Fuzzy Proportional Derivative Controller Research Articles

Design and implementation of fuzzy P 2 ID controller for handlebar control of a bicycle robot

This paper presents the design and implementation of a new fuzzy proportional-integration plus fuzzy proportional-derivative control scheme, named the fuzzy P 2 ID controller hereafter, for controlling the handlebar of a bicycle robot with time-varyi

Automatic Ship Handling of the Maritime Search Mission using a Self-Tuning Fuzzy Gain Scheduling PD Controller

In this paper, the ship manoeuvring problems particular to maritime search and rescue are investigated. A solution to such problems is necessary for successful ship handling to save human life. A fuzzy gain scheduling Proportional Derivative (PD) controller, using a back-propagation algorithm, is developed to solve these maritime search and rescue problems. The parameters of the proposed PD controller are determined on-line by fuzzy rules and adjustable fuzzy reasons. The proposed controller is self-adaptive and can accommodate the effects caused by wave or wind disturbance. Some computer simulations are provided to illustrate the use of the main ideas.

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

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. >