The purpose of this article is to design an optimal fuzzy type-2 proportional integral derivative (FT2PID) controller to enhance the pressure tracking capability of an artificial respiratory system. A patient-hose blower-driven mechanical ventilator (MV) operated in pressure-controlled mode is examined with the proposed controller structure. The error between the desired airway pressure and the ventilator pressure is used as an input to the fuzzy type-2 controller. Another fuzzy input is the change in error. The output variables of the fuzzy inference system (FIS) of the fuzzy controller in the proposed control structure are the parameters of a PID controller. The ranges and points of a triangular-shaped fuzzy type-2 inference system are optimized for the ventilator model with a newly introduced optimizer named the human conception optimizer (HCO) algorithm. With the optimized fuzzy type-2 controller, the parameters of the PID controller are adjusted automatically during any external disturbance or in the presence of any parametric uncertainties in the system. The inherent features of handseling uncertainties of the fuzzy type-2 controller are verified with the PID controller for the ventilator model under different scenarios. With the proposed control scheme, the pressure tracking profile of the ventilator is improved in terms of response time, settling time, and overshoot as compared to the existing results.

In this paper, an optimal fuzzy-PID controller is proposed for a knee exoskeleton model for patients with knee problems caused by strokes, post-polio, osteoarthritis, etc. For this purpose, a fuzzy control logic based proportional integral derivative (Fuzzy-PID) controller is considered. The fuzzy con-troller in the proposed control structure is used to adjust the PID controller parameters for the knee exoskeleton model. The error between the targeted angle and actual angle of knee exoskeleton system and the change of this error are taken as the input variables of the fuzzy controller. The output variables are chosen as the parameters of PID controller. The membership functions of the fuzzy controller are optimized with a well-known optimization algorithm called class topper optimization. The performance of the proposed controller is examined under different scenarios and plotted on a graph.

The problem of localization is one of most important issues in wireless sensor networks . Furthermore, it is critical to monitor and evaluate the data gathered. For a variety of factors, such as upkeep, lifespan, and breakdown, the fixed density of these beacons may be increased or decreased. Because of its robustness, flexibility, and economic viability, a well-known technique for locating wireless sensor network nodes is the Distance Vector-Hop (DV-Hop) algorithm. As a result, researchers continue to look for ways to develop it. A new Non-inertial Opposition based Class Topper Optimization (NOCTO) based enhanced DV-Hop localization algorithm is proposed. It also focuses through an optimized formulation to compute the average hop-size with weight of beacon nodes in order to reduce the localization error with estimated distance between the beacon and the dumb node, due to improved localization accuracy . For spiral deployed 2 D wireless sensor networks , this paper proposes a multi-objective NOCTO-based DV-Hop localization. The simulation results indicate that our suggested multi-objective function outperforms some existing techniques.

Optimization techniques are widely being used to solve large and complex economical load dispatch (ELD) and combined emission economical dispatch (CEED) problems in power systems. These techniques can solve these problems in a short computational time. In this article, a new human intelligence-based metaheuristic optimization technique, that is, aggrandized class topper optimization (CTO), is proposed to solve ELD and CEED problems. This proposed algorithm is an upgraded form of classical CTO in which the concept of remedial classes is incorporated to enhance the learning ability of weak students of a class. To validate the exploration, exploitation, convergence, and local minima avoidance capabilities of the proposed algorithm, 29 benchmark functions are considered. Furthermore, seven different test cases for the ELD problem and four test cases for a CEED problem are considered to test the effectiveness of the proposed algorithm to solve these complex problems. The result analysis proves that the proposed algorithm provides better and effective results in almost each test case.