Nonlinear control laws are presented for stable, single-input single-output processes, whether minimum phase or nonminimum phase. This study addresses the problem of nonlinear control of nonminimum-phase processes by exploiting the connections between model-predictive control and input-output linearization. Continuous-time, long-prediction-horizon, model-predictive control laws with the shortest control horizon are derived. These are approximate, input-output, linearizing control laws with one tunable parameter and, thus, are very easy to tune. Their application and performance are illustrated using numerical simulation of a chemical reactor that exhibits nonminimum-phase behavior.