This paper presents nonlinear model following control for a class of nonlinear systems using the fuzzy model-based control approach.
This paper presents servo control for discrete-time nonlinear systems with constraints on inputs and states using the fuzzy model-based control approach.
The control law is designed using the fuzzy identified model instead of the actual HV one.
Using the fuzzy relation model, the system can quickly select feasible control methods according to the desired removal efficiency.
In the present paper the stress strain behaviour of high strength concrete subjected to axial load is obtained by using the fuzzy logic model.
The gains of AFC are obtained by using the fuzzy identifier model which is defined by real system outputs and control inputs.
The results also indicate that the nanoparticle concentrations and the nanoparticle size are the greatest effect factors to fuzzy multi-response performance index (FRTS) by using the fuzzy logic model based on the Taguchi method with the fly hobbing process.
It can be seen from the real-time adjustments shown in Figs. 8, 9 and 10 that the power needing to be adjusted is largest when using the deterministic optimization scheme, followed by the stochastic optimization model, and it is smallest when using the fuzzy stochastic optimization model.
Finally, the simulated GWQI indices using the Fuzzy Network-CANFIS model were entered into GIS, and hence groundwater quality map (raster layer) based on the results of the network simulation was earned.
Using the fuzzy conflict graph model presented in Section 2.3, a weighted max-cut algorithm needs to be used to find the optimal partition.
