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Journal ArticleDOI

New design and stability analysis of fuzzy proportional-derivative control systems

TLDR
The design principle, tracking performance, and stability analysis of a fuzzy proportional-derivative (PD) controller, derived from the conventional continuous-time linear PD controller, and the fuzzification, control-rule base, and defuzzification in the design are discussed in detail.
Abstract
This paper describes the design principle, tracking performance, and stability analysis of a fuzzy proportional-derivative (PD) controller. First, the fuzzy PD controller is derived from the conventional continuous-time linear PD controller. Then, the fuzzification, control-rule base, and defuzzification in the design of the fuzzy PD controller are discussed in detail. The resulting controller is a discrete-time 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 self-tuning 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 bounded-input/bounded-output 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 first-order and second-order linear systems, with or without time-delays, 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 second-order 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 set-points satisfactorily but the conventional PD controller cannot. >

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Citations
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Journal ArticleDOI

A robust self-tuning scheme for PI- and PD-type fuzzy controllers

TL;DR: The proposed self-tuning technique is applied to both PI- and PD-type FLCs to conduct simulation analysis for a wide range of different linear and nonlinear second-order processes including a marginally stable system where even the well known Ziegler-Nichols tuned conventional PI or PID controllers fail to provide an acceptable performance due to excessively large overshoot.
Journal ArticleDOI

An optimal fuzzy PID controller

TL;DR: The constant PID control gains are optimized by using the multiobjective genetic algorithm (MOGA) thereby yielding an optimal fuzzy PID controller, which preserves the same linear structure of the proportional, integral, and derivative parts but has constant coefficient yet self-tuned control gains.
Journal ArticleDOI

Fuzzy PID controller: Design, performance evaluation, and stability analysis

TL;DR: The main motivation for this design was to control some known nonlinear systems, such as robotic manipulators, which violate the conventional assumption of the linear PID controller.
Journal ArticleDOI

New methodology for analytical and optimal design of fuzzy PID controllers

TL;DR: Simulation results show that the proposed fuzzy PID controller produces superior control performance to the conventional PID controllers, particularly in handling nonlinearities due to time delay and saturation.

Fuzzy Logic in Control

R. Jager
References
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Journal ArticleDOI

Stability analysis and design of fuzzy control systems

TL;DR: The fuzzy block diagrams and the stability analysis are applied to the design problems of a model-based fuzzy controller and a new design technique of a fuzzy controller is proposed.
Journal ArticleDOI

Fuzzy control theory: a nonlinear case

TL;DR: It is proved theoretically that such a fuzzy controller, the smallest possible, with two inputs and a nonlinear defuzzification algorithm is equivalent to a nonfuzzy nonlinear proportional-integral (PI) controller with proportional-gain and integral-gain changing with error and rate change of error about a setpoint.
Journal ArticleDOI

Fuzzy self-tuning of PID controllers

TL;DR: A comparative simulation study on various processes shows that the performance of the new scheme improves considerably, in terms of set-point and load disturbance responses, over the PID controllers well-tuned using both the classical Ziegler-Nichols formula and the more recent Refined Ziegle Nicholas formula.
Journal ArticleDOI

On methods for improving performance of PI-type fuzzy logic controllers

TL;DR: Two types of fuzzy logic controllers are proposed that take out appropriate amounts of accumulated control input according to fuzzily described situations in addition to the incremental control input calculated by conventional fuzzy PI controllers.
Journal ArticleDOI

The simplest fuzzy controllers using different inference methods are different nonlinear proportional-integral controllers with variable gains

Hao Ying
- 01 Nov 1993 - 
TL;DR: It is analytically proven that the fuzzy control systems have the same local stability at the equilibrium point as the corresponding linear PI control system does.