John H. Lilly

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Genetic algorithms show powerful capabilities for automatically designing fuzzy systems from data, but many proposed methods must be subjected to some minimal structure assumptions, such as rule base size. In this paper, we also address the design of fuzzy systems from data. A new evolutionary approach is proposed for deriving a compact fuzzy classification(More)
Multiple-input sliding-mode techniques are applied to a planar arm actuated by four groups of pneumatic muscle (PM) actuators in opposing pair configuration. The control objective is end-effector tracking of a desired path in Cartesian space. The inputs to the system are commanded input pressure differentials for the two opposing PM groups. An existing(More)
Absfraci A fuzzy learning control technique is used for position tracking involving the vertical movement of a mass attached to a pneumatic muscle. Because the pneumatic muscle is nonlinear and time varying, conventional tixed controllers are less effective than the fuzzy controller proposed in this paper. The controller is of a PID type, with an adaptive(More)
This paper studies the evolutionary design of a fuzzy P+ID controller for an actual pneumatic muscle actuator system. The control of pneumatic muscles is a challenging problem because of their high degree of nonlinearity, time-varying parameters, and uncertainty. A fuzzy P+ID controller is constructed using an incremental fuzzy logic controller in place of(More)
Adaptive tracking techniques are applied to pneumatic muscle actuators arranged in bicep and tricep configurations. The control objective is to force the joint angle to track a specified reference path. Mathematical models are derived for the bicep and tricep configurations. The models are nonlinear and in general time-varying, making adaptive control(More)
This paper considers the incorporation of negative examples into fuzzy inference systems (FIS). A new method of defuzzification called dot attenuation is presented. This is a generalization of conventional defuzzification that has the ability to incorporate negative examples into the FIS’ reasoning process. Several variations of dot attenuation including(More)
Data-Driven Rule Extraction Using Adaptive Fuzzy-Neural Models Adam E. Gaweda August 9, 2002 Neural network and fuzzy rule-based approaches to data-driven modeling have recently gained a lot of attention. The property of universal approximation makes it possible to imitate a large class of complex nonlinear systems with a certain degree of accuracy, while(More)