Human Conception Optimizer-Based Optimal Type-2 Fuzzy PID Controller Design for Artificial Respiratory System
研究了一种基于人类构想优化器算法优化的二型模糊PID控制器，用于提高人工呼吸机在压力控制模式下的气道压力跟踪能力，减少响应时间、稳定时间和超调量。
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.