PI-Fuzzy controllers for integral plants to ensure robust stability

Evolving Fuzzy Models for Automated Translation

“…The results presented in Figure 6 are encouraging. However, different conclusions are expected to be obtained for other applications [33][34][35][36][37][38][39][40].…”

Discrete Time Consensus of Leader Following BLDC Motor with Delay

“…In [18], a 2 DOF fuzzy controller is proposed for foot trajectory tracking of hexapod robot having a prefilter, designed by genetic algorithm (GA), in feed forward loop to overcome delay caused by dead zone thereby improving tracking whereas feedback controller ensure stability. In [19], Takagi-Sugeno and Mamdani fuzzy model are used to design PI-Fuzzy controller for servo based integral plant. They tune PI controller by the extended symmetrical optimum method to ensure robust stability in the presence of disturbances in the plant.…”

“…System response (in this case it is BLDC motor), is defined as whereas desired system response is The RST controlled close loop system configuration is given in Fig. 2 while transfer functions are presented in equation (19) and (20) respectively [21]. (20), we deduce that polynomial define close loop poles which can be decomposed into and ie desired dominant poles and auxiliary poles of the closed loop respectively.…”

Particle swarm optimization algorithm reinforced fuzzy proportional–integral–derivative for a quadrotor attitude control

“…[8][9][10] In such type, the significant represent is quadrotor that has four symmetric rotorcrafts in each couple. 8,9,11 The proportional-integral-derivative (PID) controllers 3,8,9,[12][13][14][15] are of low cost, uncomplicated structure, and easy design, which are applied to many hi-tech fields, from industrial machine to biped robot, and especially in UAV systems. However, the controller methods such as PID, backstepping, and fuzzy PID approaches for the UAVs 10,11,16 have problems in practical applications for better performance, robustness, and less oscillation.…”