This paper presents two hybrid control topologies; the
topologies
are designed by combining artificial intelligence approaches and sliding-mode
control methodology. The first topology mixes the learning algorithm
for multivariable data analysis (LAMDA) approach with sliding-mode
control. The second offers a Takagi–Sugeno multimodel approach,
internal model, and sliding-mode control. The process under study
is a nonlinear pH neutralization process with high nonlinearities
and time-varying parameters. The pH process is simulated for multiple
reference changes, disturbance rejection, and noise in the transmitter.
Performance indices are used to compare the proposed approaches quantitatively.
The hybrid control topologies enhance the performance and robustness
of the pH process under study.