State estimation and non linear model based control of benchmark processes using unscented kalman filter
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Abstract
In this work, Non-linear Model Based Control (NMBC) scheme for
newlinethe non-linear system is proposed. The proposed control law is a function of
newlinethe model states and the measured output variable. Performance comparison
newlineof the proposed NMBC scheme with that of Non-linear Model Predictive
newlineControl Scheme (NMPC) is reported. The proposed model based control law
newlinehas a single tuning parameter and it can be optimally determined by
newlineminimizing a suitable performance measure. From the extensive simulation
newlinestudies on benchmark processes such as Continuous Stirred Tank Reactor
newline(CSTR), Conical-tank and pH processes, it can be inferred that servo and
newlineregulatory performances of the proposed NMBC scheme is found to be
newlinematching closely with the NMPC scheme, with the proposed control scheme
newlinebeing computational less intensive. Performance comparison of NMBC with
newlineNMPC, highlighting offset free servo performance, rejection of unmeasured
newlinedisturbance and robustness to model-plant mismatch are reported.
newlineIn addition, a Non-linear Model Based Control Scheme using an
newlineUnscented Kalman Filter (UKF-NMBC) for the process affected by random
newlinedisturbances and random errors in measurements are formulated. The
newlineproposed control law is also a function of the model states and the measured
newlineoutput variable. An Augmented Unscented Kalman Kilter (AUKF) is used to
newlinegenerate unbiased state estimates in the presence of step-like changes in the
newlineunmeasured disturbances. From the extensive simulation studies on
newlinebenchmark processes such as Continuous Stirred Tank Reactor (CSTR),
newlineConical-tank and pH processes, it can be inferred that servo-regulatory
newlineperformance of the proposed AUKF-NMBC scheme is also found to be
newlinematching closely with the Non-linear Model Predictive Control Scheme using
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