Learning and tuning fuzzy logic controllers through reinforcements

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing

Using Fuzzy Logic for Performance Evaluation in Reinforcement Learning

Current reinforcement learning algorithms require long training periods which generally limit their applicability to small size problems. A new architecture is described which uses fuzzy rules to initialize its two neural networks: a neural network for performance evaluation and another for action selection. This architecture is applied to control of dynamic systems and it is demonstrated that it is possible to start with an approximate prior knowledge and learn to refine it through experiments using reinforcement learning

Intelligent Inference Systems Corp.

Current reinforcement learning algorithms require long training periods which generally limit their applicability to small size problems. A new architecture is described which uses fuzzy rules to initialize its two neural networks: a neural network for performance evaluation and another for action selection. This architecture is applied to control of dynamic systems and it is demonstrated that it is possible to start with an approximate prior knowledge and learn to refine it through experiments using reinforcement learning.

Genetic Algorithms for Automated Tuning of Fuzzy Controllers: A Transportation Application

We describe the design and tuning of a controller for enforcing compliance with a prescribed velocity profile for a rail-based transportation system. This requires following a trajectory, rather than fixed setpoints (as in automobiles). We synthesize a fuzzy controller for tracking the velocity profile, while providing a smooth ride and staying within the prescribed speed limits. We use a genetic algorithm to tune the fuzzy controller's performance by adjusting its parameters (the scaling factors and the membership functions) in a sequential order of significance. We show that this approach results in a controller that is superior to the manually designed one, and with only modest computational effort. This makes it possible to customize automated tuning to a variety of different configurations of the route, the terrain, the power configuration, and the cargo. 1. Introduction 1.1. Problem Description We propose a system, composed of a cruise planning and a cruise control module, tha..

Fuzzy Logic Control of Resonant Converters for Power Supplies

Fuzzy Logic Control (FLC) technology has drastically reduced the development time and deployment cost for the synthesis of nonlinear controllers for dynamic systems. As a result we have experienced an increased number of FLC applications. We will illustrate our efforts in FLC technology transfer aimed at controlling a resonant converter power supply. We will discuss the development of FLCs for a Series-Resonant Converter (SRC) and a Single-Ended Parallel MultiResonant Converter (SEP-MRC). We will emphasize the role of fuzzy logic in the development and deployment of these applications, and will show how to meet their stringent throughput requirements by compiling the FLC into look-up tables. 1. Problem Description: Resonant Converter Control for Power Supplies Power supplies require a regulator to maintain the output voltage or output power constant in light of operational or environmental changes. For instance, the voltage of a power supply will tend to drop if load current increases..