
IEEE TFS: Abstracts of Published Papers, vol. 2, no. 4
New design and stability analysis of fuzzy proportionalderivative
control systems
This paper describes the design principle, tracking performance, and stability analysis of a fuzzy proportionalderivative (PD) controller. First, the fuzzy PD controller is derived from the conventional continuoustime linear PD controller. Then, the fuzzification, controlrule base, and defuzzification in the design of the fuzzy PD controller are discussed in detail. The resulting controller is a discretetime fuzzy version of the conventional PD controller, which has the same linear structure in the proportional and the derivative parts but has nonconstant gains: both the proportional and derivative gains are nonlinear functions of the input signals. The new fuzzy PD controller thus preserves the simple linear structure of the conventional PD controller yet enhances its selftuning control capability. Computer simulation results have demonstrated this advantage of the fuzzy PD controller, particularly when the process to be controlled is nonlinear. After a detailed stability analysis, where a simple and realistic sufficient condition for the boundedinput/boundedoutput stability of the overall feedback control system was derived, several computer simulation results are compared with the conventional PD controller. Although the conventional and fuzzy PD controllers are not exactly comparable, the authors compare them in order to have a sense of how well the fuzzy PD controller performs. For this reason, in the simulations several firstorder and secondorder linear systems, with or without timedelays, are first used to test the performance of the fuzzy PD controller for step reference inputs: the fuzzy PD control systems show remarkable performance, as well as (if not better than) the conventional PD control systems. Moreover, the fuzzy PD controller is compared to the conventional PD controller for a particular secondorder linear system, showing the advantage of the fuzzy PD controller over the conventional one in the sense that in order to obtain the same control performance the conventional PD controller has to employ an extremely large gain while the fuzzy controller uses a reasonably small gain. Finally, in the case of nonlinear systems, the authors provide some examples to show that the fuzzy PD controller can track the setpoints satisfactorily but the conventional PD controller cannot.
First break refraction event picking using fuzzy logic systems
First break picking is a pattern recognition problem in seismic signal processing, one that requires much human effort and is difficult to automate. The authors' goal is to reduce the manual effort in the picking process and accurately perform the picking. Feedforward neural network first break pickers have been developed using backpropagation training algorithms applied either to an encoded version of the raw data or to derived seismic attributes which are extracted from the raw data. The authors summarize a study in which they applied a backpropagation fuzzy logic system (BPFLS) to first break picking. The authors use derived seismic attributes as features, and take lateral variations into account by using the distance to a piecewise linear guiding function as a new feature. Experimental results indicate that the BPFLS achieves about the same picking accuracy as a feedforward neural network that is also trained using a backpropagation algorithm; however, the BPFLS is trained in a much shorter time, because there is a systematic way in which the initial parameters of the BPFLS can be chosen, versus the random way in which the weights of the neural network are chosen.
Application of a fuzzy discrimination analysis for diagnosis of valvular
heart disease
We have applied the discrimination analysis proposed by Norris, Pilsmorth, and Baldwin to the diagnosis of valvular heart diseases. They proposed the diagnosis method which uses concepts from fuzzy set theory. It consists of two independent parts: discrimination analysis and connectivity analysis. We performed the experiments in order to evaluate the effectiveness of the proposed discrimination analysis part of the method. Also, we extended the original method to handle partial manifestation of symptoms and severity of diseases by using fuzzy sets. In addition, we introduced the concept of prototypicalness of patients with a particular disease to improve the performance of the diagnosis. The results of the experiments are very promising. In the best case, we achieved a rate of true positive diagnosis of 81% while maintaining a rate of false positive diagnosis at the low level of 10%. We report the quantitative results of the experiments.
Fuzzy smoothing algorithms for variable structure systems
A variable structure system (VSS) is a control system implementing different control laws in different regions of the state space divided by a set of boundary manifolds. The control input switches from one control law to another when the state crosses the boundary manifolds. In general, the control input may not be smooth when switching at these boundary manifolds and may excite high frequency dynamics. This paper proposes two fuzzy rule based algorithms for smoothing the control input. The merits of these fuzzy smoothing control algorithms are illustrated by two examples: a semiactive suspension system based on optimal control and a direct drive robot arm under discrete time sliding mode control. The controller design for these two examples is a blend of traditional control theoretic approaches and fuzzy rule based approaches.
Adaptive control of a class of nonlinear systems with fuzzy logic
An adaptive tracking control architecture is proposed for a class of continuoustime nonlinear dynamic systems, for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible. The architecture employs fuzzy systems, which are expressed as a series expansion of basis functions, to adaptively compensate for the plant nonlinearities. Global asymptotic stability of the algorithm is established in the Lyapunov sense, with tracking errors converging to a neighborhood of zero. Simulation results for an unstable nonlinear plant are included to demonstrate that incorporating the linguistic fuzzy information from human experts results in superior tracking performance.
A generalized fuzzy Petri net model
The paper proposes a new model of Petri nets based on the use of logic based neurons. In contrast to the existing generalizations, this approach is aimed at neuraltype modeling of the entire concept with a full exploitation of the learning capabilities of the processing units being used there. The places and transitions of the net are represented between this model and the previous twovalued counterpart is also revealed. The learning aspects associated with the nets are investigated. These abstracts are posted in order to accelerate dissemination of evolving Fuzzy Systems information. The abstracts are from papers published in the IEEE Transactions on Fuzzy Systems (TFS). 


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