expensive and sophisticated instrument. Intelligent system which is suppose to posses humanlike expertise within a specific domain, adopts itself and learn to do better in any condition. Soft computing an approach for constructing computationally intelligent system consist of several computing techniques, including neural network, fuzzy set theory and derivate free optimization method such as genetic algorithms and simulated annealing. As it incorporate human knowledge effectively, to deal with imprecision and uncertainty, and to learn to adopt itself to unknown or changing environment for better performance

An integrated approach of learning, planning, and execution

Agents (hardware or software) that act autonomously in an environment have to be able to integrate three basic behaviors: planning, execution, and learning. This integration is mandatory when the agent has no knowledge about how its actions can affect the environment, how the environment reacts to its actions, or, when the agent does not receive as an explicit input, the goals it must achieve. Without an a priori theory, autonomous agents should be able to self-propose goals, set-up plans for achieving the goals according to previously learned models of the agent and the environment, and learn those models from past experiences of successful and failed executions of plans. Planning involves selecting a goal to reach and computing a set of actions that will allow the autonomous agent to achieve the goal. Execution deals with the interaction with the environment by application of planned actions, observation of resulting perceptions, and control of successful achievement of the goals. Learning is needed to predict the reactions of the environment to the agent actions, thus guiding the agent to achieve its goals more efficiently. In this context, most of the learning systems applied to problem solving have been used to learn control knowledge for guiding the search for a plan, but few systems have focused on the acquisition of planning operator descriptions. As an example, currently, one of the most used techniques for the integration of (a way of) planning, execution, and learning is reinforcement learning. However, they usually do not consider the representation of action descriptions, so they cannot reason in terms of goals and ways of achieving those goals. In this paper, we present an integrated architecture, lope, that learns operator definitions, plans using those operators, and executes the plans for modifying the acquired operators. The resulting system is domain-independent, and we have performed experiments in a robotic framework. The results clearly show that the integrated planning, learning, and executing system outperforms the basic planner in that domain.Publicad

Reabilitação de infra-estruturas urbanas de abastecimento de água

Dissertação apresentada para obtenção do grau de Mestre em Engenharia doAmbiente (ramo de Hidráulica e Recursos Hídricos), na Faculdade de Engenharia da Universidade do Porto, sob a orientação dos Professores Doutores J.C. Tentugal Valente e Paulo T. Santos Monteir

On Planning while Learning

This paper introduces a framework for Planning while Learning where an agent is given a goal to achieve in an environment whose behavior is only partially known to the agent. We discuss the tractability of various plan-design processes. We show that for a large natural class of Planning while Learning systems, a plan can be presented and verified in a reasonable time. However, coming up algorithmically with a plan, even for simple classes of systems is apparently intractable. We emphasize the role of off-line plan-design processes, and show that, in most natural cases, the verification (projection) part can be carried out in an efficient algorithmic manner. 1. Introduction Suppose you find yourself in a complex labyrinth, with no recollection as to what brought you there or how to get out. You do have some knowledge as to the possible outcomes of your actions (e.g., gravitation works as usual). However, several basic characteristics of your surrounding are unknown (e.g., the map of th..