Abstract
Nonlinear control laws are presented for stable single-input single-output processes, whether minimum-phase or non-minimum-phase. This study addresses the problem of nonlinear control of non-minimum-phase processes by exploiting the connections between model predictive control and input-output linearization. The derived control laws are continuous-time long-prediction-horizon model predictive control laws with shortest control horizon and are also approximate input-output linearizing control laws. They have one single tunable parameter with a transparent effect on the closed-loop performance, and thus they are very easy to tune. Their application and performance are illustrated and compared to those of an existing control law using numerical simulation of a non-minimum-phase chemical reactor.
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