Fuzzy and neural control

Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning

Adaptive Fuzzy Systems in Computational Intelligence

In recent years, the interest in computational intelligence techniques, which currently includes neural networks, fuzzy systems, and evolutionary programming, has grown significantly and a number of their applications have been developed in the government and industry. In future, an essential element in these systems will be fuzzy systems that can learn from experience by using neural network in refining their performances. The GARIC architecture, introduced earlier, is an example of a fuzzy reinforcement learning system which has been applied in several control domains such as cart-pole balancing, simulation of to Space Shuttle orbital operations, and tether control. A number of examples from GARIC's applications in these domains will be demonstrated

Artificial Intelligence Research Branch future plans

This report contains information on the activities of the Artificial Intelligence Research Branch (FIA) at NASA Ames Research Center (ARC) in 1992, as well as planned work in 1993. These activities span a range from basic scientific research through engineering development to fielded NASA applications, particularly those applications that are enabled by basic research carried out in FIA. Work is conducted in-house and through collaborative partners in academia and industry. All of our work has research themes with a dual commitment to technical excellence and applicability to NASA short, medium, and long-term problems. FIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at the Jet Propulsion Laboratory (JPL) and AI applications groups throughout all NASA centers. This report is organized along three major research themes: (1) Planning and Scheduling: deciding on a sequence of actions to achieve a set of complex goals and determining when to execute those actions and how to allocate resources to carry them out; (2) Machine Learning: techniques for forming theories about natural and man-made phenomena; and for improving the problem-solving performance of computational systems over time; and (3) Research on the acquisition, representation, and utilization of knowledge in support of diagnosis design of engineered systems and analysis of actual systems

Data-Driven Abstraction-Based Control Synthesis

This paper studies formal synthesis of controllers for continuous-spacesystems with unknown dynamics to satisfy requirements expressed as lineartemporal logic formulas. Formal abstraction-based synthesis schemes rely on aprecise mathematical model of the system to build a finite abstract model,which is then used to design a controller. The abstraction-based schemes arenot applicable when the dynamics of the system are unknown. We propose adata-driven approach that computes the growth bound of the system using afinite number of trajectories. The growth bound together with the sampledtrajectories are then used to construct the abstraction and synthesise acontroller. Our approach casts the computation of the growth bound as a robust convexoptimisation program (RCP). Since the unknown dynamics appear in theoptimisation, we formulate a scenario convex program (SCP) corresponding to theRCP using a finite number of sampled trajectories. We establish a samplecomplexity result that gives a lower bound for the number of sampledtrajectories to guarantee the correctness of the growth bound computed from theSCP with a given confidence. We also provide a sample complexity result for thesatisfaction of the specification on the system in closed loop with thedesigned controller for a given confidence. Our results are founded onestimating a bound on the Lipschitz constant of the system and provideguarantees on satisfaction of both finite and infinite-horizon specifications.We show that our data-driven approach can be readily used as a model-freeabstraction refinement scheme by modifying the formulation of the growth boundand providing similar sample complexity results. The performance of ourapproach is shown on three case studies.<br

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers