fuzzy controller

fuzzy controller

[¦fəz·ē kən′trōl·ər]
(control systems)
An automatic controller in which the relation between the state variables of the process under control and the action variables, whose values are computed from observations of the state variables, is given as a set of fuzzy implications or as a fuzzy relation.
References in periodicals archive ?
In order to control the vehicle suspension, Huang and Lin [32] used an adaptive fuzzy controller based on a sliding surface as the input variable of a fuzzy logic controller (FLC); however, the ideal configuration of fuzzy rules in FLC is not a deterministic approach [31] unless being optimized.
In [20], a new approach was developed based on a single-input FLC for MPPT to reduce the controller complexity; the input of the fuzzy controller is the signed perpendicular distance ds calculated from error (E) and the change of error (CE) of conventional FLC.
In the case of a two-input regulator, the fuzzy controller is assimilated to a conventional PI-controller.
The input and output universe of the fuzzy controller are adjusted in real time by the error e and the error change rate ec.
Separate chapters address sliding mode control, the output feedback method, single-input-rule module-based fuzzy controller design, and the input shaping control technique.
As it is illustrated in Figure 8, fuzzy controller is assigned to control zone temperature, static air pressure, and C[O.sub.2] level.
Consequently, a procedure called parallel distributed compensation (PAC) makes the T-S fuzzy controller design easier.
The fuzzy controller proposed relied on the distance and orientation of the robot to the object as inputs to generate the needed wheel velocities to move the robot smoothly to its target while switching to the obstacle avoidance algorithm designed to avoid obstacles.
The main contribution of this work is to perform dynamic parameter adaptation using Type 1, interval Type 2, and generalized Type 2 fuzzy system in the harmony search algorithm applied to optimization of the membership functions of a fuzzy controller for a benchmark problem.
The authors have already suggested the fuzzy controller in [23] and the neurofuzzy controller in [24].
The fuzzy controller acts in situations where the depth of discharge is greater than 70% and applied directly on vehicle performance and the system slows to protect the battery power by reducing the power consumption, since it is directly proportional to the tensile force of the EV.
An adaptive fuzzy controller was previously applied to a multivariable system with uncertainties and demonstrated some practical value [13].
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