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].