— A novel algorithm is developed to update the rule base of the fuzzy gain scheduling of the PID controller. The algorithm fulfills the following requirements: all data needed are stored in a rule base, this keeps the rule base small so that it can also be maintained manually, and it stores the PID parameters with a common base of operation points. Key-Words: — nonlinear control, controller design

This paper investigates two fuzzy logic PID controllers that use simplified design schemes. Fuzzy logic PD and PI controllers are effective for many control problems but lack the advantages of the fuzzy PID controller. Design methodologies are in their infancy and still somewhat intuitive. Fuzzy controllers use a rule base to describe relationships between the input variables. Implementation of...

This chapter describes the use of genetic programming to evolve a fuzzy rule base to model gene expression. We describe the problem of genetic regulation in details and offer some reasons as to why many computational methods have difficulties in modeling it. We describe how a fuzzy rule base can be applied to this problem as well as how genetic programming can be used to evolve a fuzzy rule bas...

The objective of this work is to provide a simple and effective nonlinear controller. Our strategy involves switching the underlying strategies in order to maintain a robust control. If a disturbance moves the system outside the region of stability or the domain of attraction, it will be guided back onto the desired course by the application of a different control strategy. In the context of sw...

A method for building the rule-base of a fuzzy controller, using the iterative learning and adaptive neural fuzzy training is tested in practical conditions. This method aims to engage intelligent features to controller design procedure, by implying concepts and techniques from artificial intelligence as learning or adapting. An iterative self-learning algorithm is used to gather useful and tru...

Some difficulties emerging during the construction of fuzzy behaviour-based control structures are inherited from the type of the applied fuzzy reasoning. The fuzzy rule base requested for many classical reasoning methods needed to be complete. In case of fetching fuzzy rules directly from expert knowledge e.g. for the behaviour coordination module, the way of building a complete rule base is n...

In this paper, we propose a genetic-algorithm-based fuzzy-knowledge integration framework that can simultaneously integrate multiple fuzzy rule sets and their membership function sets. The proposed approach consists of two phases: fuzzy knowledge encoding and fuzzy knowledge integration. In the encoding phase, each fuzzy rule set with its associated membership functions is first transformed int...

In this paper, a fuzzy system for the control of the task of laying a fabric on a table via a robot is introduced. The task of laying is investigated and the parameters affecting the appropriate guiding of the fabric are identified to formulate the rule base for the fuzzy control system, which is tuned experimentally. The fuzzy control system formed is then verified by testing the laying of fab...

The main aim of this paper is to present MOGUL, a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach. MOGUL will consist of some design guidelines that allow us to obtain diierent Genetic Fuzzy Rule-Based Systems, i. e., evolutionary algorithm-based processes to automatically design Fuzzy Rule-Based Systems by learning and/or tuning the Fuzzy Rule ...

Melt volume-flow rate (MVR) is one of the most important quality indicators of composite materials, which depends on the proportion of the component materials. This paper reports the development of a low complexity fuzzy model that describes the relation between percentage amount of multiwall carbon nanotube (MWCNT), acrylonitrile-butadiene-styrene (ABS), polycarbonate (PC) and MVR of the resul...