L. Behera et Kk. Anand, Guaranteed tracking and regulatory performance of nonlinear dynamic systems using fuzzy neural networks, IEE P-CONTR, 146(5), 1999, pp. 484-491
A new technique for the design of stable tracking and regulatory control sy
stems for nonlinear systems using fuzzy neural networks is described. A cla
ss of nonlinear systems is considered where a few of the input variables ca
ll be manipulated (controlled) while the rest are considered as disturbance
s. System dynamics are modelled using a fuzzy neural network where each fuz
zy operating region is associated with a series-parallel linear model. Two
new control strategies are proposed using the LyaFunov synthesis approach.
In one, a disturbance invariant control scheme is proposed where the sensit
ivity of the system response with respect to the control variable is estima
ted using a fuzzy neural model. In the other strategy a model predictive sc
heme is developed in which disturbances are predicted online and the fuzzy
neural model is used to predict the control action for a desired setpoint.
The proposed controllers have been implemented for a nonlinear pH reactor t
hrough simulation. In a pH control problem the acid and buffer flow rate ar
e considered to be disturbances, while the base flow rate is controlled to
maintain a constant pH. Simulation results show that the proposed scheme pr
ovides guaranteed tracking and regulatory performance.